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Remote Sens. 2017, 9(5), 508; doi:10.3390/rs9050508

3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System

1
College of Information Science and Technology, Donghua University, Shanghai 201620, China
2
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä and Prasad S. Thenkabail
Received: 18 March 2017 / Revised: 16 May 2017 / Accepted: 17 May 2017 / Published: 21 May 2017

Abstract

Nowadays, 3D imaging of plants not only contributes to monitoring and managing plant growth, but is also becoming an essential part of high-throughput plant phenotyping. In this paper, an inexpensive (less than 70 USD) and portable platform with binocular stereo vision is established, which can be controlled by a laptop. In the stereo matching step, an efficient cost calculating measure—AD-Census—is integrated with the adaptive support-weight (ASW) approach to improve the ASW’s performance on real plant images. In the quantitative assessment, our stereo algorithm reaches an average error rate of 6.63% on the Middlebury datasets, which is lower than the error rates of the original ASW approach and several other popular algorithms. The imaging experiments using the proposed stereo system are carried out in three different environments including an indoor lab, an open field with grass, and a multi-span glass greenhouse. Six types of greenhouse plants are used in experiments; half of them are ornamentals and the others are greenhouse crops. The imaging accuracy of the proposed method at different baseline settings is investigated, and the results show that the optimal length of the baseline (distance between the two cameras of the stereo system) is around 80 mm for reaching a good trade-off between the depth accuracy and the mismatch rate for a plant that is placed within 1 m of the cameras. Error analysis from both theoretical and experimental sides show that for an object that is approximately 800 mm away from the stereo platform, the measured depth error of a single point is no higher than 5 mm, which is tolerable considering the dimensions of greenhouse plants. By applying disparity refinement, the proposed methodology generates dense and accurate point clouds of crops in different environments including an indoor lab, an outdoor field, and a greenhouse. Our approach also shows invariance against changing illumination in a real greenhouse, as well as the capability of recovering 3D surfaces of highlighted leaf regions. The method not only works on a binocular stereo system, but is also potentially applicable to a SFM-MVS (structure-from-motion and multiple-view stereo) system or any multi-view imaging system that uses stereo matching. View Full-Text
Keywords: 3D imaging; greenhouse plants; stereo matching; remote sensing; point cloud 3D imaging; greenhouse plants; stereo matching; remote sensing; point cloud
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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

Li, D.; Xu, L.; Tang, X.-S.; Sun, S.; Cai, X.; Zhang, P. 3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System. Remote Sens. 2017, 9, 508.

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