Abstract
Rice production is hugely affected by the rice blast disease among all the diseases. This work’s proposed a Modified Artificial Bee Colony Optimization Algorithm based Incremental classifier Design which typically considers the benchmark solution during prediction. But, pre-processes rely on the window size to the incremental classifier taking a greater amount of training time if a large sized dataset is considered. Therefore, this research work presents a smart segmentation and hybrid machine learning based classification for effectively predicting the rice disease. This technique combines pre-processing, segmentation, feature extraction/selection, and finally classification. Initially, the Synthetic Minority Oversampling Technique based preprocessing is introduced for data normalization process. Secondly, Modified Feature weighted Fuzzy Clustering based segmentation is used for performing the segmentation efficiently. Then the Feature extraction is done using Principal component Analysis to enhance the classifier performance. Linear Discriminant Analysis performs the feature selection. Finally, Enhanced Recurrent Neural Network is combined with Support Vector Machine called hybrid classification model which is intended to improve prediction performance. Accuracy, recall, precision, timing, and F-measure metrics are used to gauge the effectiveness of the outcomes. The simulation results show that the suggested approach outperforms the current classifiers in terms of performance.
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Senthilkumar, T.P., Prabhusundhar, P. Prediction of rice disease using modified feature weighted fuzzy clustering (MFWFC) based segmentation and hybrid classification model. Int J Syst Assur Eng Manag (2023). https://doi.org/10.1007/s13198-022-01835-7
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DOI: https://doi.org/10.1007/s13198-022-01835-7