Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification, the system aiming increase agricultural yield per unit area without harming natural resources and ecosystem. Modern farming employs technology improve productivity. Early accurate analysis diagnosis of plant disease very helpful in reducing diseases improving health food crop Plant experts are not available remote areas thus there a requirement automatic low-cost, approachable reliable solutions identify laboratory inspection expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) traditional machine image classification approaches being applied diseases. In this paper, CNN model proposed for rice potato leaf Rice leaves diagnosed with bacterial blight, blast, brown spot tungro Potato images classified into three classes: healthy leaves, early blight late dataset 5932 1500 used study. The was able learn hidden patterns from raw classify 99.58% accuracy 97.66% accuracy. results demonstrate that performed better when compared other learning classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree Random Forest.