Data-Driven Crop Yield Prediction With Weather Patterns and Soil Health Parameters Using Efficient Machine Learning, Deep Learning, and Optimization Strategies
DOI:
https://doi.org/10.29284/0drkh815Keywords:
Crop Yield Prediction, Machine Learning, Deep Learning, Weather Parameters, Soil Nutrient Analysis, Optimization Techniques, Precision Agriculture.Abstract
Agriculture plays a significant role in ensuring food security and economic development. Accurate prediction of crop yield helps farmers and policymakers make informed decisions regarding crop management, resource utilization, and agricultural planning. This study proposes a data-driven approach for predicting agricultural yield using weather conditions and soil nutrient parameters. The dataset consists of variables such as temperature, rainfall, humidity, nitrogen, phosphorus, potassium, soil pH, crop type, and crop yield. Various Machine Learning algorithms, including Linear Regression, Support Vector Machine (SVM), Random Forest, and XGBoost, are employed to analyze the relationship between environmental factors and crop productivity. In addition, Deep Learning models such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks are utilized to capture complex patterns in the data. To improve model performance and prediction accuracy, optimization techniques such as Grid Search, Bayesian Optimization, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA) are applied. The performance of the models is evaluated using statistical measures such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The results demonstrate that optimized machine learning and deep learning models provide reliable crop yield predictions, which can support precision agriculture and sustainable farming practices.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
