Jinglin Lei

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.

BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot‐spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasibility of implementing herbicide susceptibility‐based weed mapping using deep convolutional neural networks (DCNNs) to facilitate targeted and efficient herbicide applications. Additionally, applying path‐planning algorithms to weed mapping data to guide the spraying nozzle ensures minimal travel paths for herbicide application.RESULTS: DenseNet achieved high precision, recall, overall accuracy, and F₁ score values for all categories of herbicides and no herbicides, with F₁ scores ranging from 0.996 to 0.999 in the validation dataset and from 0.992 to 0.997 in the testing dataset. The average accuracies attained by DenseNet, GoogLeNet and ResNet were 0.9985, 0.9953 and 0.9980, respectively. By considering both accuracy and computational efficiency, the ResNet model was identified as the most effective among the models compared to weed detection. The performance of the Christofides, Greedy and 2‐opt algorithms in optimizing path planning for single or dual spraying nozzles was compared and analyzed. The Greedy algorithm proved the most efficient in optimizing the nozzle's trajectory.CONCLUSION: Implementing herbicide susceptibility‐based weed mapping facilitates targeted herbicide application by directing the nozzle to the grid cells containing the weeds susceptible to the herbicides. Moreover, the strategic integration of herbicide susceptibility‐based weed mapping with optimized path planning for the spraying mechanism can be adeptly implemented on smart sprayers, which could effectively reduce the herbicide input. © 2025 Society of Chemical Industry.