Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, which demand large-scale, finely annotated datasets and often suffer from low generalization. To address these challenges, this study proposes a novel dual-model framework that simplifies the task by dividing it into two tractable stages. First, a crop segmentation network is used to identify and remove cabbage (Brassica oleracea L. ssp. pekinensis) regions from field images. Since crop categories are visually consistent and singular, this stage achieves high precision with relatively low complexity. The remaining non-crop areas, which contain only weeds and background, are then subdivided into grid cells. Each cell is classified by a second lightweight classification network as either background, broadleaf weeds, or grass weeds. The classification model achieved F₁ scores of 95.1%, 91.1%, and 92.2% for background, broadleaf weeds, and grass weeds, respectively. This two-stage approach transforms a complex multi-class detection task into simpler, more manageable subtasks, improving detection accuracy while reducing annotation burden and enhancing robustness under the tested field conditions.

Weed control is crucial for optimizing corn yield. In recent years, advances in computer vision and deep learning have created new opportunities for precision agriculture. However, annotating weed datasets is typically time-consuming, labor-intensive, and costly. To address this challenge, this study proposes an indirect weed detection strategy that reduces reliance on explicit weed annotations by focusing on accurate crop segmentation. Specifically, we develop YOLO-CornSeg, a lightweight segmentation model based on an improved YOLOv8n architecture, designed for precise corn seedling segmentation. The model incorporates a C2f_DWR module to enhance multi-scale feature extraction and a Segment_Efficient head to improve segmentation performance while maintaining computational efficiency. Based on the resulting segmentation masks, an indirect weed detection strategy is applied, in which non-crop green regions are identified as weeds using HSV-based image processing. Experimental results show that YOLO-CornSeg achieves a mean Intersection over Union (mIoU) of 91.1% with a model size of 8.3 MB, outperforming several state-of-the-art two-stage semantic segmentation models while maintaining low computational complexity and a compact model size. The improved segmentation accuracy further enhances the reliability of downstream weed inference. Overall, this study highlights the potential of combining lightweight crop segmentation with indirect weed detection strategies to support precision herbicide application.

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems.