Study Of Plant Disease Detection Using Machine Learning And Deep Neural Network

Abstract

Crop diseases pose a significant risk to food security, yet their rapid identification remains challenging in many parts of the world due to the lack of necessary infrastructure. The emergence of accurate techniques in the field of leaf-based image classification has shown promising results. This paper utilizes Random Forest to distinguish between healthy and diseased leaves from datasets created for this purpose. Our proposed methodology includes various implementation phases: dataset creation, feature extraction, training the classifier, and classification. The datasets of diseased and healthy leaves are collectively trained using Random

Forest to classify the images accurately. For feature extraction, we use Histogram of Oriented Gradient (HOG). Overall, employing machine learning to train the publicly available large datasets provides a clear method to detect plant diseases on a large scale.