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Sensors 2016, 16(7), 972; doi:10.3390/s16070972

An Approach to the Use of Depth Cameras for Weed Volume Estimation

1
Center for Automation and Robotics, Spanish National Research Council, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
2
Institute of Agricultural Sciences, Spanish National Research Council, CSIC, Madrid 28006, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 4 May 2016 / Revised: 12 June 2016 / Accepted: 22 June 2016 / Published: 25 June 2016
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2015)
View Full-Text   |   Download PDF [2871 KB, uploaded 25 June 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. View Full-Text
Keywords: Kinect v2; weed/crop structure characterization; weed detection; plant volume estimation; maize Kinect v2; weed/crop structure characterization; weed detection; plant volume estimation; maize
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Andújar, D.; Dorado, J.; Fernández-Quintanilla, C.; Ribeiro, A. An Approach to the Use of Depth Cameras for Weed Volume Estimation. Sensors 2016, 16, 972.

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