Authors : Aran Vyas; Dhruv Patel; Ishan Kalal; Babita Patel

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/2s4c2pfe

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DOI : https://doi.org/10.38124/ijisrt/25jul707

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Abstract : Plant disease significantly affects global agricultural productivity. Timely and accurate detection of leaf diseases can help farmers take corrective measures and prevent large-scale crop loss. In this study, we implement a deep learning approach using Convolutional Neural Networks (CNNs) and Transfer Learning with ResNet50 on the PlantVillage dataset to identify plant leaf diseases. A baseline CNN is first evaluated, followed by extensive experiments with ResNet50 using pre- trained ImageNet weights. The model is fine-tuned for classification of 38 plant disease categories. The performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Our approach achieved an overall test accuracy of 98% with robust generalization across various classes. Furthermore, visualizations using confusion matrices and class-wise precision support interpretability. This study confirms that transfer learning is an effective solution for plant disease classification and offers a scalable framework for agricultural diagnostics.

Keywords : Plant Disease Detection, Convolutional Neural Network (CNN), Transfer Learning, Resnet50, Deep Learning, Plantvillage Dataset, Agricultural AI, Image Classification.

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