Authors - Josue Piedra, Nelson Piedra Abstract - Accurate crop production forecasting is essential for sustainable agricultural planning, effective resource management, and long-term food security. Conventional statistical and regression-based models often fail to capture the complex, nonlinear relationships that exist among agro-climatic variables, soil characteristics, and crop yield [1]. To address these limitations, this paper proposes an agentic artificial intelligence (AI)–based framework for crop production analysis that integrates autonomous decision-making with machine learning and deep learning techniques. The proposed framework utilizes agro-climatic and soil parameters such as temperature, humidity, soil moisture, cultivated area, and seasonal information to model crop production behaviour. Three predictive approaches— Linear Regression, Random Forest, and CNN–LSTM—are implemented and evaluated within the agentic framework using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) as performance metrics. Experimental results demonstrate that the Random Forest model significantly outperforms the other models, achieving an RMSE of 0.56, MAE of 0.31, and R2 value of 0.96. These findings highlight the effectiveness of agent-driven machine learning systems in accurately modelling agricultural data and supporting intelligent decision-making for crop yield optimization.
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