8Application of Machine Learning in Precision Agriculture

Ravi Sharma* and Nonita Sharma

Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India

Abstract

Agriculture is one of the most prominent sectors that add a significant contribution to the economy of any nation. The whole cycle of crop processing from soil mapping to harvesting is time-consuming and does not yield an acceptable outcome due to lack of experience and time. Precision Agriculture (PA) 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. Further, Machine Learning is applied in agriculture to help monitor the usage of agro-chemicals and to generate more benefit from the prospective of farmers and environment. A different machine learning technique being applied in agriculture is discussed in this chapter. The chapter attempts to correlate the numerous Machine Learning applications in the field of precision farming primarily in soil mapping, seed selection, irrigation, crop quality, disease detection, weed detection and yield prediction. Various Machine Learning models, Support Vector Machine (SVM), K-mean, Convolution Neural Network (CNN), Artificial Neural Network (ANN) are studied and use of these models is demonstrated in smart farming.

Keywords: Artificial neural network, machine learning, support vector machine, ...

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