Optimizing Agricultural Yields Using AI and Machine Learning

The paper explores the profound impact of AI and ML on sustainable farming practices and the optimization of agricultural production. The article highlights significant developments in data-driven decision-making for producers, such as autonomous systems, predictive analytics, and precision agriculture. Modern precision agriculture utilizes advanced technology to continuously monitor the health of crops, while predictive analytics leverages cutting-edge techniques like Support Vector Machines and regression models to forecast yields using historical data and present circumstances. Through the integration of AI, autonomous robots play a crucial role in minimizing chemical usage and labor costs by enabling precise pesticide application and effective weed removal. There are still lingering challenges related to data accessibility, scalability, quality, and adoption. Addressing these challenges requires the implementation of comprehensive training programs and enhanced data acquisition methods. It will empower farmers and ensure the successful integration of AI and ML in agriculture. Through the implementation of these innovations, the agriculture sector has the potential to see significant improvements in output, environmental impact, and food security.