A refined DenseNet Deep Learning Network for Apple Leaf Disease Prediction

Revolutionary strategies for managing agricultural diseases have been made possible by recent developments in machine learning, especially in apple leaf disease prediction. According to recent research, convolutional neural networks with fewer connections between layers near their inputs and those near the output can be trained with far greater depth, accuracy, and efficiency. To make a more precise diagnosis of apple-leaf defects than existing architectures, this work proposed a method combining DenseNet-121 and optimising transfer learning strategy for multiclass classification. DenseNet-121 is used as a feature extractor as it strengthens feature propagation and reuse, leading to sustainable feature parameter reduction. The experiment is performed on 3 publicly accessible datasets with 3 classes, 6 classes and 9 classes of apple disease in leaf. The network architecture is fed with augmented data to avoid the problem of class imbalance. The proposed model has responded exceptionally well on all three datasets, claiming 99.9%, 99% and 96% accuracy. Comparative studies and experimental data demonstrate the competitive prediction accuracy of the suggested approach. Supplementary Material File (paper_final.docx) - Download - 1.36 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 200views 74downloads Citations Download citation Shiksha Singh, Ankit Kumar Jaiswal, Karma Negi, et al. A refined DenseNet Deep Learning Network for Apple Leaf Disease Prediction. Authorea. 06 February 2025. DOI: https://doi.org/10.22541/au.173882393.31811474/v1 DOI: https://doi.org/10.22541/au.173882393.31811474/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.