Plant Disease Detection Using Machine Learning

Plant diseases pose a significant threat to global agriculture, leading to substantial economic losses and reduced crop yields.Timely and accurate diagnosis is critical to ensuring effective crop management and sustainability.In this study, we propose a deep learning-based plant disease recognition system leveraging the EfficientNetB4 convolutional neural network architecture.The system is trained and fine-tuned using the Plant Village dataset, achieving high accuracy in classifying 39 distinct classes of diseased and healthy plant leaves.To make the solution accessible to end-users, we deploy the model through a web application built using Flask, integrated with HTML, CSS, and JavaScript.The web interface allows users to upload leaf images and instantly receive disease classification results along with treatment suggestions.This comprehensive approach combines high-performance deep learning with user-centric design, offering a scalable and efficient tool for real-time plant disease diagnosis.EfficientNetB4 is a type of Convolutional Neural Network (CNN) architecture developed by Google AI.It is a part of the EfficientNet family, which is known for achieving high accuracy while being computationally efficient (i.e., less memory, faster).