Machine Learning-driven Optimal Crop Selection in Response to Market Demand

Sixty percent of Indians work in the field of agriculture, and traditional farming methods are used extensively in these endeavors (Patel and Rane in Crop recommendation system, 2023 [1]). This preference for conventional techniques has made it more difficult to maximize crop yields, which has hindered many farmers’ ability to gain financial profit from the crop yield. Choosing crops with little knowledge of the most profitable crop based on the environment as well as market conditions makes it harder for the farmer to gain the maximum amount of profit. In our study, we have used machine learning in the field of agriculture to provide improved information to the farmers to make them capable of making well-informed decisions that will help them gain more profit from their cultivated crops. Our research utilizes information such as soil nutrients, weather, and climate data, and market demand for the crop to build an intelligent system that can recommend the crop for the most optimal gains. In this research, we have utilized the machine learning algorithms such as Random Forest, Decision Trees, and Naive Bayes to improve accuracy of our recommendation system. The aim of this research is to increase the profit of farmers by providing them with the most optimized option of the crop to cultivate with precision. Our research highlights the optimal use of machine learning in the domain of agriculture and how it can be used to increase the profitability of farmers.