Robotic

Robotic Weed Control System for Tomatoes
Won Suk LeeBiological & Ag. Engineering, UC Davis
Introduction
A weed can be thought of as any plant growing in the wrong place at the wrong time and doing more harm than good. Weeds compete with the crop for water, light, nutrients and space, and therefore reduce crop yields and also affect the efficient use of machinery (Parish, 1990). Many methods are used for weed control. Among them, mechanical cultivation is commonly practiced in many vegetable crops to remove weeds, aerate soil, and improve irrigation efficiency, but this technique cannot selectively remove weeds located in the seedline between crop plants. The most widely used method for weed control is to use agricultural chemicals (herbicides and fertilizer products). In fact, the success of U.S. agriculture is attributable to the effective use of chemicals. For example, the total of 5.9 million kg of agricultural chemicals (herbicides, insecticides, fungicides, and other chemicals were used to produce processing tomatoes in California alone in 1994 (USDA, NASS and ERS, 1995). This heavy reliance on chemicals raises many environmental and economic concerns, causing many farmers to seek alternatives for weed control in order to reduce chemical use in farming. For some crop/weed situations there are no selective herbicides. Selective herbicides selectively kill only weeds and not crop plants, thus they are important for weed control.
Since hand labor is costly, an automated weed control system may be economically feasible. A real-time precision robotic weed control system could also reduce or eliminate the need for chemicals. Although there have been many efforts to control in-row weeds, no system is currently available for real-time field use. In this research, an intelligent real-time robotic weed control system has been developed to identify and locate outdoor plants for selective spraying of in-row weeds using an environmentally sound and friendly chemical application system based upon machine vision technology, pattern recognition techniques, knowledge-based decision theory, and robotics.
Tomatoes are one of the leading vegetable crops produced in California. In 1996, over 9 billion kg of processing tomatoes were produced in California, accounting for 93% of all processing tomatoes produced in the U.S. (USDA and NASS, 1997). However, the current in-row weed control method is highly dependent on labor-intensive and costly hand hoeing. A significant amount of manual work is still required for weed control in crop rows, which hopefully can be automated with today's rapidly growing state of the art computer technologies.