Advanced Disease Classification of Rutabaga plant using CNN-SVM: Enhancing Early Detection and Precision Agriculture

This study introduces an exhaustive classification system for the detection of rutabaga plant diseases is presented in this research. Clubroot, Powdery Mildew, Downy Mildew, Aphid-Transmitted Viruses, Rust Diseases, and Blackleg (Phoma lingam) are particularly highlighted. The model's outstanding performance is shown by its accuracy result (94.38–96.09 %), recall (93.10%–96.22%), and F1-Score (95.23%). Measures like these prove that the model is accurate and useful for detecting diseases. Increasing the model's practical use, the assistance percentage numbers give light to the dataset's distribution across various disease categories. The weighted average demonstrates the model's endurance across many classes, with a total accuracy of 95.24 %. At the same time, micro and macro averages provide thorough assessments of its efficiency. Utilizing state-of-the-art technologies like CNN and SVM, this research presenting a robust model for rutabaga classification. It is contributing to precision agriculture. Since the findings provide a way for targeted treatments and early disease detection, this is very significant for agricultural methods. The improved agricultural yield and increased food security on a worldwide scale are the results of this measure.