Authors:
K. Prasath, Balika J. Chelliah

Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

Abstract:

Agricultural productivity faces significant challenges due to plant diseases caused by microscopic pathogens that are difficult to detect during early developmental stages. Traditional disease detection methods rely heavily on manual inspection by agricultural experts, which is time-consuming, expensive, and prone to human error. This research presents an automated plant disease detection system that leverages deep learning and transfer learning to identify and classify plant diseases with high accuracy. The proposed framework utilises the comprehensive Plant Village dataset, which contains 54,309 images spanning 14 crop species and 38 disease classes. Multiple convolutional neural network architectures, including AlexNet, VGG16, InceptionV3, and MobileNet, were implemented and evaluated using colour, grayscale, and segmented image representations. The models were trained using an 80-20 split between training and test data to ensure robust performance evaluation. Performance metrics, including accuracy, precision, recall, and F1-score, were computed to assess model effectiveness. Among all architectures, AlexNet achieved the highest performance, with 99.56% accuracy, 0.9953 precision, 0.9971 recall, and 0.9961 F1-score. The system is deployed as a web application using HTML, CSS, JavaScript, Keras, and Python, hosted on Heroku. This automated solution enables farmers to detect plant diseases at early stages with minimal cost, facilitating timely intervention and improved crop yield management.

Keywords: Plant Disease Detection; Deep Learning; Transfer Learning; Convolutional Neural Networks; Precision Agriculture; Image Classification; Agricultural Productivity; Crop Yield Management.

Received on: 28/01/2025, Revised on: 04/04/2025, Accepted on: 11/06/2025, Published on: 06/12/2025

DOI: 10.69888/FTSHSL.2025.000512

FMDB Transactions on Sustainable Health Science Letters, 2025 Vol. 3 No. 4, Pages: 231-245

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