Research on Crop Growth Prediction Based on Improved SVM Algorithm

Computer algorithms play a pivotal role in optimizing agricultural management and enhancing yield efficiency through accurate crop growth prediction. This study presents an improved Support Vector Machine (SVM) algorithm designed to increase the precision of crop growth forecasting. The proposed approach integrates feature selection and hyperparameter optimization techniques to overcome the limitations of traditional SVM models, such as their sensitivity to parameter settings and redundancy in features. A case study using real-world agricultural data demonstrates the effectiveness of the improved SVM algorithm when compared to conventional machine learning models. The experimental results reveal that the proposed method achieves higher prediction accuracy, robustness, and generalization performance, making it a valuable computational tool for supporting agricultural decision-making.