Semantic Segmentation for Pest Detection in Crops Using a Global-Local Representation Learning Network

Precise segmentation of pests in agricultural images is highly necessary for early infestation detection, effective pest monitoring, and significant crop protection. Anyhow, the variations in background complexity, pest appearance, and noise frequently minimize the precision and stability of traditional segmentation approaches. To overcome this challenge, this research proposes a deep learning-based pest segmentation framework that combines Adaptive Weighted Mean Filtering (AWMF) with the Global-Local Representation Learning Network (GlLo-SegNet). Initially, AWMF is applied using the proposed method to minimize the undesired biases and enhance image quality during preprocessing. Then, the GlLo-SegNet-based segmentation process is applied by integrating global semantic information and local geometric information using multi-scale convolution (MC), multi-scale pooling (MP), and multi-scale feature fusion strategies. Additionally, in order to enhance multi-scale fusion and semantic segmentation accuracy, an attention-based decoder is designed. The significance of the proposed approach lies in the capability to attain precise and stable pest segmentation in the case of challenging agricultural imaging conditions. The experimental outcomes indicate the model’s effectiveness on two benchmark datasets. On the agricultural pest dataset, the proposed method attained 97.76% recall, 98.74% precision, and 98.25% Dice coefficient. Using the crop pest and disease detection dataset, 97.26% recall, 98.82% precision, and 98.13% Dice coefficient are obtained. These outcomes ensure that the proposed model presents an effective and robust solution for agricultural pest segmentation.