Growth Measurement of a Community of Strawberries Using Three-Dimensional Sensor

Yamamoto, Satoshi; Hayashi, Shigehiko; Tsubota, Shogo

doi:10.2525/ecb.53.49

Cited by 8 publications

(3 citation statements)

References 6 publications

(4 reference statements)

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“…Yamamoto and co-workers [20] used a depth camera system placed in a stationary position while strawberry plants were placed on a movable planting bench underneath. In the present experiment, a 2D LiDAR laser scanner was mounted on a linear, electric conveyor to scan each individual strawberry plant with growing container from two sides in the fruit production environment.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner

et al. 2022

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Monitoring of plant vegetative growth can provide the basis for precise crop management. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants (‘Honeoye’ and ‘Malling Centenary’) 14–77 days after planting (DAP). Canopy geometrical variables, i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from 3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived parameters (R2 = 0.98, 0.90, 0.93, and 0.96 with number of points per plant, volume, height, and projected canopy area, respectively). However, the measuring uncertainty was high in the dense canopies. Particularly, the canopy volume estimation was adapted to the plant habitus to remove gaps and empty spaces in the canopy point cloud. The parametric values for maximum point to point distance (Dmax) = 0.15 cm and slice height (S) = 0.10 cm resulted in R² = 0.80 and RMSPE = 26.93 % for strawberry plant volume estimation considering actual volume measured by water displacement. The vertical volume profiling provided growth data for cultivars ‘Honeoye’ and ‘Malling Centenary’ being 51.36 cm³ at 77 DAP and 42.18 cm3 at 70 DAP, respectively. The results contribute an approach for estimating plant geometrical features and particularly strawberry canopy volume profile based on LiDAR point cloud for tracking plant growth.

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Section: Discussionmentioning

confidence: 99%

“…Recent studies on strawberry reported the application of an RGB-depth camera to quantify plant growth [19,20]. Guan et al [21] mounted RGB and infrared cameras on a tractor-driven platform, applying structure-from-motion technique to reconstruct the 3D point cloud of strawberries.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner

et al. 2022

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“…Li et al [29] adopted Kinect for side-view imaging of two varieties of tomato plants in consecutive growth periods; vivid 3D visualization of tomato plants was realized after analyzing the digitized tomato organ samples. Yamamoto et al [30] constructed a Kinect-based system for 3D measurement of a community of strawberries cultivated on a 1-meter-long bench in greenhouse. Schima et al [31] applied the Lytro LF, an innovative low-cost light-field camera to monitor plant growth dynamics and plant traits, showing that this device could be a tool for on-site remote sensing.…”

Section: Introductionmentioning

confidence: 99%

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

Tang

et al. 2017

Remote Sensing

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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.

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Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review

Gao

et al. 2020

Computers and Electronics in Agriculture

179

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Growth Measurement of a Community of Strawberries Using Three-Dimensional Sensor

Cited by 8 publications

References 6 publications

Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner

Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner

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

Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review

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