Application of Machine Learning in Precision Agriculture

Abstract

Agriculture is one of the most prominent sectors that add a significant contribution to the economy of any nation. Precision Agriculture involves the application of Machine Learning (ML) methods to produce strong outcomes and forecasts for the development and well identification of disease and cannabis well in advance. This chapter attempts to correlate the numerous ML applications in the field of precision farming primarily in soil mapping, seed selection, irrigation, crop quality, disease detection, weed detection and yield prediction. Machine learning is about training robots to do what appeals to human beings naturally: knowing from practice. Crop yield prediction plays a crucial role in global food production and supply chain. Agricultural production depends primarily on temperature, pests, and harvest process preparation. One of the main reasons for using supervised machine learning technique is it require less data and time to train.