Enhancements in Agricultural Forecasting in Bundelkhand Facilitated by the Use of Artificial Intelligence

Deepesh Agarwal; Dharamdas Kumar

Enhancements in Agricultural Forecasting in Bundelkhand Facilitated by the Use of Artificial Intelligence

Author(s)	Deepesh Agarwal, Dharamdas Kumar
Country	India
Abstract	The Bundelkhand region comprises more than fifty percent of Uttar Pradesh's total pulse farming area, although its output is inferior to the state average. This requires the execution of many technical interventions, alongside the establishment of infrastructure as well as marketing tactics. Developing sophisticated predictive models is crucial for properly forecasting crop yields, as changes in the climate and rising unpredictability intensify the worldwide food safety dilemma. A sophisticated prediction model for projecting crop yields was created to tackle these difficulties. The model aimed to address concerns about global food security in light of the increasing population. The study focuses on eleven widely farmed crops in Bundelkhand. The Bundelkhand area produces crops like cassava, maize, potatoes, rice (paddy), soybeans, and wheat. Groundnut, cotton, mustard, and cotton. The integration of Artificial Neural Networks, or ANN, alongside the Optimization Algorithm resulted in the development of an artificial intelligence system designed to accomplish this goal. The model integrates meteorological conditions, pesticide use, and historical yield data.After training the ANN with an improved model using 70 percent of the data that was accessible, we assessed its performance with the final thirty percent. A comparison was performed between the model's accuracy and that of ANN models, using statistical benchmarks. The suggested model surpassed other models, attaining superior measurements with an RMSE of 14.83, MAE of 88.53, MAPE of 0.07, and a R² of 0.98 overall agricultural production prediction. The results demonstrate an improvement in the accuracy and reliability of agricultural output forecasts compared to the currently used methods. A comprehensive system aimed at augmenting agricultural production forecasting via enhanced predictive skills, hence possibly improving the effectiveness of resources and optimizing crop management, is shown by the proposed model. This study illustrates the capacity of machine learning to tackle global agricultural issues and improve food security measures, holding substantial importance for agricultural strategy and formulation of policies.
Published In	Volume 6, Issue 12, December 2025
Published On	2025-12-01
Cite This	Enhancements in Agricultural Forecasting in Bundelkhand Facilitated by the Use of Artificial Intelligence - Deepesh Agarwal, Dharamdas Kumar - IJLRP Volume 6, Issue 12, December 2025.

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Enhancements in Agricultural Forecasting in Bundelkhand Facilitated by the Use of Artificial Intelligence

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