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Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment
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Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment
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Journal Article
Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment
2022
Overview
Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.
