Optimizing Convolutional Neural Networks and Support Vector Machines for Spinach Disease Detection: A Hyperparameter Tuning Study

An analysis of the illnesses affecting the leaves of spinach and their classification using AI and SVM algorithms are presented in this research study. Data gathering, visual examination, and sophisticated image analysis methods based on Convolutional Neural Networks (CNNs) for feature extraction were all part of the study’s methodical methodology. Then, SVM classifiers were trained to identify and categorize diseases using the collected features. The suggested method successfully identified spinach leaf diseases for a variety of disease classes with good accuracy, precision, recall, and F1 scores. The findings help in the creation of specialized disease management strategies by offering useful insights into the occurrence and features of illnesses affecting spinach leaves. In addition, the study emphasizes how AI and machine learning algorithms could enhance illness identification. The classification models may need to be improved, more data sources may need to be investigated, and real-time monitoring systems for early disease detection may need to be integrated into future research paths. Overall, this research advances our knowledge of spinach leaf diseases and lays the road for environmentally friendly spinach farming methods.