Enhancing Crop Image Classification: Comparative Analysis of Augmentation Techniques for Small Datasets

Enhancing Crop Image Classification: Comparative Analysis of Augmentation Techniques for Small Datasets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Crop Image Classification: Comparative Analysis of Augmentation Techniques for Small Datasets Satish Mandavalli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4312590/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In crop image classification, the sets of data often aren't very big, aren't balanced well, and don't exactly match what happens in real life. When models are trained using this data, they end up getting too good at the data they were trained on—and don't work so well on real data. The potency of three methods—transformative augmentation, data augmentation, and generative augmentation, in improving a small dataset of pests was estimated in this study. These methods were compared to a setup where the dataset wasn't changed at all. In the first method, random changes were made to things like brightness, rotation, and colors in the images to create more of them. In the second method, Generative Adversarial Network (GAN) is used to create fake images of pests to add to the small dataset. Three different models were then trained for each method to see how well they could identify the pests. This experiment was repeated a thousand times to ensure genuine results. It was found that at a certain level of confidence, the three methods made a big difference in how well the models could identify pests. The method using transformative augmentation worked the best. It helped make the images clearer by reducing things like background noise and differences in lighting and angles. Artificial Intelligence and Machine Learning Generative Adversarial Network Generative Augmentation Pests Transformative Augmentation Figures Figure 1 Figure 2 Figure 3 Figure 4 I. INTRODUCTION The potential of Deep Learning (DL) for pest detection, however, is hampered by limited datasets. Yet, current pest datasets fail on one or both criteria [ 1 ]–[ 5 ]. A dominant paradigm in addressing crop pests and their effects on livelihood is Integrated Pest Management (IPM). On the one hand, pest datasets are incomplete relative to standard-sized datasets. A promising avenue to automate pest detection is the use of DL techniques [ 6 ]–[ 12 ]. This application is particularly relevant in pest detection where pests may be classified based on their distinct physical traits. However, the number of samples per pest category of current datasets is remarkably low, averaging 160 samples per category. IPM seeks to combine all available pest control methods to manage pests in an economical and environmentally sustainable way. Automation should be used to augment local knowledge in pest management to support the precise and early diagnosis of pests. With climate change and sudden weather shifts favourable to pest populations—the ill effects of outbreaks on local economies are only expected to increase. An important application in this domain is image classification, whereby DL models are trained to extract relevant features of images and classify them given various classes. In the Philippines alone, invasive crop pests have cost the economy 600 million annually. Pests account for up to 4070 billion each year. In the Philippines, for example, some farmers are unable to make judicious use of pesticides due to uncertainty in identifying pests. Yet, its sustained adoption by local economies across the world remains tentative in part due to a lack of technical knowledge among farmers. To build robust classification models, it is important to obtain datasets that are complete and representative. Therefore, a viable alternative to transformative augmentation is generative augmentation in which DL is used to synthesize new image data that may be used for expanding limited datasets. An important use case for Generative Adversarial Network (GAN) is augmenting datasets for which data is expected to be scarce as in medical images or images of rare plant disease. Hence, GANs represent an opportunity for artificially diversifying limited datasets in pest recognition to improve classification models. In this regard, a relevant approach to diversifying datasets is data augmentation. Data augmentation is the process of artificially increasing the size and quality of datasets. Several studies across different crops testify to the ability of basic data augmentation to improve model accuracy relative to models with no data augmentation at all—have shown that adding GAN-generated pest images to a limited dataset improves classification accuracy by as much as 3%. GANs are a family of DL frameworks that are used to generate synthetic data by learning the distribution of a given dataset. Yet, a study on crop disease detection has shown that models trained under homogeneous images report less than 50% accuracy when tested on real-life samples, which is a concern being addressed by recent curators of pest datasets. However, an important caveat in this finding is that IPM have used homogeneous images in GAN training, which we have previously shown contributes to poor generalizability to field condition images. This concern also highlights the need for datasets that not only have a high quantity of data but are also diverse enough to cope with the wide range of conditions found in the field. On the other hand, some datasets also fail to represent the vast array of field conditions. Such manipulations are indispensable to crop pest classification where visual diagnoses may be done from different angles, with varying backgrounds, and at different times of the day. Therefore, transformative augmentation, for example, adds new images simply through image manipulations such as flipping, rotating, scaling, adjusting contrast and brightness, and color transformations. Showing how well transformative and generative augmentation work with field datasets is an important step in making automatic crop pest identification better. If these methods work well, it could mean we don't need to collect as much data manually. Instead, we could create different kinds of data artificially, making the training process for pest identification models faster and more accurate. It will help the farmers more effectively to figure out what's wrong with their crops as this is a big part of managing crop diseases, which is important for keeping food production steady over time. This knowledge can help us make existing methods better and, eventually, improve how good our data and models are for identifying crop pests. So, the goal of this study is to see how well transformative and generative data augmentation can improve models that identify tomato pests in real field conditions. Particularly, we wants to: Use transformative and generative augmentation for different kinds of tomato pests. Make models that identify tomato pests with no extra data, with transformative augmentation, and with generative augmentation. The study is as follows; the materials and methods are shown in the next section. The experimental analysis is carried out in Section III, and we wrap up the investigation with some conclusions and ideas for future work in Section IV. II. MATERIALS AND METHODS A dataset of tomato pests curated by [ 13 ] contains 8 classes of tomato pests. Unlike datasets like plant village 1 , where the images were taken in labs with controlled conditions, [ 13 ] dataset has images with more complex backgrounds and different numbers and poses of pests, making it more realistic. Only the classes in the dataset with at least 50 image samples were considered, resulting in the use of 6 out of the 8 classes for the study as shown in Fig. 1 . The dataset is somewhat limited and slightly unbalanced, which may lead to overfitting. However, after testing three different models for classifying images of pests (refer to Table I) where each model uses a different method to change the images we solve the problem of overfitting. TABLE I OVERVIEW OF EXPERIMENTAL DESIGN Components Setups Control Setup (No Augmentation) Experimental Setup 1 (Transformative Augmentation) Experimental Setup 2 (Generative Augmentation) Training Data Original Pest Images Original Images + Transformed Images Original Images + Transformed Images + Generated Images Model MobileNet + Image Classification Model A. Augmentation 1) No Augmentation: Experiment 1 To see how well the model works without any changes to the images, the original dataset was used for training without any adjustments. The dataset was divided into three parts—training, validation, and testing as shown in Table II. By only training the latter layers of the model—we preserve model learning of general objects such as leaves and insects, which is instrumental for learning the overall features of tomato leaves and pests. The image classification model employs a Convolutional Neural Network (CNN) [ 14 ]–[ 17 ], which includes a dense layer for classification purposes. Convolutional layers within the CNN are responsible for extracting high-level features from the images. Initially, these layers identify relatively simple features such as edges and shapes, while later layers identify more significant parts such as leaves and stems. Pooling layers take the convolved features of previous layers and reduce their dimensions, usually through max-pooling, to decrease the complexity of and heighten relevant features for classification. In fine-tuning, we excluded the last 5 layers of the model, appended a dense layer for classification, and froze the first 22 layers. To compensate for the lack of image data, we take advantage of transfer learning. However, the candidate class or category with the highest probability is inferred to be the subject of the image. Therefore, the MobileNet model was trained to convergence with early stopping, i.e., when the validation accuracy did not significantly improve after 3 epochs. For this experiment, therefore, we finetune a MobileNet model pre-trained on ImageNet available from the Keras API. The MobileNet architecture possesses a small number of parameters and file sizes, making it suitable for edge computing by farmers. A CNN requires a large amount of training data to be effective. This architecture entailed preprocessing the images to a size of 128 by 128 pixels and normalized pixel values from a range of -1 to 1. The activation function then transforms the weighted sum of the inputs through a non-linear function that gives corresponding probabilities to each of the candidate classes. TABLE II TRAIN, VALIDATION, AND TEST SPLIT OF ORIGINAL DATASET Classes Train Validation Test B. argentifolii 43 5 12 H. armigera 79 7 22 M. persicae 96 8 26 S. exigua 54 6 15 S. litura 70 7 19 T. urticae 53 6 15 2) Transformative Augmentation: Experiment 2 Expanding the original training set by applying random transforms on each image. In contrast to the first experiment, an augmented version of the original dataset is used for model training. PyTorch—TorchVision 2 transforms are used to sequentially apply color jitter, random vertical flip, and random horizontal flip on each image. A round of data augmentation applied to an image as shown in Fig. 2 —the set of configurations for the model is identical to that of Experiment 1. Table III shows the counts for the training, validation, and test split of each pest class. The augmented dataset is then used as training data for a separate MobileNet model. Note that the validation and test sets are not altered, which are identical to those of Experiment 1. TABLE III TRAIN, VALIDATION, AND TEST SPLIT OF DATASET WITH TRANSFORMATIVE AUGMENTATION Train Validation Test Classes Original Transformed Total B. argentifolii 43 957 1000 5 12 H. armigera 79 921 1000 7 22 M. persicae 96 904 1000 8 26 S. exigua 54 946 1000 6 15 S. litura 70 930 1000 7 19 T. urticae 53 947 1000 6 15 3) Generative Augmentation: Experiment 3 The goal of the generator is to synthesize fake images that are practically indistinguishable from real images by learning the distribution of the original dataset. Table IV shows the counts of the real pest images fed to each GAN. Given the difficulty in training and evaluating GANs, it is not guaranteed that the synthetic images would be of comparative quality to that of the real images. On the other hand, for the real input, we first randomly transform the real training images and then feed them to the GANs. GANs require both fake and real images as input [ 18 ]–[ 23 ]. For this purpose, we use GANs to synthesize new images from the original pest dataset. The adversarial relationship is more formally represented by the mini-max loss function. For optimal training, we use a subset of GANs called the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) framework to generate synthetic pest images from the original dataset. After training, we add synthetic images generated by the GANs to the base dataset until images of each class in the classification dataset reach 1000. It is based upon the WGAN which boosted the model training stability of the original GAN through an improved loss function. On the other hand, the goal of the discriminator is to correctly classify images as either real or fake, outputting a binary value to indicate either real or fake. We use a batch size of 128 for training as per [ 24 ] implementation as shown in Table V; therefore, all of the images were processed in one step per epoch. Initially, we explored transforming the images every several thousand epochs, but after significant training coming up with low-quality inputs, we halted the transformations. At times, therefore, researchers do not evaluate the model itself but simply judge the quality of the generated images. While the previous experiment relied on transformative augmentation, Experiment 3 utilizes generative augmentation, whereby DL is used to create fake tomato pest images. TABLE IV NUMBER OF REAL TRAINING IMAGES USED TO TRAIN GAN PER CLASS Pest Class Number of Transformed Images B. argentifolii 43 H. armigera 79 M. persicae 96 S. exigua 54 S. litura 70 T. urticae 53 TABLE V GENERATOR AND DISCRIMINATOR MODEL ARCHITECTURE Discriminator Generator Layer Output Shape Layer Output Shape Conv2d-1 [64, 64, 64] ConvTranspose2d-1 [1024, 4, 4] LeakyReLU-2 [64, 64, 64] BatchNorm2d-2 [1024, 4, 4] Dropout2d-3 [64, 64, 64] ReLU-3 [1024, 4, 4] Conv2d-4 [128, 32, 32] ConvTranspose2d-4 [512, 8, 8] BatchNorm2d-5 [128, 32, 32] BatchNorm2d-5 [512, 8, 8] LeakyReLU-6 [128, 32, 32] ReLU-6 [512, 8, 8] Conv2d-7 [256, 16, 16] ConvTranspose2d-7 [256, 16, 16] BatchNorm2d-8 [256, 16, 16] BatchNorm2d-8 [256, 16, 16] LeakyReLU-9 [256, 16, 16] ReLU-9 [256, 16, 16] Conv2d-10 [512, 8, 8] ConvTranspose2d-10 [128, 32, 32] BatchNorm2d-11 [512, 8, 8] BatchNorm2d-11 [128, 32, 32] LeakyReLU-12 [512, 8, 8] ReLU-12 [128, 32, 32] Conv2d-13 [1024, 4, 4] ConvTranspose2d-13 [64, 64, 64] BatchNorm2d-14 [1024, 4, 4] BatchNorm2d-14 [64, 64, 64] LeakyReLU-15 [1024, 4, 4] ReLU-15 [64, 64, 64] Dropout2d-16 [1024, 4, 4] ConvTranspose2d-16 [3, 128, 128] Conv2d-17 [ 1 , 1 , 1 ] Tanh-17 [3, 128, 128] However, we kept the training data and transformations to a minimum, hoping to reduce overall training time and complexity. WGAN training does not always lead to local convergence for common use cases and GAN evaluation remains an open research problem. However, we did not have a stopping criterion for training inherent in the GAN model, unlike the early stopping mechanism of the classification model. We rely on a combination of objective and subjective measures such as the inception score and manual visual inspection of images to judge image quality. For this, we slightly altered Marko’s GAN architecture by removing the sigmoid activation function since the discriminator/critic of WGAN-GP outputs a continuous rather than binary value to indicate the realness or fakeness of an image. Therefore, for each of the tomato pest classes, we train a separate GAN, producing 6 GAN models in total. For the initial fake input, we feed a noise vector of size 100 to each of the GANs. WGAN-GP adds a gradient penalty that regularizes the gradient norm for improved training. We just apply the transformations and do not expand the dataset, keeping the original counts. Table III shows the counts for the training, validation, and test splits (same as transformative augmentation). WGAN and WGAN-GP were originally designed for noise-to-image synthesis where the generator starts with random noise and gradually morphs it into an acceptable image across several epochs. As in Experiment 2, we do not alter or augment the original validation and test sets. Hence, the generator and discriminator are participating in a zero-sum adversarial game. B. Evaluation Metrics In particular, we use a well-known metric called the Inception Score (IS). The goal is for the IS of the synthetic images to approximate that of the real images. For reference, synthetic images generated from the CIFAR-10 dataset gained an IS of 8.09 compared to the IS of the real images of the same dataset of 11.24. A high IS indicates that salient objects were found in the images and that the images were significantly different. The IS for the generated dataset is arrived at by first predicting the class probabilities of the images using the Inception Model pre-trained on 1000 image classes. In addition to evaluating the classifiers, also assesses the quality of the generated images of the GAN to support the accuracy score obtained in the third experiment. Then, the conditional and marginal probabilities of the images are derived as a measure of their quality and diversity, respectively. The primary metric used to evaluate the skill of the three models is classification accuracy. However, given that model training and evaluation are stochastic procedures, adopt an experimental repeats approach for a more reliable estimate of accuracy. Then, obtain a grand mean of the accuracy scores per model, which is used to compare their general performance against each other. The IS measures two aspects of the image: 1) the ”objectness” of the images, or the degree to which a salient object is perceivable in each image, and 2) the variation among images. Applying this approach, repeat the training and evaluation of the models 1000 times, getting each of their accuracy scores at each repeat. III. EXPERIMENTAL ANALYSIS A. Image Classification The classification performance of the three models falls well within the 70% range, as shown in Table VI. Consistent with the hypothesis, the experimental models with augmentation performed better in terms of classification accuracy than the control model with no augmentation as shown in Table I. In particular, the setup with transformative augmentation performed 2.1% better than the control setup while the generative augmentation performed 1% better than the control setup. Furthermore, the confidence intervals of the classification accuracies of the three models reveal that these differences were statistically significant (α = 0.05) with no overlap among the intervals. Also, confirm the hypothesis that the augmented setups possess higher mean classification accuracy scores than the control setup (α = 0.033). Among the three models, the model with transformative augmentation performed the best (α = 0.033). In addition to having the highest classification accuracy, it also possessed a more compact distribution and fewer low-scoring outliers. On the other hand, both the control model and the GAN-assisted model had slightly wider spreads and more low-scoring outliers, as shown in Fig. 3 . The experimental models performed better as augmentation increased the quantity and diversity of the training set. In particular, transformative augmentation reduced overfitting as shown in its compact range and minimal deviation of outliers. The transformative approach was less about creating new training images than it was about resisting the noise of existing images by reducing biases to background, color, and idiosyncratic best features. TABLE VI MEAN CLASSIFICATION ACCURACY SCORES OF THE THREE MODELS Setup Mean Accuracy (%) Bonferroni-corrected 95% Confidence Interval Control 72.05 [71.82, 72.28] Transformative 74.11 [73.94, 74.29] Generative 73.02 [72.90, 73.25] On the other hand, the generative approach outperformed than the basic approach but did no better than the transformative one. This result may be attributed to the fact that while the GANs produced some images similar to those of the training set, the synthesized images were largely of low and variable quality. A close inspection of the model errors reveals commonalities and slight differences germane to the type of data augmentation applied, if any, as shown in Fig. 4 . An important general pattern observed is that all three models classified with great accuracy T. urticae (µ = 90%) and M. persicae (mean = 89%) which are both egg-shaped insects with distinct colors. On the other hand, the models performed poorly in classifying S. exigua (µ = 52%) and S. litura (µ = 56%) which are both wormlike insect larvae, with the models misclassifying them into either class or H. armigera which is also wormlike. Notably, these two classes performed best under the control set-up, indicating that augmentation had no beneficial effect on these classes. Meanwhile, generative augmentation contributed significantly to boosting the accuracy of H. armigera relative to the other two models while transformative augmentation also improved the accuracy B. argentifolii the most. In general, all three models had the same best-performing and worst-performing classes due to factors inherent in pest morphology. For example, T. urticae and M. persicae were best-performing partly because they had more salient physical parts like legs and appendages and distinct morphological traits like shape and colors that allowed them to be more easily classified. Meanwhile, S. litura and S. exigua, both coming from the same genus, had similar wormlike characteristics with slight differences in texture, which potentially made it harder for them to be classified. This result indicates that classes close in distribution must have more training examples so that the variation among similar classes would exceed the variation within them in model training. Notably, data augmentation had no effect in improving the classification of these two species. As these classes were similar and had few images, this finding confirms that data augmentation can only reduce noise but not compensate for the lack of diverse and high-quantity data, which were all the more needed since the classes were close in distribution. Also, generative augmentation improved the classification of H. armigera. Yet, the pixelated quality of its fake generated images suggests that the classifier may have learned only vague and general features of the set of images for this class, indicating overfitting. Meanwhile, the positive impact of transformative augmentation on the classification of B. argentifolii is more credible. As the transforms preserved image quality, the transformative model surely learned the anatomy of the whitefly more effectively compared to the other models. Given also that the whitefly has distinct physical parts and white color, this pest became easily distinguishable from the background and other pests, lending itself well to transformative augmentation. B. Image Synthesis The images made were not very good, and they varied a lot in quality, just like other studies that made animal images. When closely looked at, some, like S. exigua and T. urticae, looked like they could be from the same group of images used to train the model. But others, like M. persicae and H. armigera, looked blurry. The IS help measure how good the fake images are compared to the real ones. The IS for each group, with 1000 images for each one are shown in Table VII. There are a few reasons why the images made by our WGAN weren't very good. First, when GANs are trained using small datasets that have a lot of variety, often end up with bad images. This was seen in the experiments because the dataset had images with different backgrounds, sizes, and subjects, which led to images with low-to-okay IS. Second, when there is a small dataset, GANs sometimes just copy the original images without adding anything new, which means the training set doesn't get any better. This can be seen in some of the better images made compared to the real ones. Third, it's harder to make fake animal images compared to other things because animals have lots of little details that are hard to model. TABLE VII INCEPTION SCORES (IS) OF THE REAL VS. GENERATED IMAGES Classes Real w/ Transformative Augmentation Fake T. urticae 3.7 2.21 S. litura 4 2 S. exigua 4.6 2.6 M. persicae 5.1 1.9 B. argentifolii 5.4 1.7 H. armigera 5.9 2.3 This is why some of the fake images, like S. exigua and S. litura, had higher IS and looked more like the real ones. But H. armigera didn't follow this pattern, even though it's also an animal with a simple shape. This is because the real images of H. armigera had lots of detail, which made it harder for the GAN to copy them well, even though the bug itself isn't that complicated. If simpler bugs are easier to make with GANs, then more complicated bugs are harder. For example, adult bugs like B. argentifolii and M. persicae have lots of little parts that are hard to copy, so the fake images didn't look very good. Also, sometimes M. persicae is green and see-through, so it blends in with leaves, which makes it hard to see in the fake images. T. urticae was different though, even though it's a small spider bug. It got the highest IS even though its real images weren't that complex, which made it easier for the GAN to copy them. Because bugs with higher fake IS were often misidentified by our CNN, it seems that the study supports this idea, but more research is needed to be sure. Right now, it's not sure if GANs can help much with small datasets of complicated bugs. Therefore, more experiments with opposed settings for the GAN are needed to see what works best. Overall, the WGAN didn't do very well with this bug classification problem, which shows that better GANs that can learn from just a few images with lots of different things in them are needed. Also, since not having enough images in the dataset leads to bad fake images—using transformative augmentation might help make more and different images for the GAN to learn from. In the study, using a regular GAN with default settings and a small dataset made fake images that weren't very good and didn't help the model much. IV. CONCLUSION AND FUTURE WORKS The transformative augmentation of GAN training images should also be explored to help widen the diversity of images synthesized for the pest classifier. Our generative approach also contributed a small improvement in accuracy as it helped create some images similar to that of the training set. Data augmentation produced significant differences in the accuracy scores of pest classification models. As such, future research should also study the feasibility and tenability of combining crowdsourced images with the original pest images and their augmentations. Crowdsourced images from the user feedback mechanism, in addition to augmented images, may help in expanding existing pest datasets. However, the pixelated quality of the images contributed to greater noise, causing low-scoring outliers in the classification accuracy scores similar to that of the control set-up. The model was embedded in a mobile app that enables users to subject an image to pest diagnosis, with built-in user feedback. The classification model trained under the dataset with transformative augmentation, currently having the highest mean accuracy score, was chosen for mobile deployment. The performance of our generative model indicates that a more rigorous hyperparameter search for WGAN is needed to establish its effectiveness in working with small, complex pest image datasets. Also, advanced GAN architectures that could account for complexities in background and specimen morphology should likewise be researched and developed for easier pest image synthesis training. 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S. Kashyap, and P. Saxena, “MLOps: A Review,” Aug. 2023, Accessed: Sep. 16, 2023. [Online]. Available: https://arxiv.org/abs/2308.10908v1 S. Wazir, G. S. Kashyap, K. Malik, and A. E. I. Brownlee, “Predicting the Infection Level of COVID-19 Virus Using Normal Distribution-Based Approximation Model and PSO,” Springer, Cham, 2023, pp. 75–91. doi: 10.1007/978-3-031-33183-1_5. G. S. Kashyap, K. Malik, S. Wazir, and R. Khan, “Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing,” Multimedia Tools and Applications , vol. 81, no. 25, pp. 36685–36698, Oct. 2022, doi: 10.1007/s11042-021-11558-9. I. J. Goodfellow et al. , “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014, vol. 3, no. January, pp. 2672–2680. doi: 10.1007/978-3-658-40442-0_9. Footnotes https://plantvillage.psu.edu/ https://pytorch.org/vision/ Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4312590","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294593871,"identity":"12b28ec1-033d-48fb-ae35-8712f1814058","order_by":0,"name":"Satish Mandavalli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYPACZgZ+BsYGMJO9gQjVYEKyAaqF5xixWgwOQPkEtci3nz+64ecOa3nja4cbP1dU1MnxyDcwfviYg1uLwZlktpu9Z9INt91ObJY8c+awMQ8bA7PkzG14tDAks93gbTvMCNTSxtjYdiBxPxsDGzMvHi3y/Y/Zbv5tO2y/eTZYS11iDyEtDDeS2W4DbUncIA3WwkxYi8GNx2a3ZdvSk2eA/NIA9guQgc8v8v2Jz26+bbO27Z+d/vBjAyjEmA8f/PARn8OwAGicjoJRMApGwSggHwAAAqZR426564oAAAAASUVORK5CYII=","orcid":"","institution":"Microsoft","correspondingAuthor":true,"prefix":"","firstName":"Satish","middleName":"","lastName":"Mandavalli","suffix":""}],"badges":[],"createdAt":"2024-04-23 14:15:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4312590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4312590/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55340872,"identity":"38d977d1-fd4d-4587-aa0c-d723dffb4e42","added_by":"auto","created_at":"2024-04-26 02:07:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40741,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of images in the tomato pest dataset across different types\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4312590/v1/db8e783d116fcb5c31a16656.png"},{"id":55340870,"identity":"e919642b-fe05-455d-a267-efd0b862c420","added_by":"auto","created_at":"2024-04-26 02:07:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96816,"visible":true,"origin":"","legend":"\u003cp\u003eVarious transformations applied on a pest image\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4312590/v1/f92f78e2d4f12ece54848313.png"},{"id":55340874,"identity":"ed381d4c-5bcf-492a-b529-013021f0cc39","added_by":"auto","created_at":"2024-04-26 02:07:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14335,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whiskers plot illustrating the average setup accuracy\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4312590/v1/089a28bfa39b55b51d55e4e5.png"},{"id":55340869,"identity":"92f842aa-3db8-4721-a644-5b83d6775c38","added_by":"auto","created_at":"2024-04-26 02:07:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44300,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion Matrices of the Three Models\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4312590/v1/224aaf84a8d51868fa5bdf70.png"},{"id":55341518,"identity":"505881dc-3f5d-45a0-aa2a-08a7778d8e24","added_by":"auto","created_at":"2024-04-26 02:15:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":495474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4312590/v1/a5eef93d-bf64-4d02-8463-b6458a8fe385.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhancing Crop Image Classification: Comparative Analysis of Augmentation Techniques for Small Datasets\u003c/p\u003e","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eThe potential of Deep Learning (DL) for pest detection, however, is hampered by limited datasets. Yet, current pest datasets fail on one or both criteria [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A dominant paradigm in addressing crop pests and their effects on livelihood is Integrated Pest Management (IPM). On the one hand, pest datasets are incomplete relative to standard-sized datasets. A promising avenue to automate pest detection is the use of DL techniques [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This application is particularly relevant in pest detection where pests may be classified based on their distinct physical traits. However, the number of samples per pest category of current datasets is remarkably low, averaging 160 samples per category. IPM seeks to combine all available pest control methods to manage pests in an economical and environmentally sustainable way. Automation should be used to augment local knowledge in pest management to support the precise and early diagnosis of pests. With climate change and sudden weather shifts favourable to pest populations\u0026mdash;the ill effects of outbreaks on local economies are only expected to increase. An important application in this domain is image classification, whereby DL models are trained to extract relevant features of images and classify them given various classes. In the Philippines alone, invasive crop pests have cost the economy 600\u0026nbsp;million annually. Pests account for up to 4070\u0026nbsp;billion each year. In the Philippines, for example, some farmers are unable to make judicious use of pesticides due to uncertainty in identifying pests. Yet, its sustained adoption by local economies across the world remains tentative in part due to a lack of technical knowledge among farmers.\u003c/p\u003e \u003cp\u003eTo build robust classification models, it is important to obtain datasets that are complete and representative. Therefore, a viable alternative to transformative augmentation is generative augmentation in which DL is used to synthesize new image data that may be used for expanding limited datasets. An important use case for Generative Adversarial Network (GAN) is augmenting datasets for which data is expected to be scarce as in medical images or images of rare plant disease. Hence, GANs represent an opportunity for artificially diversifying limited datasets in pest recognition to improve classification models. In this regard, a relevant approach to diversifying datasets is data augmentation. Data augmentation is the process of artificially increasing the size and quality of datasets. Several studies across different crops testify to the ability of basic data augmentation to improve model accuracy relative to models with no data augmentation at all\u0026mdash;have shown that adding GAN-generated pest images to a limited dataset improves classification accuracy by as much as 3%. GANs are a family of DL frameworks that are used to generate synthetic data by learning the distribution of a given dataset. Yet, a study on crop disease detection has shown that models trained under homogeneous images report less than 50% accuracy when tested on real-life samples, which is a concern being addressed by recent curators of pest datasets. However, an important caveat in this finding is that IPM have used homogeneous images in GAN training, which we have previously shown contributes to poor generalizability to field condition images. This concern also highlights the need for datasets that not only have a high quantity of data but are also diverse enough to cope with the wide range of conditions found in the field. On the other hand, some datasets also fail to represent the vast array of field conditions. Such manipulations are indispensable to crop pest classification where visual diagnoses may be done from different angles, with varying backgrounds, and at different times of the day. Therefore, transformative augmentation, for example, adds new images simply through image manipulations such as flipping, rotating, scaling, adjusting contrast and brightness, and color transformations.\u003c/p\u003e \u003cp\u003eShowing how well transformative and generative augmentation work with field datasets is an important step in making automatic crop pest identification better. If these methods work well, it could mean we don't need to collect as much data manually. Instead, we could create different kinds of data artificially, making the training process for pest identification models faster and more accurate. It will help the farmers more effectively to figure out what's wrong with their crops as this is a big part of managing crop diseases, which is important for keeping food production steady over time. This knowledge can help us make existing methods better and, eventually, improve how good our data and models are for identifying crop pests. So, the goal of this study is to see how well transformative and generative data augmentation can improve models that identify tomato pests in real field conditions. Particularly, we wants to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUse transformative and generative augmentation for different kinds of tomato pests.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMake models that identify tomato pests with no extra data, with transformative augmentation, and with generative augmentation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe study is as follows; the materials and methods are shown in the next section. The experimental analysis is carried out in Section III, and we wrap up the investigation with some conclusions and ideas for future work in Section IV.\u003c/p\u003e"},{"header":"II.\tMATERIALS AND METHODS","content":"\u003cp\u003eA dataset of tomato pests curated by [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] contains 8 classes of tomato pests. Unlike datasets like plant village\u003csup\u003e1\u003c/sup\u003e, where the images were taken in labs with controlled conditions, [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] dataset has images with more complex backgrounds and different numbers and poses of pests, making it more realistic. Only the classes in the dataset with at least 50 image samples were considered, resulting in the use of 6 out of the 8 classes for the study as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The dataset is somewhat limited and slightly unbalanced, which may lead to overfitting. However, after testing three different models for classifying images of pests (refer to Table I) where each model uses a different method to change the images we solve the problem of overfitting.\u003c/p\u003e\n\u003cp\u003eTABLE I\u0026nbsp;OVERVIEW OF EXPERIMENTAL DESIGN\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComponents Setups\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl Setup (No Augmentation)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExperimental Setup 1 (Transformative Augmentation)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExperimental Setup 2 (Generative Augmentation)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Data\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOriginal Pest Images\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOriginal Images\u0026thinsp;+\u0026thinsp;Transformed Images\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOriginal Images\u0026thinsp;+\u0026thinsp;Transformed Images\u0026thinsp;+\u0026thinsp;Generated Images\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMobileNet\u0026thinsp;+\u0026thinsp;Image Classification Model\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eA. Augmentation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1) No Augmentation: Experiment 1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo see how well the model works without any changes to the images, the original dataset was used for training without any adjustments. The dataset was divided into three parts\u0026mdash;training, validation, and testing as shown in Table II. By only training the latter layers of the model\u0026mdash;we preserve model learning of general objects such as leaves and insects, which is instrumental for learning the overall features of tomato leaves and pests. The image classification model employs a Convolutional Neural Network (CNN) [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u0026ndash;[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], which includes a dense layer for classification purposes. Convolutional layers within the CNN are responsible for extracting high-level features from the images. Initially, these layers identify relatively simple features such as edges and shapes, while later layers identify more significant parts such as leaves and stems. Pooling layers take the convolved features of previous layers and reduce their dimensions, usually through max-pooling, to decrease the complexity of and heighten relevant features for classification. In fine-tuning, we excluded the last 5 layers of the model, appended a dense layer for classification, and froze the first 22 layers. To compensate for the lack of image data, we take advantage of transfer learning. However, the candidate class or category with the highest probability is inferred to be the subject of the image. Therefore, the MobileNet model was trained to convergence with early stopping, i.e., when the validation accuracy did not significantly improve after 3 epochs. For this experiment, therefore, we finetune a MobileNet model pre-trained on ImageNet available from the Keras API. The MobileNet architecture possesses a small number of parameters and file sizes, making it suitable for edge computing by farmers. A CNN requires a large amount of training data to be effective. This architecture entailed preprocessing the images to a size of 128 by 128 pixels and normalized pixel values from a range of -1 to 1. The activation function then transforms the weighted sum of the inputs through a non-linear function that gives corresponding probabilities to each of the candidate classes.\u003c/p\u003e\n\u003cp\u003eTABLE II\u0026nbsp;TRAIN, VALIDATION, AND TEST SPLIT OF ORIGINAL DATASET\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClasses\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTrain\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValidation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB. argentifolii\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH. armigera\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM. persicae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. exigua\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. litura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT. urticae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e2) Transformative Augmentation: Experiment 2\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExpanding the original training set by applying random transforms on each image. In contrast to the first experiment, an augmented version of the original dataset is used for model training. PyTorch\u0026mdash;TorchVision\u003csup\u003e2\u003c/sup\u003e\u003ca id=\"#FNLinkFn2\" class=\"FNLink\" href=\"#Fn2\"\u003e\u003c/a\u003e transforms are used to sequentially apply color jitter, random vertical flip, and random horizontal flip on each image. A round of data augmentation applied to an image as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026mdash;the set of configurations for the model is identical to that of Experiment 1. Table III shows the counts for the training, validation, and test split of each pest class. The augmented dataset is then used as training data for a separate MobileNet model. Note that the validation and test sets are not altered, which are identical to those of Experiment 1.\u003c/p\u003e\n\u003cp\u003eTABLE III\u0026nbsp;TRAIN, VALIDATION, AND TEST SPLIT OF DATASET WITH TRANSFORMATIVE AUGMENTATION\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"54%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTrain\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"11%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e\u003cstrong\u003eClasses\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOriginal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTransformed\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eB. argentifolii\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e957\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eH. armigera\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eM. persicae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eS. exigua\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e946\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eS. litura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eT. urticae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003e947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e1000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e3) Generative Augmentation: Experiment 3\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of the generator is to synthesize fake images that are practically indistinguishable from real images by learning the distribution of the original dataset. Table IV shows the counts of the real pest images fed to each GAN. Given the difficulty in training and evaluating GANs, it is not guaranteed that the synthetic images would be of comparative quality to that of the real images. On the other hand, for the real input, we first randomly transform the real training images and then feed them to the GANs. GANs require both fake and real images as input [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u0026ndash;[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. For this purpose, we use GANs to synthesize new images from the original pest dataset. The adversarial relationship is more formally represented by the mini-max loss function. For optimal training, we use a subset of GANs called the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) framework to generate synthetic pest images from the original dataset. After training, we add synthetic images generated by the GANs to the base dataset until images of each class in the classification dataset reach 1000. It is based upon the WGAN which boosted the model training stability of the original GAN through an improved loss function. On the other hand, the goal of the discriminator is to correctly classify images as either real or fake, outputting a binary value to indicate either real or fake. We use a batch size of 128 for training as per [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] implementation as shown in Table V; therefore, all of the images were processed in one step per epoch. Initially, we explored transforming the images every several thousand epochs, but after significant training coming up with low-quality inputs, we halted the transformations. At times, therefore, researchers do not evaluate the model itself but simply judge the quality of the generated images. While the previous experiment relied on transformative augmentation, Experiment 3 utilizes generative augmentation, whereby DL is used to create fake tomato pest images.\u003c/p\u003e\n\u003cp\u003eTABLE IV\u0026nbsp;NUMBER OF REAL TRAINING IMAGES USED TO TRAIN GAN PER CLASS\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabd\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePest Class\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of Transformed Images\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB. argentifolii\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH. armigera\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM. persicae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. exigua\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. litura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT. urticae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTABLE V\u0026nbsp;GENERATOR AND DISCRIMINATOR MODEL ARCHITECTURE\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDiscriminator\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eGenerator\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLayer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOutput Shape\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLayer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutput Shape\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeakyReLU-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDropout2d-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReLU-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeakyReLU-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReLU-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeakyReLU-9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReLU-9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[256, 16, 16]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeakyReLU-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[512, 8, 8]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReLU-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[128, 32, 32]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBatchNorm2d-14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeakyReLU-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReLU-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[64, 64, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDropout2d-16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[1024, 4, 4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConvTranspose2d-16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[3, 128, 128]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConv2d-17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTanh-17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[3, 128, 128]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHowever, we kept the training data and transformations to a minimum, hoping to reduce overall training time and complexity. WGAN training does not always lead to local convergence for common use cases and GAN evaluation remains an open research problem. However, we did not have a stopping criterion for training inherent in the GAN model, unlike the early stopping mechanism of the classification model. We rely on a combination of objective and subjective measures such as the inception score and manual visual inspection of images to judge image quality. For this, we slightly altered Marko\u0026rsquo;s GAN architecture by removing the sigmoid activation function since the discriminator/critic of WGAN-GP outputs a continuous rather than binary value to indicate the realness or fakeness of an image. Therefore, for each of the tomato pest classes, we train a separate GAN, producing 6 GAN models in total. For the initial fake input, we feed a noise vector of size 100 to each of the GANs. WGAN-GP adds a gradient penalty that regularizes the gradient norm for improved training. We just apply the transformations and do not expand the dataset, keeping the original counts. Table III shows the counts for the training, validation, and test splits (same as transformative augmentation). WGAN and WGAN-GP were originally designed for noise-to-image synthesis where the generator starts with random noise and gradually morphs it into an acceptable image across several epochs. As in Experiment 2, we do not alter or augment the original validation and test sets. Hence, the generator and discriminator are participating in a zero-sum adversarial game.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. Evaluation Metrics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn particular, we use a well-known metric called the Inception Score (IS). The goal is for the IS of the synthetic images to approximate that of the real images. For reference, synthetic images generated from the CIFAR-10 dataset gained an IS of 8.09 compared to the IS of the real images of the same dataset of 11.24. A high IS indicates that salient objects were found in the images and that the images were significantly different. The IS for the generated dataset is arrived at by first predicting the class probabilities of the images using the Inception Model pre-trained on 1000 image classes. In addition to evaluating the classifiers, also assesses the quality of the generated images of the GAN to support the accuracy score obtained in the third experiment. Then, the conditional and marginal probabilities of the images are derived as a measure of their quality and diversity, respectively. The primary metric used to evaluate the skill of the three models is classification accuracy. However, given that model training and evaluation are stochastic procedures, adopt an experimental repeats approach for a more reliable estimate of accuracy. Then, obtain a grand mean of the accuracy scores per model, which is used to compare their general performance against each other. The IS measures two aspects of the image: 1) the \u0026rdquo;objectness\u0026rdquo; of the images, or the degree to which a salient object is perceivable in each image, and 2) the variation among images. Applying this approach, repeat the training and evaluation of the models 1000 times, getting each of their accuracy scores at each repeat.\u003c/p\u003e"},{"header":"III. EXPERIMENTAL ANALYSIS","content":"\u003cp\u003eA. \u003cem\u003eImage Classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe classification performance of the three models falls well within the 70% range, as shown in Table VI. Consistent with the hypothesis, the experimental models with augmentation performed better in terms of classification accuracy than the control model with no augmentation as shown in Table I. In particular, the setup with transformative augmentation performed 2.1% better than the control setup while the generative augmentation performed 1% better than the control setup. Furthermore, the confidence intervals of the classification accuracies of the three models reveal that these differences were statistically significant (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05) with no overlap among the intervals. Also, confirm the hypothesis that the augmented setups possess higher mean classification accuracy scores than the control setup (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.033). Among the three models, the model with transformative augmentation performed the best (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.033). In addition to having the highest classification accuracy, it also possessed a more compact distribution and fewer low-scoring outliers. On the other hand, both the control model and the GAN-assisted model had slightly wider spreads and more low-scoring outliers, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The experimental models performed better as augmentation increased the quantity and diversity of the training set. In particular, transformative augmentation reduced overfitting as shown in its compact range and minimal deviation of outliers. The transformative approach was less about creating new training images than it was about resisting the noise of existing images by reducing biases to background, color, and idiosyncratic best features.\u003c/p\u003e\n\u003cp\u003eTABLE VI MEAN CLASSIFICATION ACCURACY SCORES OF THE THREE MODELS\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabf\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSetup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Accuracy (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBonferroni-corrected 95% Confidence Interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[71.82, 72.28]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTransformative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[73.94, 74.29]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenerative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[72.90, 73.25]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOn the other hand, the generative approach outperformed than the basic approach but did no better than the transformative one. This result may be attributed to the fact that while the GANs produced some images similar to those of the training set, the synthesized images were largely of low and variable quality. A close inspection of the model errors reveals commonalities and slight differences germane to the type of data augmentation applied, if any, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. An important general pattern observed is that all three models classified with great accuracy T. urticae (\u0026micro;\u0026thinsp;=\u0026thinsp;90%) and M. persicae (mean\u0026thinsp;=\u0026thinsp;89%) which are both egg-shaped insects with distinct colors. On the other hand, the models performed poorly in classifying S. exigua (\u0026micro;\u0026thinsp;=\u0026thinsp;52%) and S. litura (\u0026micro;\u0026thinsp;=\u0026thinsp;56%) which are both wormlike insect larvae, with the models misclassifying them into either class or H. armigera which is also wormlike. Notably, these two classes performed best under the control set-up, indicating that augmentation had no beneficial effect on these classes. Meanwhile, generative augmentation contributed significantly to boosting the accuracy of H. armigera relative to the other two models while transformative augmentation also improved the accuracy B. argentifolii the most. In general, all three models had the same best-performing and worst-performing classes due to factors inherent in pest morphology. For example, T. urticae and M. persicae were best-performing partly because they had more salient physical parts like legs and appendages and distinct morphological traits like shape and colors that allowed them to be more easily classified. Meanwhile, S. litura and S. exigua, both coming from the same genus, had similar wormlike characteristics with slight differences in texture, which potentially made it harder for them to be classified. This result indicates that classes close in distribution must have more training examples so that the variation among similar classes would exceed the variation within them in model training. Notably, data augmentation had no effect in improving the classification of these two species. As these classes were similar and had few images, this finding confirms that data augmentation can only reduce noise but not compensate for the lack of diverse and high-quantity data, which were all the more needed since the classes were close in distribution. Also, generative augmentation improved the classification of H. armigera. Yet, the pixelated quality of its fake generated images suggests that the classifier may have learned only vague and general features of the set of images for this class, indicating overfitting. Meanwhile, the positive impact of transformative augmentation on the classification of B. argentifolii is more credible. As the transforms preserved image quality, the transformative model surely learned the anatomy of the whitefly more effectively compared to the other models. Given also that the whitefly has distinct physical parts and white color, this pest became easily distinguishable from the background and other pests, lending itself well to transformative augmentation.\u003c/p\u003e\n\u003cp\u003eB. \u003cem\u003eImage Synthesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe images made were not very good, and they varied a lot in quality, just like other studies that made animal images. When closely looked at, some, like S. exigua and T. urticae, looked like they could be from the same group of images used to train the model. But others, like M. persicae and H. armigera, looked blurry. The IS help measure how good the fake images are compared to the real ones. The IS for each group, with 1000 images for each one are shown in Table VII. There are a few reasons why the images made by our WGAN weren't very good. First, when GANs are trained using small datasets that have a lot of variety, often end up with bad images. This was seen in the experiments because the dataset had images with different backgrounds, sizes, and subjects, which led to images with low-to-okay IS. Second, when there is a small dataset, GANs sometimes just copy the original images without adding anything new, which means the training set doesn't get any better. This can be seen in some of the better images made compared to the real ones. Third, it's harder to make fake animal images compared to other things because animals have lots of little details that are hard to model.\u003c/p\u003e\n\u003cp\u003eTABLE VII\u0026nbsp;INCEPTION SCORES (IS) OF THE REAL VS. GENERATED IMAGES\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabg\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClasses\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReal w/ Transformative Augmentation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFake\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT. urticae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. litura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS. exigua\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM. persicae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB. argentifolii\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH. armigera\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis is why some of the fake images, like S. exigua and S. litura, had higher IS and looked more like the real ones. But H. armigera didn't follow this pattern, even though it's also an animal with a simple shape. This is because the real images of H. armigera had lots of detail, which made it harder for the GAN to copy them well, even though the bug itself isn't that complicated. If simpler bugs are easier to make with GANs, then more complicated bugs are harder. For example, adult bugs like B. argentifolii and M. persicae have lots of little parts that are hard to copy, so the fake images didn't look very good. Also, sometimes M. persicae is green and see-through, so it blends in with leaves, which makes it hard to see in the fake images. T. urticae was different though, even though it's a small spider bug. It got the highest IS even though its real images weren't that complex, which made it easier for the GAN to copy them. Because bugs with higher fake IS were often misidentified by our CNN, it seems that the study supports this idea, but more research is needed to be sure. Right now, it's not sure if GANs can help much with small datasets of complicated bugs. Therefore, more experiments with opposed settings for the GAN are needed to see what works best. Overall, the WGAN didn't do very well with this bug classification problem, which shows that better GANs that can learn from just a few images with lots of different things in them are needed. Also, since not having enough images in the dataset leads to bad fake images\u0026mdash;using transformative augmentation might help make more and different images for the GAN to learn from. In the study, using a regular GAN with default settings and a small dataset made fake images that weren't very good and didn't help the model much.\u003c/p\u003e"},{"header":"IV. CONCLUSION AND FUTURE WORKS ","content":"\u003cp\u003eThe transformative augmentation of GAN training images should also be explored to help widen the diversity of images synthesized for the pest classifier. Our generative approach also contributed a small improvement in accuracy as it helped create some images similar to that of the training set. Data augmentation produced significant differences in the accuracy scores of pest classification models. As such, future research should also study the feasibility and tenability of combining crowdsourced images with the original pest images and their augmentations. Crowdsourced images from the user feedback mechanism, in addition to augmented images, may help in expanding existing pest datasets. However, the pixelated quality of the images contributed to greater noise, causing low-scoring outliers in the classification accuracy scores similar to that of the control set-up. The model was embedded in a mobile app that enables users to subject an image to pest diagnosis, with built-in user feedback. The classification model trained under the dataset with transformative augmentation, currently having the highest mean accuracy score, was chosen for mobile deployment. The performance of our generative model indicates that a more rigorous hyperparameter search for WGAN is needed to establish its effectiveness in working with small, complex pest image datasets. Also, advanced GAN architectures that could account for complexities in background and specimen morphology should likewise be researched and developed for easier pest image synthesis training. Our transformative approach prevented overfitting to the training set by reducing biases on background, angle, lighting, and other general image features. This technique created a regularizing effect that helped make the classification accuracy more consistent across experimental repeats.\u003c/p\u003e"},{"header":"V.\tDECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eA. Funding:\u0026nbsp;\u003c/strong\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Conflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Data Availability:\u0026nbsp;\u003c/strong\u003eData will be made on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Code Availability:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eCode will be made on reasonable request.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eV. K. Kanaparthi, \u0026ldquo;Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis,\u0026rdquo; Dec. 2023, doi: 10.20944/PREPRINTS202312.0427.V1.\u003c/li\u003e\n\u003cli\u003eV. 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Goodfellow \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Generative adversarial nets,\u0026rdquo; in \u003cem\u003eAdvances in Neural Information Processing Systems\u003c/em\u003e, 2014, vol. 3, no. January, pp. 2672\u0026ndash;2680. doi: 10.1007/978-3-658-40442-0_9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plantvillage.psu.edu/\u003c/span\u003e\u003cspan address=\"https://plantvillage.psu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pytorch.org/vision/\u003c/span\u003e\u003cspan address=\"https://pytorch.org/vision/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative Adversarial Network, Generative Augmentation, Pests, Transformative Augmentation","lastPublishedDoi":"10.21203/rs.3.rs-4312590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4312590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn crop image classification, the sets of data often aren't very big, aren't balanced well, and don't exactly match what happens in real life. When models are trained using this data, they end up getting too good at the data they were trained on\u0026mdash;and don't work so well on real data. The potency of three methods\u0026mdash;transformative augmentation, data augmentation, and generative augmentation, in improving a small dataset of pests was estimated in this study. These methods were compared to a setup where the dataset wasn't changed at all. In the first method, random changes were made to things like brightness, rotation, and colors in the images to create more of them. In the second method, Generative Adversarial Network (GAN) is used to create fake images of pests to add to the small dataset. Three different models were then trained for each method to see how well they could identify the pests. This experiment was repeated a thousand times to ensure genuine results. It was found that at a certain level of confidence, the three methods made a big difference in how well the models could identify pests. The method using transformative augmentation worked the best. It helped make the images clearer by reducing things like background noise and differences in lighting and angles.\u003c/p\u003e","manuscriptTitle":"Enhancing Crop Image Classification: Comparative Analysis of Augmentation Techniques for Small Datasets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 02:07:36","doi":"10.21203/rs.3.rs-4312590/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34833d8c-bfb2-4cd0-8cdd-895026e9a645","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31056107,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-04-26T02:07:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 02:07:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4312590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4312590","identity":"rs-4312590","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}