Automated Identification of Systemic Fluorescent Markers in Vegetable Seedling Leaves for Weed and Crop Differentiation

Crop and weed differentiation is a major limitation to automation of weed removal. Vegetable crops are very susceptible to damage from early season weed competition. Herbicides used in vegetable crops provide partial weed control, however weed management programs in vegetable crops generally cannot depend on herbicides alone. While some progress has been made towards the development of commercial robotic weed control machines, the majority of vegetable producers still depend upon hand weeding for weed control, a major cost component in the system, indicating that the existing cultivator (whether robotic or conventional) and herbicide technologies are not adequate for the task. The objective of this study was to establish an effective computer vision method to rapidly differentiate crops from weeds using a systemic marker, Rhodamine B, present in vegetable crop seedlings as a machine-readable identification trait. The seedlings were germinated first. After germination, the roots of these plants were irrigated with Rhodamine dye solution that was detectable in the crop leaves. A novel computer vision system was designed with a custom illumination system designed specifically to excite the fluorescence properties of Rhodamine B and to image them. Rhodamine B was selected for study because it can be used as a fluorescent tracer, has good systemic properties in plants, and is included on the USA EPA List 4B of inert pesticide ingredients “for which EPA has sufficient information to reasonably conclude that the current use pattern in pesticide products will not adversely affect public health or the environment.” and has been used as a tracer in drinking water and for many biological applications. Study results show that the system can detect and allow visualization of the Rhodamine dye internal to the crop system in crop leaves. The research demonstrates that a Crop Signaling approach, using Rhodamine B can be used by a computer vision system to automatically discriminate weeds from vegetable seedlings such as snap bean and tomato.