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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 1990 s 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 d Esnon 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 MJX ITC L Tian D C Slaughter b detail of camera mountings The toolbar was controlled to follow the seed line in the field L Tian D C Slaughter x other plant leaves