17 Ramya V Geetha K Journal of Animal and Plant Sciences JAPS 20252025/11/30 35 6 1571-1584 1018-7081 https://doi.org/10.36899/JAPS.2025.6.0133 <p><span lang="EN-IN">Insect pests</span><span lang="EN-IN">&nbsp;pose a significant threat to crop yields, leading to considerable economic losses in agriculture. This study presents two innovative deep learning-based methods for intelligent pest detection. The first approach integrates the Fused-MBConv architecture with a Gradient-based Multinomial Logistic Regression (GMLR) activation function to enhance classification performance through efficient feature extraction and improved non-linear decision boundaries. The second method leverages hyperspectral correlation features derived from normalized cross-correlation matrices, exploiting unique spectral signatures of insects for precise identification. A dataset of 3,516 real-world insect images spanning 12 species was used for training and evaluation. Experimental results demonstrated that the proposed FMBC-GMLR model achieved a classification accuracy of 96%, outperforming conventional models such as ResNet50, YOLOv3, and MobileNet in precision, recall, and F1 score. These findings underscore the model&rsquo;s robustness and potential for deployment in smart farming systems to enable early pest detection and reduce crop damage.</span></p> Deep Learning; Precision Agriculture; Hyperspectral Cameras; Automated Pest Detection Pakistan Agricultural Scientists Forum https://thejaps.org.pk/AbstractView.aspx?mid=2025-JAPS-228