GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton

Yu, Jiguo; Li, Changying; Robertson, Jon S.; Sun, Shangpeng; Xu, Rui; Paterson, Andrew H.

doi:10.1038/s41598-018-19142-2

Cited by 58 publications

(48 citation statements)

References 26 publications

(23 reference statements)

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“…From the obtained data, canopy height, canopy area and plant volume were estimated [57]. In yet another study, a phenotyping vehicle called "GPhenoVision" was developed using a high-clearance tractor, and mounted with a stereo RGB camera, thermal camera, hyperspectral camera and an RTK-GPS [58]. It was tested in a cotton breeding trial to estimate canopy growth and development.…”

Section: Proximal Phenotypingmentioning

confidence: 99%

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

Chawade

Ham

Blomquist³

et al. 2019

Agronomy

174

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High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

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Section: Proximal Phenotypingmentioning

confidence: 99%

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

Chawade

Ham

Blomquist³

et al. 2019

Agronomy

174

View full text Add to dashboard Cite

show abstract

“…Furthermore, a group of colorized 3D images of soybean plants were divided into fifteen single areas for 15 pots automatically according to spatial information. Finally, the reconstructed 3D plant points were used to calculate the following three phenotypic traits for each soybean plant: (1) the plant height (H) was the shortest distance from the upper boundary of the main photosynthetic tissues (excluding inflorescences) of a plant to the ground level [40]; (2) the canopy breadth, which consisted of two aspects: the width across-row (WAR) distance is the distance along the x-axis, while the width in-row (WIR) distance is the distance along the y-axis [30]; and (3) the color indices in the HSI color space.…”

Section: Overall Process Flow For Calculating Phenotypic Traitsmentioning

confidence: 99%

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Zhu

Guan

et al. 2019

Remote Sensing

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Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

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“…Recently, several studies have successfully demonstrated that the use of information technology to monitor key indicators could replace the visual decision-making of expert farmers and prevent the loss of sophisticated cultivation techniques [1][2][3]. In these studies, data mining, image processing, and machine learning technologies were applied to plant images and environmental data in order to determine and extract indicator variables for such decision-making.…”

Section: Introductionmentioning

confidence: 99%

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

Wakamori

Mineno

2019

Plant Phenomics

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The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers. Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index, calculated from leaf wilting and environmental data. However, these studies have not addressed the specific role of leaf wilting in the estimation. Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method. In this paper, we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network. To clarify the role of leaf wilting, this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting, quantified by optical flow, and stem diameter variations as a water stress index. The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations, which were water stress responses in plants. As the results were consistent with known plant water transport mechanisms, it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations.

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GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton

Cited by 58 publications

References 26 publications

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

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