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Climate change, market volatility, water scarcity, and pest control issues are just a few of the challenges that farmers face. Limited access to resources and education, as well as the slow adoption of new technologies, aggravate these problems. Creating intelligent systems is necessary to handle these pressing issues. This method makes use of key components such as crop type, weather, and soil properties to forecast agricultural yields. This system offers significant yield prediction capabilities for 53 crops by utilizing complex algorithms including XG-Boost, random forest, and decision tree. It takes into account crucial factors to accurately assess crop production potential, such as temperature, rainfall, nutrient levels (N, P, and K), soil pH, and temperature data. Cutting-edge machine learning techniques look at past data and trends to provide farmers with crucial information they need to make wise decisions. In order to solve the problems farmers, encounter in the modern agricultural sector, this intelligent system aims to enhance resource efficiency, farming systems' resilience and production, and the use of resources.
Sandeep Kumar Shilpa Choudhary (Sun,) studied this question.