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Xue W, Ding H, Jin T, Meng J, Wang S, Liu Z, Ma X, Li J. CucumberAI: Cucumber Fruit Morphology Identification System Based on Artificial Intelligence. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0193. [PMID: 39144674 PMCID: PMC11324094 DOI: 10.34133/plantphenomics.0193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/30/2024] [Indexed: 08/16/2024]
Abstract
Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide. Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits. At present, several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits; however, these manual methods are time-consuming and inefficient. Therefore, in this paper, we propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features, including 32 newly defined parameters. The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques. The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition. Additionally, the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation, a MobileNetV2 model for carpel classification, a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification. The relationships between the image-based manual and algorithmic traits are highly correlated, and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness, and a fruit appearance cluster analysis was also performed. In brief, CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements.
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Affiliation(s)
- Wei Xue
- College of Artificial Intelligence,
Nanjing Agricultural University, Nanjing 210095, China
| | - Haifeng Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture,
Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
| | - Tao Jin
- College of Artificial Intelligence,
Nanjing Agricultural University, Nanjing 210095, China
| | - Jialing Meng
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture,
Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
| | - Shiyou Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture,
Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
| | - Zuo Liu
- College of Artificial Intelligence,
Nanjing Agricultural University, Nanjing 210095, China
| | - Xiupeng Ma
- College of Foreign Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Ji Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture,
Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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Ma L, Deng D, Su Y, Xiao L. X-ray-μCT: nondestructively identifying hidden microphenotypes inside living crop seeds. TRENDS IN PLANT SCIENCE 2024; 29:99-100. [PMID: 37973441 DOI: 10.1016/j.tplants.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/20/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Liying Ma
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Danyi Deng
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Yi Su
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China.
| | - Langtao Xiao
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China.
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Wang W, Hu C, Chang Y, Wang L, Bi Q, Lu X, Zheng Z, Zheng X, Wu D, Niu B. Differentiated responses of the phyllosphere bacterial community of the yellowhorn tree to precipitation and temperature regimes across Northern China. FRONTIERS IN PLANT SCIENCE 2023; 14:1265362. [PMID: 37954985 PMCID: PMC10634255 DOI: 10.3389/fpls.2023.1265362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 10/10/2023] [Indexed: 11/14/2023]
Abstract
Introduction As an ephemeral and oligotrophic environment, the phyllosphere harbors many highly diverse microorganisms. Importantly, it is known that their colonization of plant leaf surfaces is considerably influenced by a few abiotic factors related to climatic conditions. Yet how the dynamics of phyllosphere bacterial community assembly are shaped by detailed climatological elements, such as various bioclimatic variables, remains poorly understood. Methods Using high-throughput 16S rRNA gene amplicon sequencing technology, we analyzed the bacterial communities inhabiting the leaf surfaces of an oilseed tree, yellowhorn (Xanthoceras sorbifolium), grown at four sites (Yinchuan, Otogqianqi, Tongliao, and Zhangwu) whose climatic status differs in northern China. Results and Discussion We found that the yellowhorn phyllosphere's bacterial community was generally dominated by four phyla: Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Nevertheless, bacterial community composition differed significantly among the four sampled site regions, indicating the possible impact of climatological factors upon the phyllosphere microbiome. Interestingly, we also noted that the α-diversities of phyllosphere microbiota showed strong positive or negative correlation with 13 bioclimatic factors (including 7 precipitation factors and 6 temperature factors). Furthermore, the relative abundances of 55 amplicon sequence variants (ASVs), including three ASVs representing two keystone taxa (the genera Curtobacterium and Streptomyces), exhibited significant yet contrary responses to the precipitation and temperature climatic variables. That pattern was consistent with all ASVs' trends of possessing opposite correlations to those two parameter classes. In addition, the total number of links and nodes, which conveys community network complexity, increased with rising values of most temperature variables. Besides that, remarkably positive relevance was found between average clustering coefficient and most precipitation variables. Altogether, these results suggest the yellowhorn phyllosphere bacterial community is capable of responding to variation in rainfall and temperature regimes in distinctive ways.
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Affiliation(s)
- Weixiong Wang
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
- The Center for Basic Forestry Research, College of Forestry, Northeast Forestry University, Harbin, China
- College of Life Science, Northeast Forestry University, Harbin, China
| | - Congcong Hu
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Yu Chang
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
- College of Life Science, Northeast Forestry University, Harbin, China
| | - Libing Wang
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Quanxin Bi
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Xin Lu
- Chifeng Research Institute of Forestry Science, Chifeng, China
- National Forestry and Grassland Shiny-Leaved Yellowhorn Engineering and Technology Research Center, Chifeng, China
| | - Zhimin Zheng
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
- The Center for Basic Forestry Research, College of Forestry, Northeast Forestry University, Harbin, China
| | - Xiaoqi Zheng
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Di Wu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
- The Center for Basic Forestry Research, College of Forestry, Northeast Forestry University, Harbin, China
- College of Life Science, Northeast Forestry University, Harbin, China
| | - Ben Niu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
- The Center for Basic Forestry Research, College of Forestry, Northeast Forestry University, Harbin, China
- College of Life Science, Northeast Forestry University, Harbin, China
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Solovchenko A, Shurygin B, Nesterov DA, Sorokin DV. Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants. Biophys Rev 2023; 15:939-946. [PMID: 37975015 PMCID: PMC10643738 DOI: 10.1007/s12551-023-01125-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/23/2023] [Indexed: 11/19/2023] Open
Abstract
High-throughput phenotyping is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenotyping comes from the automated, rapid, non-invasive collection of large datasets describing plant objects. In this context, the goal of extracting relevant information from different kinds of images is of paramount importance. We review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping. The advantages and drawbacks of both approaches will be discussed with a focus on the monitoring of plants. We argue that an emerging approach combining the strengths of the spectral and the machine learning-based approaches will remain a promising direction in plant phenotyping in the nearest future.
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Affiliation(s)
- Alexei Solovchenko
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
- Institute of Natural Sciences, Derzhavin Tambov State University, Tambov, Russia
| | - Boris Shurygin
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
- Institute of Natural Sciences, Derzhavin Tambov State University, Tambov, Russia
| | - Dmitry A. Nesterov
- Faculty of Computer Science and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitry V. Sorokin
- Faculty of Computer Science and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
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Poorter H, Hummel GM, Nagel KA, Fiorani F, von Gillhaussen P, Virnich O, Schurr U, Postma JA, van de Zedde R, Wiese-Klinkenberg A. Pitfalls and potential of high-throughput plant phenotyping platforms. FRONTIERS IN PLANT SCIENCE 2023; 14:1233794. [PMID: 37680357 PMCID: PMC10481964 DOI: 10.3389/fpls.2023.1233794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/01/2023] [Indexed: 09/09/2023]
Abstract
Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.
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Affiliation(s)
- Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | | | - Kerstin A. Nagel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Fabio Fiorani
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Olivia Virnich
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ulrich Schurr
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Anika Wiese-Klinkenberg
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, Jülich, Germany
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