Plant Disease Detection Using Machine Learning Techniques: A Comprehensive Approach

Abstract

The key to preventing losses in the production and quantity of agricultural products is the identification of plant diseases. The research on plant diseases refers to examinations of patterns on the plant that may be observed with the naked eye. For agriculture to be sustainable, plant disease diagnosis and health monitoring are essential. Manually keeping track of plant diseases is exceedingly challenging. It necessitates a huge amount of work, knowledge of plant diseases, and lengthy processing times. Hence, by taking photos of the leaves and comparing them to data sets, image processing is utilized to find plant illnesses. The data set includes several plants in picture format. Users are routed to an e-commerce website where several pesticides are listed along with their prices and usage instructions in addition to the phases of implementation included in our proposed study are dataset construction, feature extraction, classifier training, and classification. The datasets produced by to classify the photos of sick and healthy leaves; a collective Random Forest model is trained. Using the Histogram of an Oriented Gradient, we may extract characteristics from a picture. Overall, we can clearly identify the illness present in plants on a massive scale by utilising machine learning (ML) to train the vast data sets that are publicly available. detection.