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<p>Accurate crop yield prediction is critical for sustainable agricultural planning and resource optimization, especially amid increasing food demand and climate variability. This study proposes a novel two-tiered machine learning (ML) framework that integrates IoT-based soil data with advanced classification and regression models to enhance prediction accuracy. In the first tier, an Adaptive k-Nearest Centroid Neighbour (aKNCN) classifier evaluates soil quality based on key nutrient metrics. The second tier utilizes an Extreme Learning Machine (ELM) optimized via the modified Butterfly Optimization Algorithm (mBOA) to forecast crop yields, incorporating both soil quality and agro-environmental factors. The system is trained and validated on a publicly available Indian crop production dataset containing 10,000 samples across major crops (wheat, maize, rice), with features including soil moisture, temperature, and rainfall. Feature selection is performed using Correlation-Based Feature Selection (CBFA) and Variance Inflation Factor (VIF) methods to reduce noise and multicollinearity. Experimental results demonstrate that the proposed aKNCN-ELM-mBOA model significantly outperforms traditional ML models—such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Gradient Boosting (GB), and Random Forest (RF)—in terms of error metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R². The model achieves a notably low RMSE of 0.301 and MAPE of 3.932, alongside a high R² score of 0.817, indicating strong generalization. This approach underscores the potential of hybrid ML systems, enriched by IoT-driven data and robust optimization, to drive precision agriculture and informed decision-making. Future work may involve time series forecasting and scaling the model with real-time sensor data for broader deployment.</p>

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Precision Agriculture Using a Two-Tier ML Model: Integrating aKNCN Soil Classification with ELM-mBOA Yield Prediction

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Awad Bin Naeem,
  • Biswaranjan Senapati,
  • Jawad Rasheed,
  • Fazeel Abid,
  • Shtwai Alsubai

摘要

Accurate crop yield prediction is critical for sustainable agricultural planning and resource optimization, especially amid increasing food demand and climate variability. This study proposes a novel two-tiered machine learning (ML) framework that integrates IoT-based soil data with advanced classification and regression models to enhance prediction accuracy. In the first tier, an Adaptive k-Nearest Centroid Neighbour (aKNCN) classifier evaluates soil quality based on key nutrient metrics. The second tier utilizes an Extreme Learning Machine (ELM) optimized via the modified Butterfly Optimization Algorithm (mBOA) to forecast crop yields, incorporating both soil quality and agro-environmental factors. The system is trained and validated on a publicly available Indian crop production dataset containing 10,000 samples across major crops (wheat, maize, rice), with features including soil moisture, temperature, and rainfall. Feature selection is performed using Correlation-Based Feature Selection (CBFA) and Variance Inflation Factor (VIF) methods to reduce noise and multicollinearity. Experimental results demonstrate that the proposed aKNCN-ELM-mBOA model significantly outperforms traditional ML models—such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Gradient Boosting (GB), and Random Forest (RF)—in terms of error metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R². The model achieves a notably low RMSE of 0.301 and MAPE of 3.932, alongside a high R² score of 0.817, indicating strong generalization. This approach underscores the potential of hybrid ML systems, enriched by IoT-driven data and robust optimization, to drive precision agriculture and informed decision-making. Future work may involve time series forecasting and scaling the model with real-time sensor data for broader deployment.