article · Neural Computing and Applications
Accurate crop yield prediction is crucial for food security and improving agricultural practices, yet standard methods often fail to reflect complex environmental interactions. A three-phase machine learning framework addresses these limitations by pairing refined feature selection with hyperparameter tuning for a Support Vector Regressor. The initial preprocessing phase carries out data normalisation and uses K-means clustering alongside a correlation-based filter to reduce the dataset. Next, a hybrid feature selection approach, designated FMIG-RFE, identifies the most influential variables. Finally, the prediction phase applies an improved variant of the Crayfish Optimisation Algorithm, termed ICOA, to automatically tune the regressor hyperparameters. Experimental evaluations confirm that this integrated framework outperforms existing state-of-the-art methods, delivering higher prediction accuracy and greater computational efficiency when modelling crop yields.
Reliable yield forecasts help farmers and agricultural planners make informed decisions, allocate resources effectively, and safeguard food supplies. By capturing complex environmental factors and automating model tuning, this approach improves the accuracy of yield estimates without requiring demanding manual adjustments, making computational tools more dependable for agricultural decision support.
The framework is aimed at agricultural software providers, farm managers, and food security agencies seeking more accurate crop forecasting models. Being an algorithmic architecture tested experimentally against standard benchmarks, the technology sits at an applied research stage. Further development into usable agricultural software or integration with farm management information systems would be needed before direct commercial deployment.
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Abstract Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms.
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DOI: 10.1007/s00521-024-10226-x
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