Machine learning and deep learning in agriculture

Abstract

Agriculture is the backbone of our economy, as the requirement of foodstuff due to rise in population is constantly increasing. Weed detection and prevention is difficult to discriminate from crops, so machine learning using sensors is used. Deep learning covers several layers of neural networks designed to perform more cultured tasks. One of the main differences between machine learning and deep learning is deep learning requires more data for classification whereas small data are enough for classification in machine learning. Neural network and back propagation are the basis of deep learning. Major applications of deep learning in agriculture can be classified as plant or crop classification, pest and yield prediction, robot harvesting, monitoring of disaster etc. Mainly disease recognition model for plants can be applied by leaf image and pattern classification. Berkley Vision and Learning Centre has developed a novel deep learning framework to build a disease detection model.