Abstract
This study aims to develop an intelligent agricultural yield recommendation framework leveraging the capabilities of AI algorithms. The proposed framework takes yield efficiency and optimal growing seasons as crucial factors in generating appropriate crop recommendations. We have put forth four widely used models, namely Linear Regression (LR) and Multi-Layer Perceptron (MLP), which were trained and evaluated on a comprehensive dataset comprising historical agricultural data encompassing various features such as climatic factors, soil properties, and geographical variables. Furthermore, the data was segmented based on seasonal patterns to provide crop suggestions tailored to specific time periods. The performance of these models was assessed using standard metrics, and an ensemble approach was considered to enhance the system's robustness. Ultimately, the developed framework offers farmers and agricultural professionals a valuable tool for making informed decisions, optimizing crop selection, and enhancing overall agricultural productivity.
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