Automated Crop Yield Prediction System Using Machine Learning Algorithm

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P. Bhasha, Dr. J. Suresh Babu, Muniraju Naidu Vadlamudi, Kochumol Abraham, Sanjaya Kumar Sarangi


Agriculture is one of the most important sources of food and one of the most important social problems. Due to expanding populations, food scarcity or shortages are presently a problem in many nations. Crop production prediction is challenging due to several complex factors. Crop production is often influenced by a range of variables, such as timing of harvest, weather, water availability, water quality, genotype, insect infestations, soil quality, terrain, and others. Based on prior performance and trustworthy historical data, farmers used to anticipate crop production and then make significant cultivation decisions in line with the projections. Using a range of features, machine learning (ML), a branch of artificial intelligence (AI) that focuses on learning, is a practical technique that can estimate yields more precisely. The major goals of this research are to estimate crop yield production using a machine learning algorithm and to provide farmers with an easy-to-use user interface.

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