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Weeds pose a major threat to soybean yield during the early seedling stage, where accurate identification of their spatial locations and contours is essential for precise field management. This study proposes an improved UAV-based YOLOv11-seg framework for high-precision weed segmentation in soybean fields. A real-field weed dataset was established under complex agricultural environments. A UAV-inspection-oriented, task-driven improved YOLOv11-seg weed segmentation method is proposed. The core of this method lies in the targeted integration and adaptation of existing modules to optimize small-target perception. To enhance detection accuracy, the backbone and neck C3K2 modules were replaced with RCSOSA (reparameterized convolution based on channel shuffle and one-shot aggregation). A Spatially Enhanced Attention Module (SEAM) was integrated into the C2PSA block to better distinguish small weeds from soybean seedlings, while the inverted Residual Mobile Block (iRMB) and adaptive down-sampling module (ADown) improved feature representation and reduced detail loss in low-contrast scenes. Experimental results show that the proposed model achieves mAP@0.5(Box) = 0.89 and mAP@0.5(Mask) = 0.84, surpassing mainstream models such as YOLOv8s-seg and YOLOv12s-seg, with lower computational cost (25.3 GFLOPs, 8.3 M parameters). The main contribution of this study lies in establishing a complete and practical end-to-end engineering workflow, spanning from accurate UAV image recognition to the generation of variable-rate application prescription maps. By integrating with the ArcGIS Pro platform, this solution achieves a fully automated pipeline from perception to decision-making, offering reliable technical support for intelligent weed control during the seedling stage in precision agriculture.

Crop weed segmentation is one of the most challenging tasks in the field of computer vision. This is because, unlike other object detection or segmentation tasks, crop and weed are similar in terms of spectral features, shape, dimensions, etc. For precision agriculture to flourish in terms of smart spraying of crops, efficient systems to distinguish between crop and weed are the need of the hour, which if precise, will take a huge step toward solving the issue of food scarcity. To tackle this issue, we propose new ensemble architecture of two models—a U-Net with a modified backbone and an encoder–decoder. These networks learn to distinguish between soil and crop and soil and weed, respectively, whose ensemble gives state-of-the-art results on pixel-wise annotations of combined crop and weed images. Moreover, it also learns that the model captures un-annotated features since each component of the architecture learnt either crop or weed features to high precision. Finally, the proposed architecture is compared with the U-Net and SegNet, which are popular segmentation networks, and consistently achieved better results.

Precisely segmenting multi-category farmland weeds is of great significance for achieving targeted weeding and sustainable agriculture. However, the similar morphology between field crops and weeds, complex occlusions, variable lighting conditions, and the diversity of target scales pose severe challenges to the accuracy and efficiency of existing methods on resource-constrained platforms. This study proposes a novel instance segmentation framework, CPD-WeedNet, specifically designed for fine-grained weed identification in complex field scenarios. CPD-WeedNet innovatively presents three core components: the CSP-MUIB backbone module, which enhances the discriminative ability of initial features at a low computational cost; the PFA neck module, which efficiently integrates shallow-layer details to improve the contour capture of small and medium-sized targets; and the DFS neck module, which utilizes the Transformer to enhance global context understanding and cope with large targets and complex occlusions. On a self-constructed soybean field weed dataset, CPD-WeedNet achieved 80.6% mAP50(Mask) and 85.3% mAP50(Box), with pixel-level mIoU and mAcc reaching 86.6% and 94.6% respectively, significantly outperforming mainstream YOLO baselines. On the public Fine24 dataset, CPD-WeedNet attained 75.4% mIoU, 81.7% mAcc, and 65.9% mAP50 (Mask), demonstrating an excellent balance between performance and efficiency. The proposed CPD-WeedNet achieves an excellent balance between performance and efficiency, demonstrating its significant potential as a key vision technology for the development of low-cost, real-time intelligent weeding systems. This research is of great significance for promoting precision agriculture.

Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model’s sensitivity towards different classes and improve the model’s ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability.

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on farmland. In this paper, we propose a semantic segmentation method based on a cascaded encoder-decoder network, namely CED-Net, to differentiate weeds from crops. The existing architectures for weeds and crops segmentation are quite deep, with millions of parameters that require longer training time. To overcome such limitations, we propose an idea of training small networks in cascade to obtain coarse-to-fine predictions, which are then combined to produce the final results. Evaluation of the proposed network and comparison with other state-of-the-art networks are conducted using four publicly available datasets: rice seeding and weed dataset, BoniRob dataset, carrot crop vs. weed dataset, and a paddy–millet dataset. The experimental results and their comparisons proclaim that the proposed network outperforms state-of-the-art architectures, such as U-Net, SegNet, FCN-8s, and DeepLabv3, over intersection over union (IoU), F1-score, sensitivity, true detection rate, and average precision comparison metrics by utilizing only (1/5.74 × U-Net), (1/5.77 × SegNet), (1/3.04 × FCN-8s), and (1/3.24 × DeepLabv3) fractions of total parameters.

Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment.

Remote sensing image segmentation holds significant application value in precision agriculture, environmental monitoring, and other fields. However, in the task of fine-grained segmentation of weeds and crops, traditional deep learning methods often fail to balance global semantic information with local detail features, resulting in over-segmentation or under-segmentation issues. To address this challenge, this paper proposes a segmentation model based on a dual-branch Transformer architecture—DBFormer—to enhance the accuracy of weed detection in remote sensing images. This approach integrates the following techniques: (1) a dynamic context aggregation branch (DCA-Branch) with adaptive downsampling attention to model long-range dependencies and suppress background noise, and (2) a local detail enhancement branch (LDE-Branch) leveraging depthwise-separable convolutions with residual refinement to preserve and sharpen small weed edges. An Edge-Aware Loss module further reinforces boundary clarity. On the Tobacco Dataset, DBFormer achieves an mIoU of 86.48%, outperforming the best baseline by 3.83%; on the Sunflower Dataset, it reaches 85.49% mIoU, a 4.43% absolute gain. These results demonstrate that our dual-branch synergy effectively resolves the global–local conflict, delivering superior accuracy and stability in the context of practical agricultural applications.

Weed control is a global issue of great concern, and smart weeding robots equipped with advanced vision algorithms can perform efficient and precise weed control. Furthermore, the application of smart weeding robots has great potential for building environmentally friendly agriculture and saving human and material resources. However, most networks used in intelligent weeding robots tend to solely prioritize enhancing segmentation accuracy, disregarding the hardware constraints of embedded devices. Moreover, generalized lightweight networks are unsuitable for crop and weed segmentation tasks. Therefore, we propose an Attention-aided lightweight network for crop and weed semantic segmentation. The proposed network has a parameter count of 0.11M, Floating-point Operations count of 0.24G. Our network is based on an encoder and decoder structure, incorporating attention module to ensures both fast inference speed and accurate segmentation while utilizing fewer hardware resources. The dual attention block is employed to explore the potential relationships within the dataset, providing powerful regularization and enhancing the generalization ability of the attention mechanism, it also facilitates information integration between channels. To enhance the local and global semantic information acquisition and interaction, we utilize the refinement dilated conv block instead of 2D convolution within the deep network. This substitution effectively reduces the number and complexity of network parameters and improves the computation rate. To preserve spatial information, we introduce the spatial connectivity attention block. This block not only acquires more precise spatial information but also utilizes shared weight convolution to handle multi-stage feature maps, thereby further reducing network complexity. The segmentation performance of the proposed network is evaluated on three publicly available datasets: the BoniRob dataset, the Rice Seeding dataset, and the WeedMap dataset. Additionally, we measure the inference time and Frame Per Second on the NVIDIA Jetson Xavier NX embedded system, the results are 18.14 msec and 55.1 FPS. Experimental results demonstrate that our network maintains better inference speed on resource-constrained embedded systems and has competitive segmentation performance.

Weed segmentation is a fundamental task in precision agriculture, essential for targeted intervention and sustainable farming. However, achieving accurate segmentation remains challenging due to the high visual similarity between weeds and crops, as well as the ambiguous, fine-grained boundaries often present in complex field environments. To address this, we present WS-DINO, a novel weed segmentation network built upon the DINOv2 vision foundation model. Our framework introduces two key innovations: (1) a Feature Prior Module that leverages a Canny-guided refinement process to extract and inject fine-grained cues related to weed texture, morphology, and boundaries into specific blocks of the Vision Transformer; and (2) a Spatial Feature Fusion Module that leverages convolutional layers to generate multi-scale spatial features, which are then fused with the semantically rich token features from DINOv2, effectively compensating for the Transformer’s limitations in capturing local spatial details. Comprehensive evaluation on the public PhenoBench dataset shows that WS-DINO achieves an mIoU of 88.67% and outperforms the evaluated benchmark methods. Moreover, on the challenging MotionBlurred dataset, WS-DINO reaches 88.75% mIoU, showing stable performance under motion blur and degraded visual conditions.

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8 MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, and is suitable for complex vegetable field environments and resource-limited laser weeding devices.