Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera Photography System

Yu, Zhitong; Teng, Poching; Shimizu, Y.; Hosoi, Fumiki; Omasa, Kenji

doi:10.3390/s16060874

Cited by 49 publications

(42 citation statements)

References 56 publications

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“…Leemans et al [51] used stereoscopic images to build a model with distance information, LAI was segmented and calculated in the field. Multi-camera can consist a multi-view stereo vision system, Zhang et al [52] combined four images taken at different positions with SFM algorithm to reconstruct paprika plant in 3D and accurately estimated leaf lengths and widths, which can be used to measure LA [53,54] .…”

Section: Leaf Parameters Measurement Using Color Digital Cameras and mentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

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Section: Leaf Parameters Measurement Using Color Digital Cameras and mentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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show abstract

“…Thus, the application of 3D imaging technologies has gradually been a long-time challenging study topic. In recent decades, 3D imaging technologies are being widely applied in plant science and modern agriculture Snavely et al, 2006;Omasa et al, 2007;Kazmi et al, 2014;Zhang et al, 2016 . With the development of 3D imaging and the improvement of sensor accuracy, this research area is now receiving more attention than ever before. 3D imaging techniques include contact and non-contact methods.…”

Section: Introductionmentioning

confidence: 99%

3D monitoring for plant growth parameters in field with a single camera by multi-view approach

Zhang

Teng

Aono

et al. 2018

J. Agric. Meteorol.

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In recent decades, some photogrammetric methods for 3D monitoring of plant growth and structure parameters have been studied by the automated feature extraction and matching. In this study on growth analysis of sweet potato Ipomoea batatas L. plants at different fertilizer conditions, we proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion SfM method. Also, we handled effectively the noise problem by minimizing re-projection errors. The results of 3D models demonstrated that the average percentage error was a constant about 4.8 for plant height or decreased from 13 to 8 leaf area index, LAI = 3.5 for leaf number and from 19 to 12 LAI = 3.5 for leaf area with increasing in LAI, although each percentage error fluctuated, especially at low LAI. In contrast, the average percentage error in 2D image processing was 20 to 45 for leaf number and 60 to 90 for leaf area, and the leaf height was immeasurable. Comparing with the errors of 3D results, the errors of 2D images were much larger because 2D imaging had some problems, such as not being robust against occlusion of plant organs, and the ambiguity between object size and distance from the camera. On the other hand, we examined a method to calibrate the estimates from 3D models using the regression model between the measured value and the value estimated from 3D model. The regression models showed the linear and good estimation for leaf height R 2 = 0.97 and RMSE = 0.71 cm , leaf number R 2 = 0.99 and RMSE = 4.03 and leaf area R 2 = 0.98 and RMSE = 0.12 m 2 , in spite of use of data across a wide range in fertilizer supply and growth stage of sweet potato plants.The results demonstrated that 3D imaging technique in the study has the potential to remotely monitor plant growth status and estimate growth and structure parameters at various environmental factors outdoors.

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“…This makes perception with common sensors and algorithms a challenging task (Zhang et al, 2016). Sunlight or shade strongly affects the sensor outputs (Bechar and Vigneault 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Also stem detection in point clouds was performed with different sensor types like stereo cameras, light curtains and LiDAR data (Bac et al, 2014;Garrido et al, 2014;Reitberger et al, 2007). Analyzing the plant height with 3D sensor data is a well-known research topic for plant phenotyping, performed with a large variety of sensors (Zhang et al, 2016). Today's variable-rate applications are performed using map-based or sensor based approaches.…”

Section: Introductionmentioning

confidence: 99%