A study on agriculture engineering implements using machine learning

Abstract

India's primary source of income is agriculture. Farmers in India have differing perspectives on how to integrate technology into their farming operations. However, farmers lack the knowledge necessary to put technology into practice, even though crop yield productivity is improving. We performed a literature survey to get the suggestion of what types of agriculture engineering machinery had been employed to boost up crop production output. After some literature survey, this research work followed to implement machine learning techniques to provide better advice to farmers to use the agriculture engineering machinery to minimize manpower and boost crop productivity. According to this additional analysis, clustering is the most widely used machine learning technique to solve problems in agriculture fields. Regarding the execution time, k-means performed well for other clustering techniques for small and large datasets. In this research initiates three thousands of combined data in agri-machinery in various districts, this selective data should be assured the preferable results with the help of k-means clustering for an crops highest yield production. This research work fully engaged with k-means techniques to deeply analysis in the Agri machinery benefits for south region districts and it provides the strongly recommended machinery's knowledge to farmers to implement the farming works effectively. In this research that statistical ranges are predicted what type of machinery's gives good results for crop development in south TamilNadu farming land in the district wise. The goal of this study was to evaluate statistical ranges of agriculture types of machinery in south TamilNadu districts. This proposed effort aims to provide a better proposal for achieving a suitable level of crop output with the use of more agricultural technology.