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Project description

Development of a Computer Vision System for Weed Detection and Control. (93CC023)
Program UC IPM competitive research grants program
Principal
investigators
D.C. Slaughter, Biological and Agricultural Engineering, UC Davis
P. Chen, Biological and Agricultural Engineering, UC Davis
R.G. Curley, Biological and Agricultural Engineering, UC Davis
R.F. Norris, Botany, UC Davis
Host/habitat Unspecified
Pest Unspecified Weeds
Discipline Weed Science
Beneficial
organism
Unspecified
Review
panel
Cultural Controls
Start year (duration)  1993 (Two Years)
Objectives Develop the basis for an intelligent weed removal machine by transferring and building upon recent advances in computer vision technology, knowledge-based decision theory, and robotics.

Develop a computer vision system that can distinguish between crop and weed plants in the seedline to allow automated control of weed plants located between crop plants.

Final report Both off-line and real-time computer vision systems were developed to automatically distinguish between tomato plants and weeds. Results indicate that computer recognition of plants is more accurate when no portion of the plant is hidden by other plants and that tomato plants at the cotyledon to first true leaf stage were easiest to identify because tomato plants at these stages were less likely to be hidden by other plants. Results from real-world images collected in commercial processing tomato fields indicate that the new computer vision algorithm developed for this study is capable of correctly locating and identifying the tomato seedlings between 60 and 70% of the time, while mistakenly identifying weeds as tomato plants less than 3% of the time.

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