<![CDATA[Pest detection and identification play a crucial role in reducing the damage caused by pest, insect and diseases. Timely detection and response are essential to increase the quality and quantity of crop production. Efficient pest management strategies are important for achieving optimal crop quality and promoting sustainable agricultural practices. This research proposes a framework that can automatically detect pests and offer timely solutions to farmers. The proposed approach integrates intelligent computing methods with connected device networks to identify and classify pests in real time with high precision. The methodology focuses on efficiently segmenting the pest from the captured leaf image using a novel region growing based segmentation algorithm. The threshold for region growing based segmentation algorithm is based on the adaptive local region entropy which contributes to the efficient segmentation. Stacked Ensemble Classifier (SEC) is used for the classification. The metrics used for evaluating the performance of the pest detection framework are accuracy, Area Under the Receiver Operating Characteristic Curve, F1-Score and Mean Average Precision (mAP). The results indicate that the proposed SEC with region growing based segmentation framework achieves 98 % of classification accuracy and mAP of 0.96 proving that it is very effective in both classification and segmentation task. The comparative analysis further reveals that the SEC outperforms the existing machine learning models and ensemble learning models like majority voting and weighted average models for process innovation.]]>
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