Vegetable crop productivity is susceptible to damage from weed competition, with early season weeds a control priority to prevent significant yield loss. There is an urgent need for a reliable robotic sensing system that can work well in a variety of crops to achieve universal weed/crop differentiation, which would facilitate further development in robotic technologies for farming and bring economic benefits to vegetable production. The aim of this study was to develop a novel technique to create a machine-readable crop plant using a systemic crop signalling compound applied to seeds or transplants. The protocols for the crop signalling method and its detection are described. Rhodamine B (Rh–B) was selected as the signalling compound in this study, because it could be used as a fluorescent tracer, had a unique optical appearance in plants, and had the necessary properties to allow systemic behaviour in vegetable seedlings. The Rh–B tracer was applied to snap bean and the systemic behaviour analysed with a fluorescent macroscope. The uptake of Rh–B varied among treatment methods. The Rh–B uptake through the seed coat of snap beans was found mainly in the seedling hypocotyls. The results for root uptake showed that Rh–B could be more readily transported to the whole plant through the root system as compared to the application to seeds. The midvein and secondary veins of bean leaves showed stronger Rh–B fluorescence than other regions of the leaf. Higher concentrations of Rh–B resulted in greater absorption by the plant. Although the crop signalling compound could follow both seed and root pathways for plant uptake, the uptake based on the root pathway had greater capacity than that of the seed pathway. The use of Rh–B provided a systemic crop signalling compound was discussed on further research and field tested to have application to enhance weed/crop differentiation by automated weeders in vegetable crops. The systemic crop signalling system successfully created a machine-readable signal on vegetable crops, and appeared to be non-destructive, cost effective, efficient and accurate for performing automatic plant care tasks.

中文翻译：

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开发用于蔬菜作物自动实时植物护理的系统作物信号系统

蔬菜作物的生产力很容易受到杂草竞争的损害，早季杂草是控制重点，以防止显着的产量损失。迫切需要一种可靠的机器人传感系统，它可以在多种作物中正常工作，以实现杂草/作物的普遍分化，这将促进农业机器人技术的进一步发展，并为蔬菜生产带来经济效益。本研究的目的是开发一种新技术，使用应用于种子或移植的系统性作物信号化合物来创建机器可读的作物植物。描述了作物信号方法及其检测的协议。本研究选择罗丹明 B (Rh–B) 作为信号化合物，因为它可以用作荧光示踪剂，在植物中具有独特的光学外观，并具有允许蔬菜幼苗系统行为的必要特性。将 Rh-B 示踪剂应用于脆豆，并用荧光显微镜分析系统行为。Rh-B 的吸收因治疗方法而异。通过脆豆种皮吸收的 Rh-B 主要在幼苗下胚轴中发现。根吸收的结果表明，与应用于种子相比，Rh-B 可以更容易地通过根系运输到整个植物。豆叶的中脉和次脉显示出比叶片其他区域更强的 Rh-B 荧光。较高浓度的 Rh-B 导致植物吸收更多。尽管作物信号化合物可以遵循植物吸收的种子和根途径，基于根途径的吸收能力大于种子途径的吸收能力。Rh-B 的使用提供了一种系统性作物信号化合物，在进一步的研究和田间测试中进行了讨论，以用于通过自动除草机在蔬菜作物中增强杂草/作物分化。系统作物信号系统成功地在蔬菜作物上创建了一个机器可读的信号，并且对于执行自动植物护理任务似乎是非破坏性的、具有成本效益的、高效和准确的。