Enhancing Plant Disease Detection through Machine Learning: A Study on CNN, SVM, VGG19, and ResNet

Abstract

Purpose: The proposed study will develop an AI-assisted automated crop disease detection system for tomato, cotton, and wheat plants based on CNN, SVM, VGG19, and ResNet models, combined with an intelligent treatment recommendation system that will help farmers address diseases in a timely and effective manner. Design / Methodology / Approach: One of the proposed frameworks uses data pre-processing, augmentation, and comparative training of CNN, SVM, VGG19, and ResNet models, with hyperparameters, dropout regularisation, and a smart prescription module that provides organic, chemical, and agronomic treatment recommendations based on Kaggle public databases. Research Limitation: The system is restricted to three crop types on class-imbalanced data and has yet to be tested in practical, real-time mobile or field settings, with ResNet showing unstable classification and requiring further improvement. Findings: CNN had the highest accuracy of 92.38%, followed by VGG19 at 91.40%, SVM at 84.19%, and ResNet at 80.00%. Data augmentation increased overall generalisation by about 5, and hyperparameter optimisation is significant to the overall performance of the model. Practical Implication: The framework offers a scalable, end-to-end disease detection and treatment advisor framework which can be deployed through mobile applications and edge AI devices to facilitate real-time, offline crop diagnostics for resource-constrained farmers in agricultural settings. Social Implication: By enabling text-to-speech accessibility and early disease detection with AI, the system will assist low-literacy smallholder farmers, enhance food security, and encourage sustainable farming practices through the targeted and limited use of chemicals. Originality/Value: The study introduces a unique multi-model benchmarking infrastructure, which includes both automated disease classification and a prescriptive treatment engine and an inclusive text-to-speech interface, showing that shallow CNN networks can be more accurate than deeper ones and creating a pipeline of precision agriculture, spending more responsibly and inclusively on a course-to-course basis.