Analysis of Deep Learning Architectures for Smart Plant Disease Detection and Organic Remedy Recommendation

Authors: Pradeep, Usha Dhankar, Aditya Kumar

Abstract: Plant diseases pose a major threat to global agricultural productivity, food security, and sustainability. Traditional disease diagnosis methods are often time-consuming, subjective, and require expert intervention. Recent advancements in artificial intelligence, particularly deep learning, have revolutionized agricultural disease detection through automated image-based systems. This study presents a comprehensive analysis of deep learning architectures such as Convolutional Neural Networks (CNNs), Transfer Learning Models (ResNet, VGG, Inception), and Vision Transformers (ViTs) for smart plant disease detection and integrates an organic remedy recommendation framework. The research employs publicly available plant disease datasets to train and compare model performances in terms of accuracy, precision, and recall. Furthermore, an ontology-based organic treatment recommender system is developed to suggest sustainable, eco-friendly remedies. The experimental results demonstrate that deep learning models achieve over 97% detection accuracy, while the proposed organic recommendation layer enhances decision-making for sustainable agriculture.

DOI: http://doi.org/10.5281/zenodo.21702860