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A significant reduction in the amount of pesticides applied in agricultural and biological systems could be achieved using spot spray technology. To accomplish this, advanced plant sensor systems must be developed that can accurately locate and identify weeds from crop plants in the field. Currently, both public and commercial efforts have concentrated on single element optical sensors based on key reflective elements of the plant and soil system. Machine vision or image analysis is being investigated as another possible tool in plant sensing. It may provide valuable optical design information for less expensive single-element sensors. Moreover, shape features and textural analysis already provides simple broadleaf-grass classification based on staged plant images. These have not been thoroughly field tested. Another approach is to test image analysis algorithms, using three-dimensional rendering of weed and plant canopy architecture under complex lighting regimes. What was essentially done was to extract plant shape and textural information, along with essential physiological data from actual photographic images and then reassemble them as a virtual plant in the computer. A dissection program was written in C and efficiently extracts and stores irregular leaf shape and texture data. A canopy architecture program was written in C and Media Cybernetics HALOR graphics routines under DOS Expanded Memory on a personal computer. The plant simulation model consists of a three dimensional space where simulated light rays are generated as diffuse or speculative illumination. Plant surfaces are simulated with actual textural maps. The virtual plant is then manipulated to generate images that would be seen with machine vision. Computer simulated weed images were used to generate and test different fields of view sizes for evaluating how single element optical sensors would respond to composite leaf-soil reflectance.
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