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An Approach to the Use of Depth Cameras for Weed Volume Estimation
- Source :
- Sensors (Basel, Switzerland), Sensors; Volume 16; Issue 7; Pages: 972, Digital.CSIC. Repositorio Institucional del CSIC, instname, Sensors, Vol 16, Iss 7, p 972 (2016)
- Publication Year :
- 2016
- Publisher :
- MDPI, 2016.
-
Abstract
- The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.<br />The Spanish Ministry of Economy and Competitiveness has provided support for this research via projects AGL2014-52465-C4-3-R and AGL2014-52465-C4-1-R, and Bosch Foundation. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).
- Subjects :
- weed detection
Point cloud
Kinect v2
Biomass
lcsh:Chemical technology
maize
01 natural sciences
Biochemistry
Article
Analytical Chemistry
lcsh:TP1-1185
Segmentation
Electrical and Electronic Engineering
Instrumentation
Remote sensing
Mathematics
weed/crop structure characterization
plant volume estimation
business.industry
010401 analytical chemistry
Volume (computing)
04 agricultural and veterinary sciences
Automation
Atomic and Molecular Physics, and Optics
0104 chemical sciences
Agronomy
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
RGB color model
Precision agriculture
business
Weed
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 16
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....d59ce6ea0bcf1f8f5555eb6f16878adc