CottonWeedControl

Information about CottonWeedControl

Published on January 24, 2008

Author: Cajetano

Source: authorstream.com

Content

Crop verses Weed Recognition Artificial neural networks: Neural Network Plant Recognition for Vision Based Robotic Weed control:  Crop verses Weed Recognition Artificial neural networks: Neural Network Plant Recognition for Vision Based Robotic Weed control Paper 01-3104 Session 95: Real-Time Image Applications ASAE 2001 International meeting August 1st, 9:35am Chris Gliever, Ph.D. student ([email protected]) David C Slaughter, Professor at UC Davis Dept. of Biological & Agricultural Engineering Slide2:  Introduction Weed control during first 6 weeks greatly increase yield $300 to produce one-hectare of cotton (13% weed control) No weed control average decrease in yield is 25% Main limiting resources: light, water & nutrients Therefore seedline weed control most critical to yield Courtesy of Ross Lamm Slide3:  Courtesy of Ross Lamm Slide4:  Weed sprayer developed by Won Suk Lee (1998) 1. Capture top view image of seedline. 2. Analyze image into crop/weed classes. 3. Make spray map 4. Apply herbicide only to weed foliage when micro-sprayer passes over leaves. Slide5:  ZISC Radial Basis Function (RBF) ANN structure Slide6:  Objectives Evaluate low cost alternative weed/crop classifier engine by simulating ZISC hardware Maintain software compatibility with microcontroller and therefore generate a spray map Maintain hardware compatibility with micros-sprayer Obtain an weed hit accuracy above 80% Target a real-time solution that allows a minimum tractor speed of 2mph Evaluate lower cost 1 CCD camera to 3 CCD camera Slide7:  1 CCD verses 3 CCD Camera Slide8:  Building a label image for each training image Segmenting image into plant & background regions EG=(Green value-Blue value)+(Green value-Red value) Slide9:  Creating an Edge image from a binary image Laplace kernel Binary image Edge image Slide10:  Creating the ANN input feature vector Slide11:  Example training from label and raw images Slide12:  Example spray map and test result output of GUI Slide13:  Experimental Results from two test sets 92% overall accuracy 91% cotton missed 93% weeds hit Slide14:  Conclusion The evaluation of ZISC hardware simulation resulted in a 92% overall recognition accuracy. This algorithm shows promise for real-time robotic weed control. Heavily damaged cotton leaves leads to crop misclassification. Weed clumps as large as crop leads to weed misclassification within the center, but leaf tips still get mapped for spray. Additional research into better feature vectors could ameliorate these potential sources of errors. The lower cost 1-CCD generated too much color distortion to be an effective imaging device.

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