Abstract— In many areas, ragi is a significant staple crop that is valued for both its nutritional content and resistance to harsh environmental factors. For farmers to optimise cultivation techniques, schedule harvests, and ensure effective resource use, accurate yield prediction is essential. The dataset is split into training and testing subsets during the model-training stage. The XGB Regressor algorithm picks up patterns and connections between the input features and the related Ragi yields as it learns from the training data. Several assessment metrics, including mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared), are used to assess the performance of the model. The model can be used to forecast Ragi yields for fresh, unforeseen data once it has been trained and validated. Farmers can input climatic and agricultural characteristics now or in the future, and the model will produce an estimate of the anticipated Ragi output. In order to plan crop rotations, use irrigation and fertiliser efficiently, and reduce potential output losses, farmers can benefit greatly from this prediction. This study's advantages include better agricultural planning and resource management, which can boost production, cut costs, and improve sustainability in the cultivation of ragi. The model provides precise yield projections by utilising the XGB Regressor algorithm, which can help Ragi farmers make better-informed decisions and achieve better results.

PDF | DOI: 10.17148/IJARCCE.2023.125162

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