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February 1, 2000 MACHINE VISION IDENTIFICATION OF TOMATO SEEDLINGS FOR AUTOMATED WEED CONTROL L. Tian MEMBER ASAE D. C. Slaughter MEMBER ASAE R. F. Norris ABSTRACT A machine vision system to detect and locate tomato seedlings and weed plants in a commercial agricultural environment was developed and tested. Images acquired in agricultural tomato fields under natural illumination were studied extensively, and an environmentally adaptive image segmentation algorithm was developed to improve machine recognition of plants under these conditions. The system was able to identify the majority of non-occluded target plant cotyledons, and to locate plant centers even when the plant was partially occluded. Of all the individual target crop plants 65% to 78% were correctly identified and less than 5% of the weeds were incorrectly identified as crop plants. Keywords. Machine vision, pattern recognition, tomato, weeds. INTRODUCTION Agricultural production experienced a revolution in mechanization over the past century. However, due to the working environment, plant characteristics, or costs, there are still tasks which have remained largely untouched by the revolution. Hand laborers in 1990s still may have to perform tedious field operations that have not changed for centuries. Identification of individual crop plants in the field and locating their exact position is one of the most important tasks needed to further automate farming. Only with the technology to locate individual plants, can smart field machinery be developed to automatically and precisely perform treatments such as weeding, thinning, and chemical application. Early studies of machine vision systems for outdoor field applications concentrated mainly on robotic fruit harvesting. Parrish and Goksel (1977) first studied the use of machine vision for fruit harvesting in 1977. In France, a vision system was developed at the CEMAGREF center to pick apples (Grand dEsnon et al., 1987). Slaughter and Harrel (1989) developed a machine vision system that successfully picked oranges in the grove. Fruits generally have regular shapes and are often distinguishable by their unique color when compared to the color of the background foliage. Less work has been done on outdoor plant identification. Jia et al. (1990) investigated the use of machine vision to locate corn plants by finding the main leaf vein from a top view. Unfortunately this technique is not applicable to most dicot row crops. A group of researchers at the University of California at Davis have developed a machine L. Tian, D.C. Slaughter, 2). The EDP is the closest to the CEN of ITC; 3). The PXC is between 60 % to 130% of PXC of ITC; 4). The angle () between MJXs is the smallest and not greater than 20 degrees, as shown in figure 2. Rule 7. If there is no cotyledon within the near neighborhood but a possible partially occluded tomato cotyledon (OTC) exits, the one to be paired with the ITC has to have the following characteristics: 1). The occluded cotyledon to be paired is the one with a PXC bigger than that of ITC, and located near the EDP and within an angle 80 degrees as shown in in figure 3, 2). The maximum distance, D in figure 3, between the two boundary intersection points on the radial line from the nearer end of ITC is greater than 80% of the MJX of ITC. 20 MJX degree MJX ITC ATC EDP CEN D 80 degrees M