DEPLANT-Deep Learning For Plant Disease Prediction

Authors: Aditya Kumar Gupta, Pratishtha Srivastava, Swathy R

Abstract: Plant diseases significantly reduced agricultural productivity and directly affected farmers' income, while manual inspection methods for disease detection remained laborious, subjective, and difficult to scale. This paper presented DEPLANT, a deep learning based system developed to detect plant diseases from images of leaves. Three deep learning architectures, namely Convolutional Neural Network CNN, VGG16, and Residual Network ResNet, were implemented and compared for the classification of sixteen categories of healthy and diseased plant leaves using the PlantVillage dataset. The dataset consisted of 22079 RGB images, split into 80 percent for training and 20 percent for validation. Experimental results showed that the ResNet model achieved the highest validation accuracy of 87.34 percent, followed by VGG16 with 85.47 percent, while the baseline CNN model reached 92.11 percent training accuracy but only 63.62 percent validation accuracy, indicating overfitting. These results demonstrated that transfer learning based architectures generalized considerably better than a shallow CNN trained from scratch on a limited dataset. The DEPLANT system was designed for real time deployment through a Streamlit based web application, allowing farmers and agricultural experts to upload leaf images and obtain instant predictions. The proposed framework supports smart farming and precision agriculture by enabling faster, more accurate, and more accessible plant disease diagnosis compared with traditional manual inspection methods.

DOI: http://doi.org/10.5281/zenodo.21701434