Özet:

Abstract Modern crop yield prediction helps farmers and policymakers maximize agricultural operations. Predicting crop yields is difficult, especially given scant agricultural datasets. This paper proposes a novel method that combines K-Fold validation and multi-model ensemble approaches to improve crop production forecast accuracy and address sparse data. Our technique starts with an improved sparse data clustering process that efficiently groups comparable data points and mitigates the impact of missing or limited information. Clustering helps us find patterns and trends in data, reducing the impact of data sparsity on crop production projections. K-Fold validation, a strong cross-validation method, is used to evaluate various prediction models. We test each model on different folds by partitioning the data into K subsets. K-Fold validation validates the generalizability of our multi-model ensemble strategy, improving crop production estimates. 5-fold validation of multi-models like SVM, CNN, DT, NN, and NB predicts. Predictions depend on "log of" performance. Our methodology works on real-world agricultural datasets through considerable experimentation and comparison with existing methods. In scarce data, crop yield forecast accuracy improved significantly. Our ensemble of models beats individual models, demonstrating the value of many approaches for prediction. In conclusion, K-Fold validation and multi-model ensembles improve crop production prediction accuracy, especially with scarce agricultural data. This research can improve agricultural decision-making and sustainability by developing more precise predictions.

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