Abstract
Weed management continues to be a significant challenge in modern agriculture, primarily due to the aggressive growth patterns of weeds and their direct competition with crops for essential resources such as light, water, and nutrients. Although recent developments in precision agriculture have led to the emergence of automated weed detection systems aimed at reducing operational costs and decreasing reliance on chemical herbicides, achieving accurate crop-weed segmentation remains a persistent difficulty. This is largely attributed to high visual similarity between crops and weeds, coupled with variations in illumination and field conditions. To address these challenges, Convolutional Neural Networks (CNNs) have been increasingly adopted for their capability to perform end-to-end, pixel-level classification, particularly when leveraging multi-spectral imagery. In this context, PSPEdgeWeedNet is proposed, a novel edge-aware deep learning architecture tailored for precise semantic segmentation of crops and weeds within peanut cultivation fields. Distinct from the conventional Pyramid Scene Parsing Network (PSPNet) and its boundary-aware variant developed as a baseline in this research, PSPEdgeWeedNet introduces a dedicated edge detection branch. This branch is specifically engineered to enhance boundary localization and improve delineation between adjacent vegetation classes. In post-processing, Conditional Random Fields (CRFs) are used to slightly enhance the segmentation results around object boundaries. Additionally, all models were trained on a curated peanut field dataset using class-weighted loss functions to effectively address inherent class imbalance. Comprehensive experimental evaluations reveal that PSPEdgeWeedNet significantly outperforms existing state-of-the-art architectures including PSPNet, SegNet, UNet, DeepLabv3, Swin-Unet, and light weight transformer model based on ViT across multiple performance metrics such as Intersection over Union (IoU), precision, recall, and F1-score. These results highlight the critical role of incorporating edge-aware mechanisms within semantic segmentation frameworks, thereby enhancing the robustness and accuracy of automated weed detection systems in complex, real-world agricultural environments.