An Intelligent Yield Prediction of Crops in Smart Agriculture Management Using Enhanced Fuzzy Based AI Framework

In modern agriculture, there is a developing need for correct predictions of crop yields to optimize production and help resource control. Traditional strategies of yield prediction frequently depend upon manual records collection and analysis, resulting in time-consuming and doubtlessly error-prone effects. It has caused an increased interest in developing intelligent agriculture management systems that leverage superior technology to enhance farming practices. The proposed version explores using a more desirable fuzzy-based AI framework to expect crop yields in a clever agriculture place. Fuzzy judgment is an effective mathematical device that could capture uncertainty and imprecision in data, making it appropriate for agriculture's complicated and dynamic nature. The improved fuzzy-based AI framework uses a combination of fuzzy logic judgment and device mastering techniques to investigate numerous parameters with weather, soil situations, and plant increase patterns to make accurate predictions of crop yields. The system can learn and adapt to changing conditions by incorporating AI, making it more robust and reliable than traditional strategies. This method could revolutionize clever agriculture management by offering farmers actual-time data and hints for optimizing crop production.