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The agriculture sector is critical to satisfying the world's ever-increasing food need. Accurate crop yield prediction is critical for ensuring sustainable agriculture and optimizing agricultural production. Our system effectively analyses many elements influencing crop development, such as soil qualities, climate patterns, and historical agricultural data, by integrating environmentally conscious methods and utilizing the power of AI algorithms. The predictive model predicts crop production with high accuracy, allowing farmers and government or NGOs to make educated decisions about resource allocation and productivity while reducing environmental effect. Green principles combined with cutting-edge AI technology in crop yield prediction offer a viable answer for sustainable agriculture and promises a more robust food supply system in the future. In this article, the proposed system uses the Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model, which provides more accurate results than the prior models and can be used to predict the production of any type of crop. The outcomes show considerable increases in forecast accuracy and give useful information about the elements that affect crop output.
Perumal et al. (Tue,) studied this question.