A Resilient Forecasting Model for Sustainable Agriculture and Optimal Yield Production
Keywords:
Machine learning, agricultural yield prediction, hybrid model, decision tree, support vector machine, random forest, gradient boosting and linear regressionAbstract
Given India's enormous breadth and dense population, the forecast of agricultural output is vital for guaranteeing food security. The process, however, is hard due to the influence of a myriad of elements, such as farming techniques, environmental circumstances, and technology improvements. Existing machine learning (ML) models have issues due to the quality and variety of data, model overfitting, sophisticated model architectures, insufficient feature engineering, and temporal dependencies. Therefore, a robust and efficient model that addresses these difficulties is important. In this work, an analysis was undertaken employing five prominent ML algorithms — Random Forest (RF), XGBoost, Decision Tree (DT), Support Vector Machine (SVM), and Linear Regression (LR) — on a crop prediction dataset collected from Kaggle. Algorithms that displayed the highest coefficient of determination (R²) were selected to develop a hybrid model for aggregate prediction. Results revealed that the suggested hybrid model, covering DT, XGBoost, and RF, surpassed individual classifiers in terms of R²score and outperformed the existing models, obtaining an accuracy of 98.6%. This provides a robust and efficient paradigm for agricultural yield forecasting. Consequently, a user-friendly tool, 'Crop Yield Predictor', was developed, rendering the model accessible and practical for on-ground applications in agriculture. This technology effectively turns complicated data and algorithms into actionable insights, bridging the gap between advanced machine learning techniques and practical agricultural applications.
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