Intra-row weeds have a significant impact on crop yield and health, competing with crops for nutrients, water, and sunlight. Traditional uniform herbicide applications in weed management not only risk harming the environment but can also compromise crop health. Precision spraying technology, guided by machine vision, offers a solution by accurately identifying and targeting weeds, thereby reducing overall herbicide use. Inter-row weed segmentation can be done easily with simple thresholding, but intra-row region weed cannot be segmented with simple thresholding due to many similarities between the intra-row weeds and plants. So, in this study, a novel methodology is introduced to dynamically estimate intra-row weed density for the entire crop row of chilli by integrating ByteTrack Simple Online and Real Time Tracker (BTSORT) with YOLOv7 crop recognition model to track the plant and Hue-Saturation-Value (HSV) color model with simple thresholding to segment weeds between tracks to avoid repetitive intra-row weed density estimation. The weed density between the plants in these regions is calculated and categorized into low, medium, and high levels based on the number of weed pixels in the intra-row region. The YOLOv7 Crop recognition model recognized the chilli plants with achieved a precision of 0.92 and a recall of 0.94 at 47.39 FPS. The BTSORT with YOLOv7 crop recognition model on a test video dataset performed well with MOTA and MOTP of 0.85 and 0.81, respectively. The developed dynamic intra-row weed density estimation method classifies it with an overall accuracy of 0.87. Additionally, the system processed 1280x720 frames 1.38 times faster than 1920x1080 frames, enabling efficient real-time intra-row weed density estimation across full crop rows.

中文翻译：

---

使用集成的 CRM、BTSORT 和 HSV 模型，在辣椒作物冠层的整个视频流中快速估计行内杂草密度


行内杂草对作物产量和健康有重大影响，与作物争夺养分、水分和阳光。杂草管理中传统的统一除草剂应用不仅有危害环境的风险，还会损害作物健康。由机器视觉引导的精确喷洒技术通过准确识别和定位杂草来提供解决方案，从而减少除草剂的整体使用。行间杂草分割可以通过简单的阈值轻松完成，但由于行内杂草和植物之间有许多相似之处，因此不能用简单的阈值对行内杂草进行分割。因此，在这项研究中，引入了一种新的方法，通过将 ByteTrack 简单在线和实时跟踪器 （BTSORT） 与 YOLOv7 作物识别模型集成来动态估计整个辣椒作物行的行内杂草密度，以跟踪植物和色调饱和度值 （HSV） 颜色模型，通过简单的阈值来分割轨道之间的杂草，以避免重复的行内杂草密度估计。计算这些区域中植物之间的杂草密度，并根据行内区域中的杂草像素数分为低、中和高水平。YOLOv7 作物识别模型以 0.92 的精度识别辣椒植株，以 47.39 FPS 的速度实现 0.94 的召回率。在测试视频数据集上，带有 YOLOv7 作物识别模型的 BTSORT 在 MOTA 和 MOTP 分别为 0.85 和 0.81 的情况下表现良好。开发的动态行内杂草密度估计方法对其进行分类，总体准确率为 0.87。此外，该系统处理 1280x720 帧的速度比 1920x1080 帧快 1.38 倍，从而能够对整个作物行进行高效的实时行内杂草密度估计。