Crop Disease Detection using Machine Learning

Abstract

Agriculture is the primary source of food for the world's population, despite the rapid increase in population. Early detection of plant diseases in the field would be beneficial to improve crop production efficiency. Technology has become increasingly important in agriculture in recent years, as it is used to improve efficiency, reduce costs, and increase yields. The emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. Our proposed work includes various phases of implementation of image classification, namely dataset creation, feature extraction, training the classifier, and classification. The work also included hardware design and implementation, as well as software programming for the microcontroller unit of the detector. The system utilized the microcontroller to receive and send data from the various sensors to an online database.