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The world is facing a number of challenges such as climate change, diminishing soil quality and stagnant crop yields, which call for scalable solutions to ensure food security for a growing population. In this paper, we explore the role of data science in modern agriculture, highlighting the importance of big data analytics, geospatial technology, and machine learning. We use the datasets collected in a decision support tool called Rice Crop Manager (RCM) and employ machine learning algorithms to estimate yield targets, which can then be used to generate field-specific nutrient management recommendations. The proposed methodology involves extracting useful insights from
the data by using statistics, artificial intelligence (AI), domain knowledge, and predictive analysis. We use cutting-edge technologies like machine learning to develop a model that can select the optimal parameters to generate the recommendations. We estimate the target yield with a calibration and validation ratio of 70:30 through performance evaluation, particularly by computing R-Square, root mean square error, and mean absolute error. Our analysis shows that this approach is well-suited for the acquired data, and it could be automated to derive more valuable information for the benefit of farmers. We propose expanding RCM's functionality to include machine learning for estimating target yields, with the aim of optimizing nutrient management recommendations and reducing decision-making time for farmers and stakeholders. Our study emphasizes the untapped potential of underutilized spatial and temporal data collected by decision-making tools, positioning machine learning as a catalyst for improved resource management in agriculture. The findings contribute to ongoing efforts in sustainable and innovative agricultural practices.
