Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass

Pongpiyapaiboon, Sorawich; Tanaka, Hidenori; Hashiguchi, Masatsugu; Hashiguchi, Takuyu; Hayashi, Atsushi; Tanabata, Takanari; Isobe, Sachiko; Akashi, Ryo

doi:10.1002/ppj2.20076

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Pongplyapaiboon et al have installed a 3D modelling device utilising our principle in an inspection line of a plant factory focusing on zoysiagrass and are conducting introductory trials. The zoysiagrass measurement algorithm used in their system differs from ours [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling—A Case Study in Leaf Lettuce

Wada,

Hayashi,

Lee

et al. 2023

Sensors

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Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.

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Section: Discussionmentioning

confidence: 99%

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling—A Case Study in Leaf Lettuce

Wada,

Hayashi,

Lee

et al. 2023

Sensors

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Techniques for Canopy to Organ Level Plant Feature Extraction via Remote and Proximal Sensing: A Survey and Experiments

Nethala,

Um,

Vemula

et al. 2024

Remote Sensing

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This paper presents an extensive review of techniques for plant feature extraction and segmentation, addressing the growing need for efficient plant phenotyping, which is increasingly recognized as a critical application for remote sensing in agriculture. As understanding and quantifying plant structures become essential for advancing precision agriculture and crop management, this survey explores a range of methodologies, both traditional and cutting-edge, for extracting features from plant images and point cloud data, as well as segmenting plant organs. The importance of accurate plant phenotyping in remote sensing is underscored, given its role in improving crop monitoring, yield prediction, and stress detection. The review highlights the challenges posed by complex plant morphologies and data noise, evaluating the performance of various techniques and emphasizing their strengths and limitations. The insights from this survey offer valuable guidance for researchers and practitioners in plant phenotyping, advancing the fields of plant science and agriculture. The experimental section focuses on three key tasks: 3D point cloud generation, 2D image-based feature extraction, and 3D shape classification, feature extraction, and segmentation. Comparative results are presented using collected plant data and several publicly available datasets, along with insightful observations and inspiring directions for future research.

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Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse

Conley,

Hejl,

Serba

et al. 2024

Sensors

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Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

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Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass

Cited by 3 publications

References 34 publications

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling—A Case Study in Leaf Lettuce

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling—A Case Study in Leaf Lettuce

Techniques for Canopy to Organ Level Plant Feature Extraction via Remote and Proximal Sensing: A Survey and Experiments

Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse

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