Vegetable crops grown in the field are particularly vulnerable to competition from weeds at the seedling stage. To avoid yield loss, weeds must be removed early in the crop cycle. Shortages of farm laborers for hand weeding and lack of effective vegetable herbicides is stimulating the development of intelligent weeders. One challenge for development of effective intelligent weeders is the precision differentiation of vegetable crops and weeds. Crop signaling is a technique that enables rapid and accurate identification of target crops. In this study, an appropriate dose of fluorescent compounds allowed the vegetable crop locations in the field to be reliably detectable by smart machines. The study examined protocols for crop root treatment, computer vision system development, crop signal detection and biomass evaluation. Rhodamine B (Rh-B) is a fluorescent compound with unique optical properties. Different doses of Rh-B were applied to the celery roots for various durations prior to transplantation to assess Rh-B transport in the plant, its photostability and potential impact on seedling growth in the natural outdoor environment of a farm. Systemic Rh-B absorbed via the roots moved throughout the celery plant in 24 h. Compared with 60 ppm solution of Rh-B, higher doses of Rh-B including 90, 180, and 270 ppm resulted in greater absorption by the plant, but such three concentrations injured the plant. The biomass test results showed that the treatment of celery roots for two days with a 60 ppm solution of Rh-B was safe for celery plants. This Rh-B dosage had good photostability in celery seedlings for about 5 weeks after transplanting. An effective dose of systemic Rh-B allowed for the rapid identification of celery plants at early growth stages, thereby facilitating the automatic differentiation of weeds and crops by a robotic machine vision system.
Su, W-H., Slaughter, D. C., & Fennimore, S. A. (2020). Non-destructive evaluation of photostability of crop signaling compounds and dose effects on celery vigor for precision plant identification using computer vision. Computers and Electronics in Agriculture, 1-8. https://doi.org/10.1016/j.compag.2019.105155