Review of Machine Learning Model Applications in Precision Agriculture

摘要

Over the past two decades, modern agriculture has made significant advancements.The methods used in farming have changed from conventional ways to digital technologies as a result of significant technology improvement.Advances in machine learning and artificial intelligence are being applied in this discipline to reevaluate farming practices in order to meet the demands of an expanding population.Throughout the entire cycle of planting, growing, and harvesting, machine learning is prevalent.It starts with the planting of a seed in the ground, goes through soil preparation, seed breeding, crop health monitoring, measuring water feed and concludes with the harvest being picked up by robots by using computer vision techniques.For crop selection, yield prediction, soil classification, weather forecasting, irrigation system, fertilizer prescription, disease prediction, and determining the minimal support price, machine learning models are developed in the field of precision agriculture.In this article we will cover the different categories of precision agriculture applications and use of machine learning models in those different categories.Various models in precision agriculture include Artificial Neural Networks, Support Vector Machines (SVMs), Convolution Neural Networks (CNN), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means Clustering.The ultimate solution to issues in agriculture rests in the efficient application of Machine Learning (ML).ML can bring about a paradigm change in nations like India where agriculture is the main source of employment.Since most Indian rural areas have adopted digitalization, ML and AI-related applications are gradually emerging in this sector.