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Crop recognition under weedy conditions based on 3D imaging for robotic weed control
- Source :
- Journal of Field Robotics. 35:596-611
- Publication Year :
- 2017
- Publisher :
- Wiley, 2017.
-
Abstract
- A 3D time-of-flight camera was applied to develop a crop plant recognition system for broccoli and green bean plants under weedy conditions. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. An efficient noise filter was developed to remove the sparse noise points in 3D point cloud space. Both 2D and 3D features including the gradient of amplitude and depth image, surface curvature, amplitude percentile index, normal direction, and neighbor point count in 3D space were extracted and found effective for recognizing these two types of plants. Separate segmentation algorithms were developed for each of the broccoli and green bean plant in accordance with their 3D geometry and 2D amplitude characteristics. Under the experimental condition where the crops were heavily infested by various types of weed plants, detection rates over 88.3% and 91.2% were achieved for broccoli and green bean plant leaves, respectively. Additionally, the crop plants were segmented out with nearly complete shape. Moreover, the algorithms were computationally optimized, resulting in an image processing speed of over 30 frames per second.
- Subjects :
- 0209 industrial biotechnology
Noise reduction
Point cloud
Image processing
04 agricultural and veterinary sciences
02 engineering and technology
Weed control
Computer Science Applications
Noise
020901 industrial engineering & automation
Agronomy
Control and Systems Engineering
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Point (geometry)
Weed
Biological system
Normal
Mathematics
Subjects
Details
- ISSN :
- 15564959
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Journal of Field Robotics
- Accession number :
- edsair.doi...........e2787650d0b6123b841f5036ce068db1