A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning
CFS; Crayfish optimization; Crop yield prediction; Feature selection; Hyperparameter optimization; Information gain; Machine learning; Support vector machine; Support vector regression
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. The Author(s) 2024.
Neural Computing and Applications, Vol-36, No. 33, pp. 20723-20750.
Springer Science and Business Media Deutschland GmbH
All Open Access; Hybrid Gold Open Access
Abdel-salam M., Faculty of Computer and Information Science, Mansoura University, Mansoura, Egypt; Kumar N., Department of CSE, Thapar Institute of Engineering and Technology, Deemed to Be University, Patiala, 147004, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, India