Non-destructive and cost-effective 3D plant growth monitoring system in outdoor conditions

Paturkar, Abhipray; Gupta, Gourab Sen; Bailey, Donald G.

doi:10.1007/s11042-020-08854-1

Cited by 15 publications

(17 citation statements)

References 30 publications

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“…Martinez-Guantar et al (2019) used a regular point and shoot camera for the 3D reconstruction of maize, sunflower and sugar beet plants, with an R 2 > 0.99 for both height and leaf area estimates compared against ground truthing measurements. Paturkar et al (2020) show that even a mobile phone can be used for image capture, with 3D reconstructions of chilli plants giving an R 2 > 0.98 for estimates of both height and leaf area. These technological advances and reductions in cost mean that photogrammetric techniques are more accessible than ever before to the plant phenotyping community.…”

Section: Discussionmentioning

confidence: 99%

Open source 3D phenotyping of chickpea plant architecture across plant development

Salter

Shrestha

Barbour

2020

Preprint

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In this work, we developed a low-cost 3D scanner and used an open source data processing pipeline to phenotype the 3D structure of individual chickpea plants. Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 minutes for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass.

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Section: Discussionmentioning

confidence: 99%

Open source 3D phenotyping of chickpea plant architecture across plant development

Salter

Shrestha

Barbour

2020

Preprint

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“…Illumination Effects: Passive techniques, to be specific, SfM gives a highly accurate point cloud [151]. However, changes in the illumination while capturing the plant images can result in missing information such as blank patches in the 3D model of the plant surface and leaves.…”

Section: Discussionmentioning

confidence: 99%

“…A regression method is used to estimate correlation between the points in the 3D model and ground truth and high correlation was achieved with R 2 =0.81. Paturkar et al [151] determined the plant growth over a period of one month. Eight manual readings and images were captured twice a week over this period.…”

Section: Plant Growthmentioning

confidence: 99%

Making Use of 3D Models for Plant Physiognomic Analysis: A Review

2021

Self Cite

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Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field.

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“…Martinez-Guanter et al [ 29 ] used a regular point and shoot camera for the 3D reconstruction of maize, sunflower and sugar beet plants, with an R 2 > 0.99 for both height and leaf area estimates compared against ground truthing measurements. Paturkar et al [ 39 ] show that even a mobile phone can be used for image capture, with 3D reconstructions of chilli plants giving an R 2 > 0.98 for estimates of both height and leaf area. These technological advances and reductions in cost mean that photogrammetric techniques are more accessible than ever before to the plant phenotyping community.…”

Section: Discussionmentioning

confidence: 99%

Open source 3D phenotyping of chickpea plant architecture across plant development

2021

View full text Add to dashboard Cite

Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass. Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.

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Non-destructive and cost-effective 3D plant growth monitoring system in outdoor conditions

Cited by 15 publications

References 30 publications

Open source 3D phenotyping of chickpea plant architecture across plant development

Open source 3D phenotyping of chickpea plant architecture across plant development

Making Use of 3D Models for Plant Physiognomic Analysis: A Review

Open source 3D phenotyping of chickpea plant architecture across plant development

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