PlantStereo: A High Quality Stereo Matching Dataset for Plant Reconstruction

Qingyu Wang, Dihua Wu, Wei Liu, Mingzhao Lou, Huanyu Jiang, Yibin Ying and Mingchuan Zhou ()
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Qingyu Wang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Dihua Wu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Wei Liu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mingzhao Lou: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Huanyu Jiang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Yibin Ying: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mingchuan Zhou: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2023, vol. 13, issue 2, 1-18

Abstract: Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the E P E and b a d − 3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the E P E and b a d − 3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.

Keywords: stereo matching; dataset; deep learning; plant reconstruction; depth perception (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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