PLANT DISEASE DETECTION USING DEEP LEARNING

Abstract

Crop diseases pose a significant threat to food security, but their timely detection is challenging in many regions due to a lack of necessary infrastructure. Recent advancements in leaf-based image classification have yielded promising outcomes. This study leverages Random Forest to differentiate between healthy and diseased leaves using newly created datasets. The proposed methodology includes dataset generation, feature extraction, classifier training, and image classification. Diseased and healthy leaf datasets are collectively trained using Random Forest for accurate classification. Feature extraction techniques are utilized for this purpose to enhance the accuracy of identification. Key Words: Leaf Based Classification, Disease Prediction, Feature Extraction, Random Forest, Diseased Leaves