A novel technique for enabling robotic weed control in a commercial processing tomato field having densely populated weeds is described. It is necessary to accurately locate the stem emerging points (SEPs) of crop plants for the successful application of a mechanical weeding actuator to remove weeds during automated weeding. However, it is a difficult and challenging task to locate the SEPs in complex natural scenarios such as when the main stem is occluded by weeds or crop foliage, the crop plants are lying on the soil surface, there are non-uniform planting bed conditions, or there is leaf damage due to insects etc. To overcome these challenges a novel crop signalling concept has been proposed to mark the crop plants at planting to make them machine-readable. Plants lacking this crop signal were classified as weeds and removed by the robotic weed knife actuator. A machine-vision algorithm was developed to analyse the seven views of the crop plants taken by camera with help of a specially designed imaging chamber and locate the SEPs of tomato plants, which was passed to the robotic weed knife control algorithm to remove weeds. The algorithm was successfully detected and located the main stems of tomato plants in outdoor environment with success rate of 99.19% while traveling at a speed of 3.2 km h−1 with a processing time for all views of 30 ms f−1.

中文翻译：

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RTD-SEPs：实时检测茎出现点和作物杂草分类，用于番茄生产中的机器人杂草控制

描述了一种在杂草密集的商业加工番茄田中实现机器人杂草控制的新技术。为了在自动除草过程中成功应用机械除草执行器去除杂草，必须准确定位作物的茎出现点 (SEP)。然而，在复杂的自然场景中定位 SEPs 是一项艰巨且具有挑战性的任务，例如当主茎被杂草或作物叶子遮挡时，作物位于土壤表面，存在不均匀的种植床条件，或者由于昆虫等导致叶片损坏。为了克服这些挑战，已经提出了一种新的作物信号概念，用于在种植时标记作物植物，使其机器可读。缺乏这种作物信号的植物被归类为杂草，并由机器人除草刀执行器清除。开发了一种机器视觉算法，在专门设计的成像室的帮助下分析相机拍摄的作物植物的七个视图，并定位番茄植物的 SEP，并将其传递给机器人除草刀控制算法以去除杂草。该算法在室外环境下成功检测并定位番茄植株的主茎，成功率为 99.19%，同时以 3.2 km h-1 的速度行驶，所有视图的处理时间为 30 ms f-1。它被传递给机器人除草刀控制算法以去除杂草。该算法在室外环境下成功检测并定位番茄植株的主茎，成功率为 99.19%，同时以 3.2 km h-1 的速度行驶，所有视图的处理时间为 30 ms f-1。它被传递给机器人除草刀控制算法以去除杂草。该算法在室外环境下成功检测并定位番茄植株的主茎，成功率为 99.19%，同时以 3.2 km h-1 的速度行驶，所有视图的处理时间为 30 ms f-1。