Plant Disease Detection using Machine Learning

Abstract

Plant disease detection is a cutting-edge and enlightening system that helps users learn about diseases, training, and other fascinating events happening in their local area. This organization helps the local population stay informed about activities in and around their town, region, or locale. This approach requires both machine learning and image processing in order to function. The accuracy of the results has been improved by using contemporary methods like machine learning and deep learning algorithms. As a whole, random forests are a learning technique for problems like classification, regression, and others that work by building a forest of decision trees during the training period. A component descriptor used in computer vision and image processing for object detection is the histogram of oriented gradients (HOG). In this case, we are using three component descriptors: 1. Hu moments 2. Haralick texture 3. Colour Histogram