A Hybrid Deep–Handcrafted Feature Fusion Framework for Soil Image Classification and Intelligent Crop Recommendation

A Hybrid Deep–Handcrafted Feature Fusion Framework for Soil Image Classification and Intelligent Crop Recommendation | 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 Article A Hybrid Deep–Handcrafted Feature Fusion Framework for Soil Image Classification and Intelligent Crop Recommendation R. Rajakumar, P. Umamaheswari, Ganesh Jayaraman, S. Meganthan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8195692/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 Soil is prime natural resources that affects ecosystem stability, environmental sustainability, and agricultural productivity. Precision agriculture and intelligent crop management depend heavily on accurate soil classification. Numerous studies have been proposed by various researchers to determine crop recommendations and soil classification. However, the fine-grained texture and color variations inherent in soil images make classification challenges. This study proposed a hybrid deep-handcrafted feature fusion framework that combines handcrafted descriptors like Local Binary Pattern (LBP) and Color Histogram with deep features based on Convolutional Neural Networks (CNNs). Initially, we applied a conventional classifier like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and AdaBoost. Where RF achieved the best accuracy of 84.66%. In order to enhance the accuracy applied several pretrained transfer learning models, such as VGG16, ResNet50, MobileNetV2, InceptionV3, and DenseNet121.Based on performance, ResNet50 provides better accuracy 77.28% than other transfer learning models. The proposed hybrid fusion model demonstrated the superior performance, with 99.00% accuracy, whereas the CNN baseline model we developed and achieved 94.10% accuracy. Robustness was evaluated using precision, recall, F1-Score, and AUC metrics. In addition, a Graphical User Interface (GUI) was developed for real-time soil classification and crop recommendation enabling data-driven, sustainable agricultural practices. Biological sciences/Ecology Earth and environmental sciences/Ecology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Soil Classification CNN Feature Fusion LBP Color Histogram Precision Agriculture Crop Recommendation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8195692","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":561664751,"identity":"5d88b677-e92b-46d9-af44-84dc552e2e57","order_by":0,"name":"R. 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Precision agriculture and intelligent crop management depend heavily on accurate soil classification. Numerous studies have been proposed by various researchers to determine crop recommendations and soil classification. However, the fine-grained texture and color variations inherent in soil images make classification challenges. This study proposed a hybrid deep-handcrafted feature fusion framework that combines handcrafted descriptors like Local Binary Pattern (LBP) and Color Histogram with deep features based on Convolutional Neural Networks (CNNs). Initially, we applied a conventional classifier like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and AdaBoost. Where RF achieved the best accuracy of 84.66%. In order to enhance the accuracy applied several pretrained transfer learning models, such as VGG16, ResNet50, MobileNetV2, InceptionV3, and DenseNet121.Based on performance, ResNet50 provides better accuracy 77.28% than other transfer learning models. The proposed hybrid fusion model demonstrated the superior performance, with 99.00% accuracy, whereas the CNN baseline model we developed and achieved 94.10% accuracy. Robustness was evaluated using precision, recall, F1-Score, and AUC metrics. 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