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Research Scholar, Department of Computer Science, Kongunadu Arts and Science College , Coimbatore, Tamil Nadu , India
Associate Professor, Head, Department of Information Technology, Kongunadu Arts and Science College , Coimbatore, Tamil Nadu , India
This study presents an advanced approach for Pest Detection (PD) through automatic identification of invasive insects using Deep Ensemble Learning (DEL) and intelligent Feature Extraction (FE) techniques. The process begins with Z-score normalization (ZSN) to eliminate noise and improve classifier performance. Improved Feature Weighted Fuzzy Clustering (IFWFC) model is presented to achieve effective data grouping and Henon Chaotic Map Encryption (HCME) model to encrypt the image is presented. The extraction of the feature step involves the application of Hybrid Enhanced Wild Horse Optimization (EWHO) and Gray-Level Co-occurrence Matrix (GLCM) to extract the features accurately. The feature selection (FS) is performed on a modified version of Cuckoo Search Algorithm (MCSA) to achieve a precision in the model. The DEL model combines several deep learning models such as ResNet 50, Enhanced DenseNet201 and Granular Neural Network (GNN), which are optimized using the Pelican Optimization Algorithm (POA), to achieve a better classification. The IP102 data set was used to test the system with a better score of 99.90 as accuracy, 99.92 as precision, and 99.91 as a recall. The proposed model is much better than the current pest recognition techniques in classification with respect to accuracy, thus making it useful in real-time pest detection. This study presents the prospect of DEL-based model in precision agriculture and provides an efficient method of pest identification which can lead to better crop production and less dependence on pesticides.
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