Integrating Machine Learning for Precise Crop Yield Prediction

Considering that climate change causes uncertainty in agricultural production, with changing weather conditions, accurate forecasting of crop yields has become very important in matters of food security. This article introduces a novel predictive model of crop yield. The predictive model integrates historical yields, soil health, and the weather to present reliable forecasts to farmers. Empowering a farmer in planning will make the model enable him or her to plan better, harvest better, minimize risks, and give proper guidance to better decisionmaking. In an indirect way, it will have impacts on farming beyond just aiding governments and agricultural stakeholders at large to achieve the more efficient allocation of resources, stabilize market prices, and decrease food waste. This contribution is meant for the new smarter and more sustainable agricultural practices of answering the emerging problems of this new world and comes with the data-driven step-up real-time prediction; our actual purpose is to convert the agriculture business into a highly robust industry with greater productivity to yield long-term sustainability in environmental as well as in economic fronts.