Precision Agriculture and the Role of Machine Learning

Abstract

Agriculture and related industries employ approximately seventieth of India’s labour force, while agriculture contributes approximately seventeen percent of India’s gross domestic product (GDP). In addition to the production of food, revenue, and jobs, the agricultural sector is responsible for the production of a diverse range of other goods and services. In addition to the provision of necessary primary resources, this is another purpose served by it. However, many of the practices that are used in current crop farming can trace their roots back several hundred years earlier. When trying to forecast which crops will flourish in different climates, Indian farmers still have a difficult time because there is not enough information available. The purpose of this research is to provide a summary of the numerous and varied strategies that AI has been suggested using to boost agricultural production in various regions of the world. The overall notion that directs the use of these strategies is known as ”precision agriculture,” and the idea of ”crop recommender systems” is a subset of that concept. This study presents a variety of different methodologies, some of which includeSimilarity-based Models, KNN, Ensemble-based Models, Neural Networks, and many more. In order to create recommendations that optimize productivity while minimizing costs, these algorithms take into account not only the profile also texture of the soil but also the weather and temperature.