Agricultural Informatics

1A Study on Various Machine Learning Algorithms and Their Role in Agriculture

Kalpana Rangra and Amitava Choudhury*

School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

Abstract

The term machine learning indicates empowering the machine to gain knowledge and process it for decision making. The domain of crop production is very important for organizations, firms, products related to agriculture. Data collection is done from different sources for crop forecasting. The collected data may vary in shape, size and type depending upon the source of collection. Agricultural data may be collected from metrological sources, agricultural and metrological, soil, sensors that are remotely installed, agricultural statistics, etc. Marketing, storage, transportation and decisions pertaining to crops have high requirement of accurate data that should be produced timely and can be used for predictions.

Keywords: Agriculture, machine learning, smart farming, decision tree, crop prediction, automated farming, ML models for agriculture

1.1 Introduction

Machine learning can be studied under two vast categories called supervised and unsupervised learning. Supervised learning pertains to fact that data and process is supervised by supervisor. The process of training data is controlled to find the conclusions for new data. Some of the most commonly used techniques for supervised learning are Artificial neural network, Bayesian network, decision tree, support vector ...