Journal of Agricultural Big Data >
Big Data of Plant Phenomics and Its Research Progress
Received date: 2019-05-05
Online published: 2019-08-21
Plant phenomics is capable of acquiring gigantic multi-dimensional, multi-environment, and multi-source heterogeneous plant phenotyping datasets through integrated automation platforms and information retrieval technologies, based on which the big-data driven plant phenomics research is established. This emerging research domain aims to systematically and thoroughly explore the internal relationship between "gene-phenotype-environment" at the omics level, so that phenomics methods can be utilized to unravel the formation mechanism of specific biological traits in a comprehensive manner. As a result, it is greatly catalyzing the research progress of functional genomics, crop molecular breeding, and efficient cultivation. In this paper, we summarized the background, definition, initiation, and features of the big-data driven plant phenomics, followed by a systemic overview of the progress of this field, including the acquisition and analysis of plant phenotyping data, data management and relevant database construction techniques for administering big data generated, the prediction of phenotypic traits, and its connection with the plant omics research. Furthermore, this paper focuses on discussing present problems and challenges encountered by both plant research and related applications, including (1) the standardization of collecting plant phenotypes, (2) research and development (R&D) of diverse phenotyping devices, supporting facilities, and low-cost phenotyping equipment, (3) the establishment of big data platforms that can openly share phenotyping data and phenotypic traits information, (4) theoretical approaches for fusion algorithms and data mining techniques, and (5) collaborative, sharing and interactive mechanisms for the plant phenomics community to adopt. Finally, the paper puts forward suggestions in four aspects that need to be strengthened: (1) systematic design and standards of plant phenomics research, (2) revealing the mechanism of plant phenotype and environtype to facilitate intelligent equipment R&D, (3) the establishment of big data for plant phenomics, and (4) the formation of collaborations through academic networks and specialized research groups and laboratories.
Chunjiang Zhao . Big Data of Plant Phenomics and Its Research Progress[J]. Journal of Agricultural Big Data, 2019 , 1(2) : 5 -14 . DOI: 10.19788/j.issn.2096-6369.190201
| [1] | 李国杰 . 大数据研究的科学价值[J]. 中国计算机学会通讯, 2012,8(9):8-15. |
| [1] | Li G J . The scientific value of big data research[J]. Communications of the China Computer Federation, 2012,8(9):8-15. |
| [2] | 邱晨辉 . 院士专家热议“人工智能计算”——人工智能时代,谁将成为“第一生产力”[N]. 中国青年报, 2018. |
| [2] | Qiu C H . Academician experts hotly discuss "artificial intelligence computing" - who will become the "first productivity" in the era of artificial intelligence[N]. China Youth Daily, 2018. |
| [3] | 中国人工智能学会. 中国人工智能系列白皮书--智能农业 [R]. 2016. 9. |
| [3] | Chinese Association for Artificial Intelligence. White paper on artificial intelligence in China--Intelligent Agriculture[R]. 2016. 9. |
| [4] | Wallace JG , Rodgers-Melnick E and Buckler ES. On the Road to Breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics[J]. Annual Review of Genetics, 2018,52:421-444. |
| [5] | Marx V . The big challenges of big data[J]. Nature, 2013,498:255-260. |
| [6] | illumina. HiSeq X TM series of sequencing systems[A] . Specification Sheet: Sequencing, 2016. |
| [7] | Furbank RT and Tester M . Phenomics--technologies to relieve the phenotyping bottleneck[J]. Trends in Plant Science, 2011,16(12):635-644. |
| [8] | Zhao C, Zhang Y, Du J , et al. Crop phenomics: current status and perspectives[J]. Frontiers in Plant Science, 2019,10:714. |
| [9] | Tardieu F, Cabrera-Bosquet L, Pridmore T , et al. Plant phenomics, from sensors to knowledge[J]. Current Biology, 2017,27(15):R770-R783. |
| [10] | Hickey LT, A NH, Robinson H , et al. Breeding crops to feed 10 billion[J]. Nature Biotechnology, 2019,37(7):744-754. |
| [11] | McCouch S, Baute GJ, Bradeen J , et al. Agriculture: feeding the future[J]. Nature, 2013,499(7456):23-24. |
| [12] | 潘映红 . 论植物表型组和植物表型组学的概念与范畴[J]. 作物学报, 2015,41(2):175-186. |
| [12] | Pan Y H . Analysis of concepts and categories of plant phenome and phenomics[J]. Acta Agronomica Sinica, 2015,41(2):175-186. |
| [13] | 周济, Tardieu F, Pridmore T , 等. 植物表型组学:发展、现状与挑战[J]. 南京农业大学学报, 2018,41(4):580-588. |
| [13] | Zhou J, Tardieu F, Pridmore T , et al. Plant phenomics: history, present status and challenges[J]. Journal of Nanjing Agricultural University, 2018,41(4):580-588. |
| [14] | Pieruschka R and Schurr U . Plant phenotyping: past, present, and future[J]. Plant Phenomics, 2019: 1-6. |
| [15] | Virlet N, Sabermanesh K, Sadeghi-Tehran P , et al. Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring[J]. Functional Plant Biology, 2017,44(1):143. |
| [16] | 宁康, 陈挺 . 生物医学大数据的现状与展望[J]. 科学通报, 2015,60(5-6):534-546. |
| [16] | Ning K, Chen T . Big data for biomedical research: current status and prospective[J]. Chinese Science Bulletin, 2015,60(5-6):534-546. |
| [17] | Shao M, Zhang Y, Du J , et al. Fast analysis of maize kernel plumpness characteristics through Micro-CT technology [C]. International Conference on Computer and Computing Technologies in Agriculture, 2019: 31-39. |
| [18] | Hawkesford MJ and Lorence A . Plant phenotyping: increasing throughput and precision at multiple scales[J]. Functional Plant Biology, 2017, 44(1): v-vii. |
| [19] | Cwiek-Kupczynska H, Altmann T, Arend D , et al. Measures for interoperability of phenotypic data: minimum information requirements and formatting[J]. Plant Methods, 2016,12:44. |
| [20] | Araus JL and Cairns JE . Field high-throughput phenotyping: the new crop breeding frontier[J]. Trends in Plant Science, 2014,19(1):52-61. |
| [21] | Chenu K, Deihimfard R and Chapman SC . Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt--spatial and temporal trends[J]. New Phytologist, 2013,198(3):801-820. |
| [22] | Reuzeau C, Frankard V, Hatzfeld Y , et al. Traitmill?: a functional genomics platform for the phenotypic analysis of cereals[J]. Plant Genetic Resources, 2006,4(1):20-24. |
| [23] | Li L, Zhang Q and Huang D . A review of imaging techniques for plant phenotyping[J]. Sensors (Basel), 2014,14(11):20078-20111. |
| [24] | Hall HC, Fakhrzadeh A, Luengo Hendriks CL , et al. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images[J]. Frontiers in Plant Science, 2016,7:119. |
| [25] | Du J, Zhang Y, Guo X , et al. Micron-scale phenotyping quantification and three-dimensional microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning[J]. Functional Plant Biology, 2017,44(1):10. |
| [26] | Huang K-Y . Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features[J]. Computers and Electronics in Agriculture, 2007,57(1):3-11. |
| [27] | Lee U, Chang S, Putra GA , et al. An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis[J]. PLoS One, 2018,13(4):e0196615. |
| [28] | Pound MP, Atkinson JA, Townsend AJ , et al. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping[J]. Gigascience, 2017,6(10):1-10. |
| [29] | Uzal LC, Grinblat GL, Namías R , et al. Seed-per-pod estimation for plant breeding using deep learning[J]. Computers and Electronics in Agriculture, 2018,150:196-204. |
| [30] | Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G , et al. An automatic non-destructive method for the classification of the ripeness stage of red delicious apples in orchards using aerial video[J]. Agronomy, 2019,9(2):84. |
| [31] | Ghosal S, Blystone D, Singh AK , et al. An explainable deep machine vision framework for plant stress phenotyping[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018,115(18):4613-4618. |
| [32] | Mokhtar U, Ali MAS, Hassanien AE , et al. Identifying two of tomatoes leaf viruses using support vector machine[M]. New Delhi: Springer, 2015: 771-782. |
| [33] | Raza SE, Prince G, Clarkson JP , et al. Automatic detection of diseased tomato plants using thermal and stereo visible light images[J]. PLoS One, 2015,10(4):e0123262. |
| [34] | Wetterich CB, Kumar R, Sankaran S , et al. A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of citrus huanglongbing disease in USA and Brazil[J]. Frontiers in Optics, 2013: 841738. |
| [35] | Rumpf T, Mahlein AK, Steiner U , et al. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance[J]. Computers and Electronics in Agriculture, 2010,74(1):91-99. |
| [36] | Wahabzada M, Mahlein AK, Bauckhage C , et al. Metro maps of plant disease dynamics--automated mining of differences using hyperspectral images[J]. PLoS One, 2015,10(1):e0116902. |
| [37] | Odilbekov F, Armoniene R, Henriksson T , et al. Proximal phenotyping and machine learning methods to identify septoria tritici blotch disease symptoms in wheat[J]. Frontiers in Plant Science, 2018,9:685. |
| [38] | Chen D, Neumann K, Friedel S , et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis[J]. Plant Cell, 2014,26(12):4636-4655. |
| [39] | Jin S, Su Y, Gao S , et al. Deep Learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms[J]. Frontiers in Plant Science, 2018,9:866. |
| [40] | Xiong X, Duan L, Liu L , et al. Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization[J]. Plant Methods, 2017,13:104. |
| [41] | Wang X, Xuan H, Evers B , et al. High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat[J]. bioRxiv, 2019. |
| [42] | Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov , et al. DeepWheat: estimating phenotypic traits from crop images with deep learning[J]. IEEE Winter Conference on Applications of Computer Vision, 2018: 323-332. |
| [43] | Harjatin Baweja, Tanvir Parhar, Omeed Mirbod , et al. StalkNet: a deep learning pipeline for high-throughput measurement of plant stalk count and stalk width. Field and Service Robotics. Cham: Springer, 2018. |
| [44] | Xu R, Li C and Paterson AH . Multispectral imaging and unmanned aerial systems for cotton plant phenotyping[J]. PLoS One, 2019,14(2):e0205083. |
| [45] | Garcia-Ruiz F, Sankaran S, Maja JM , et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture, 2013,91:106-115. |
| [46] | Liu S, Baret F, Abichou M , et al. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model[J]. Agricultural and Forest Meteorology, 2017,247:12-20. |
| [47] | Bauer A, Bostrom AG, Ball J , et al. Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production[J]. Horticulture Research, 2019,6:70. |
| [48] | Redmon J, Divvala S, Girshick R , et al. You Only Look Once: unified, real-Time object detection[J]. Computer Science, 2015. |
| [49] | Sabour S, Frosst N and Hinton GE . Dynamic routing between capsules[J]. Computer Science, 2017. |
| [50] | Colmsee C, Mascher M, Czauderna T , et al. OPTIMAS-DW: a comprehensive transcriptomics, metabolomics, ionomics, proteomics and phenomics data resource for maize[J]. BMC Plant Biology, 2012,12:245. |
| [51] | Reynolds D, Ball J, Bauer A , et al. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management[J]. GigaScience, 2019,8(3). |
| [52] | Salehi A, Jimenez-Berni J, Deery DM , et al. SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data[J]. Plant Methods, 2015,11:53. |
| [53] | Cooper L, Meier A, Laporte MA , et al. The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics[J]. Nucleic Acids Research, 2018,46(D1):D1168-D1180. |
| [54] | Neveu P, Tireau A, Hilgert N , et al. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System[J]. New Phytologist, 2019,221(1):588-601. |
| [55] | Zhang X, Huang C, Wu D , et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth[J]. Plant Physiology, 2017,173(3):1554-1564. |
| [56] | Arend D, Junker A, Scholz U , et al. PGP repository: a plant phenomics and genomics data publication infrastructure[J]. Database(Oxford), 2016. |
| [57] | Kohl K and Gremmels J . A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices[J]. Plant Methods, 2015,11:25. |
| [58] | Goff SA, Vaughn M, McKay S , et al. The iPlant collaborative: cyberinfrastructure for plant biology[J]. Frontiers in Plant Science, 2011,2:34. |
| [59] | Jayakodi M, Selvan SG, Natesan S , et al. A web accessible resource for investigating cassava phenomics and genomics information: BIOGEN BASE[J]. Bioinformation, 2011,6(10):391-392. |
| [60] | Krajewski P, Chen D, Cwiek H , et al. Towards recommendations for metadata and data handling in plant phenotyping[J]. Journal of Experimental Botany, 2015,66(18):5417-5427. |
| [61] | Oellrich A, Walls RL, Cannon EK , et al. An ontology approach to comparative phenomics in plants[J]. Plant Methods, 2015,11:10. |
| [62] | Lobell DB, Schlenker W and Costa-Roberts J . Climate trends and global crop production since 1980[J]. Science, 2011,333(6042):616-620. |
| [63] | Lobell DB and Burke MB . On the use of statistical models to predict crop yield responses to climate change[J]. Agricultural and Forest Meteorology, 2010,150(11):1443-1452. |
| [64] | Johnson MD, Hsieh WW, Cannon AJ , et al. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods[J]. Agricultural and Forest Meteorology, 2016, 218- 219:74-84. |
| [65] | Webb S . Deep learning for biology[J]. Nature, 2018,554(7693):555-557. |
| [66] | You J, Li X, Low M , et al. Deep gaussian process for crop yield prediction based on remote sensing data[M], 2017. |
| [67] | Gro Intelligence . How to make money with Gro intelligence yield models[EB/OL]. https://www.gro-intelligence.com/about/blog/how-to-make-money -with-gro-intelligence-yield-models. |
| [68] | Huang X, Wei X, Sang T , et al. Genome-wide association studies of 14 agronomic traits in rice landraces[J]. Nature Genetics, 2010,42(11):961-967. |
| [69] | Busemeyer L, Mentrup D, Moller K , et al. BreedVision-a multi-sensor platform for non-destructive field-based phenotyping in plant breeding[J]. Sensors (Basel), 2013,13(3):2830-2847. |
| [70] | Yang W, Guo Z, Huang C , et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice[J]. Nature Communications, 2014,5:5087. |
| [71] | Yang W, Guo Z, Huang C , et al. Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer[J]. Journal of Experimental Botany, 2015,66(18):5605-5615. |
| [72] | Donald CM . The breeding of crop ideotypes[J]. Euphytica, 1968,17:385-403. |
| [73] | Junker A, Muraya MM, Weigelt-Fischer K , et al. Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems[J]. Frontiers in Plant Science, 2014,5:770. |
/
| 〈 |
|
〉 |
