<![CDATA[Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification.]]>
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