[{
  "type": "article-journal",
  "title": "HYBRID DEEP LEARNING FOR PRECISION PEST DETECTION USING FUSED-MBCONV AND GMLR",
  "author": [
    {
      "family": "V",
      "given": ""
    },
    {
      "family": "K",
      "given": ""
    }
  ],
  "issued": {
    "date-parts": [[2025]]
  },
  "container-title": "Journal of Animal and Plant Sciences",
  "ISSN": "1018-7081",
  "volume": "35",
  "issue": "6",
  "page": "1571-1584",
  "DOI": "https://doi.org/10.36899/JAPS.2025.6.0133",
  "abstract": "<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>",
  "publisher": "Pakistan Agricultural Scientists Forum",
  "URL": "https://thejaps.org.pk/AbstractView.aspx?mid=2025-JAPS-228"
}]