Plant Disease Classification Using Transfer Learning with ResNet Architecture

Author(s)	Mr. MANCHALA D V V S SWAROOP, V.Anantha Lakshmi, M.Vishnu Vardhan, B.N.S.Ganga Babu
Country	India
Abstract	This paper presents a neural network-based approach for classifying plant leaf diseases using deep learning. Initially, a custom Convolutional Neural Network (CNN) was developed, followed by experiments with deeper pretrained architectures such as VGG16 and ResNet50. Among them, ResNet50 achieved the highest classification accuracy, demonstrating superior learning capability and robustness. The model was trained on a publicly available plant disease dataset containing 38 classes, enhanced through data augmentation techniques. Transfer learning and fine-tuning were employed to improve model efficiency and accuracy. The primary objective of this work is to compare deep learning architectures and identify the most effective model for real-time plant disease diagnosis. Experimental results confirm that the ResNet50 model outperforms the others in both training convergence and predictive accuracy.
Keywords	Plant Disease Detection, Deep Learning, Transfer Learning, ResNet50, VGG16, Convolutional Neural Network, Precision Agriculture
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-11
DOI	https://doi.org/10.71097/IJSAT.v16.i3.6958
Short DOI	https://doi.org/g9s9v4

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