Automated weed management tools in vegetable crops are needed to reduce or eliminate hand-weeding because of labour shortages and cost. Distinguishing crop plants from weeds in complex natural scenes of crop-weed mixtures remains a challenge for weed management automation. This paper presents a novel solution to the weed control problem by employing crop signalling technology: a novel systems approach that creates a machine-readable crop plant. A robot-vision-based weed-knife control system with a novel three-dimensional geometric detection algorithm was developed to automate weed control for tomato and lettuce crops. The system successfully detected the crop signal from occluded crop plants while traveling at speeds up to of 3.2 km h−1. The in-field experiments show that the system is able to reduce the number of weed plants by 83% in the seedling area. Crop detection accuracy was measured at 97.8% (precision 0.998 and recall 0.952) with a detection time of 30 ms f−1. This paper also shows that the crop signalling system has the advantage that prior knowledge of visual features of each crop and weed species is not required and poor visual appearance of the crop plants or weeds does not affect system performance.

中文翻译：

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基于植物标签几何外观的番茄生菜机器人实时除草刀控制系统

由于劳动力短缺和成本高，需要蔬菜作物中的自动化杂草管理工具来减少或消除人工除草。在作物 - 杂草混合物的复杂自然场景中区分作物植物和杂草仍然是杂草管理自动化的挑战。本文提出了一种通过采用作物信号技术来解决杂草控制问题的新解决方案：一种创建机器可读作物的新型系统方法。开发了一种基于机器人视觉的杂草刀控制系统，具有新颖的三维几何检测算法，用于自动控制番茄和生菜作物的杂草。该系统在以高达 3.2 km h-1 的速度行驶时成功检测到来自被遮挡作物的作物信号。田间试验表明，该系统能够将苗区杂草植物的数量减少83%。作物检测准确度为 97.8%（精度 0.998 和召回率 0.952），检测时间为 30 ms f-1。本文还表明，作物信号系统的优点是不需要每种作物和杂草种类的视觉特征的先验知识，作物或杂草的视觉外观不佳不会影响系统性能。