High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

Baek, Jeongho; Lee, Eun‐Gyeong; Kim, Nyunhee; Kim, Song Lim; Choi, Inchan; Ji, Hyeonso; Chung, Yong‐Suk; Choi, Mansoo; Moon, Jung-Kyung; Kim, Kyung-Hwan

doi:10.3390/s20010248

Cited by 35 publications

(27 citation statements)

References 20 publications

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“…For draft Russian almond, the external features of the entire fruit and seed phenotypes inside the fruit endocarp were measured by using the 3DPheno-Seed&Fruit software. Compared to 2D imaging methods ( Tanabata et al, 2012 ; Evgenii et al, 2017 ; Baek et al, 2020 ), our methods quantify 3D morphological traits, such as the surface area, volume, and sphericity. Compared to the other 3D image analysis pipelines designed for grain phenotyping ( Glidewell, 2006 ; Hughes et al, 2017 ; Xiong et al, 2019 ; Hu et al, 2020 ; Li et al, 2020 ), our methods provide an additional function of non-destructively measuring morphological phenotypes of seed and fruit internal compartments.…”

Section: Discussionmentioning

confidence: 99%

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High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

Liu

Jin³

et al. 2020

Front. Plant Sci.

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Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface design and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistachio nut and dwarf Russian almond fruit. R 2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.

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Section: Discussionmentioning

confidence: 99%

“…Two-dimensional (2D) based systems are increasing used. For example, Baek et al (2020) designed an image acquisition device that imaged 100 seeds at a time. To quantitatively measure morphological and color parameters of soybean seeds, the device was equipped with top and side-view RGB cameras.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

Liu

Jin³

et al. 2020

Front. Plant Sci.

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“…The size of soybean seed, which is not only a very important appearance quality but also strongly associated with the commercial value [ 4 ], is an important agronomic trait that affects the quality and yield of soybean [ 5 ]. The seed morphological phenotypes, which include seed shape, seed length, seed width, seed height, seed circumference, seed surface area and seed volume and so on, are essential to reflect the growth and development, physiology, biochemistry and genetics of soybean [ 6 ]. Paying attention to the morphological traits of soybean seeds is a powerful indicator for improving crop yield.…”

Section: Introductionmentioning

confidence: 99%

“…Since the size of soybean seeds is small generally, the operation of manual measurements is labor-intensive, time-consuming and error prone extremely. Moreover, the phenotypic information of manual measurement is limited to the seed length, seed width, and seed height, and no more information can be measured, it is not applicable for large-scale collection of soybean seeds morphological phenotype information [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of imaging technology, it is possible to measure the morphological phenotype information of high throughput soybean seeds, however the seeds need to be sparsely placed without physical contact [ 6 ]. Traditional image-based researches on soybean seed phenotyping mainly include seed quality evaluation [ 5 , 7 – 9 ], seed counting [ 10 – 12 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning

Yang

Zheng

et al. 2021

Plant Methods

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Background Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step. Results We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset. Conclusion The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.

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Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

Cited by 35 publications

References 20 publications

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

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