DIAGNOSIS OF CROP DISEASES BASED ON THE GASVM HYBRID ALGORITHM

The article discusses the problem of diagnosing diseases of crops based on machine learning methods. A hybrid GA-SVM algorithm is proposed, combining the support vector machine (SVM) method and a genetic algorithm for optimizing the hyperparameters of the model. A comparative analysis with the classical GridSearch-SVM method is carried out. Experimental results have shown that the proposed approach provides higher classification accuracy and model stability. Improved Accuracy, Precision, Recall and F1-score indicators have been achieved, as well as a reduction in the number of classification errors. Despite the increased computational costs, the GA-SVM method demonstrates a more efficient search for optimal parameters. The results obtained confirm the prospects of using hybrid algorithms to improve the quality of diagnosis of plant diseases and can be applied in intelligent agricultural systems.

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