Editorial: phenotyping; from plant, to data, to impact and highlights of the international plant phenotyping symposium - ipps 2018

Editorial on the Research Topic
Phenotyping; From Plant, to Data, to Impact and Highlights of the International Plant Phenotyping Symposium – IPPS 2018

The aim of this Research Topic is to provide a series of research articles on a range of subjects in Plant phenomics ( Tardieu et al., 2017 ) from the use of appropriate sensors for capturing morphological and physiological traits to smart ways of processing, extracting and managing “ clean” data. Presentation of new approaches to data acquisition, processing and analysis as well as prerequisites for automation are also among the objectives of this Research Topic.

Plant phenomics is the use of sensors, cameras, and algorithms for trait quantification in plants including model species crops, forages, vegetables as well as forest and fruit trees. The relationships between plants and their environment including soil microbes can affect this quantification bringing in new challenging parameters into the equation. Data may be acquired in a range of experimental conditions including laboratories, greenhouse, and field or natural experimental site within forests. Data from the latter can be used for biodiversity studies as well where the scope, measurement means, and objectives can be shifted for that purpose.

This Research Topic presents a series of articles with an insight into recent advances in plant phenomics. There are 12 research articles and one opinion paper covering the heterogeneity and complexity ( Watt et al., 2020 ) of this rapidly developing scientific domain. The majority of the articles address problems associated with data acquisition and analysis using innovative computational methods, and a few discuss artificial intelligence. This reflects the current trend in plant phenomics which leans toward recovering the maximum amount of knowledge and information from the data deluge triggered by high throughput phenotyping. The significance of data management is also highlighted. In this Research Topic, phenomics data acquisition at a very large scale, is demonstrated by Hao et al. . Their work shows how the airborne LiDAR approach allows estimation of the response of forest ecosystems regarding climate change and carbon density. Likewise, Zhang et al. used aerial vectors to enable high throughput data acquisition for understanding leaf development in rapeseed. This approach was taken to obtain usable information for precision farming, including precision fertilization, irrigation, and yield prediction. High throughput phenotyping in automated greenhouse is discussed by Nguyen et al. . They show the production of long time series of images allowing measurement of top view area and shoot biomass to help the estimation of nitrogen use efficiency in wheat.

The imaging types of data produced can also be rather diverse, including color images (as in Hao et al. ; Cho et al. ; Bateman et al. ) but also hyperspectral ( Bruning et al. ) or Fluorescence ( Hupp et al. ; Méline et al. ) imaging. Méline et al. . suggest promise for estimation of plant response to biotic stress in a non-destructive way in a model plant. This opens up future perspectives for crop research through translational biology.

Major recent innovations in automation and integration of plant phenomics have mobilized the development of deep and machine learning methods and tools and the efforts to address issues with data processing. Dobrescu et al. discuss multitask learning (MTL) to infer two morphological and one classification trait at the same time. Bateman et al. present a new method and name it local context network (LC-Net), which is designed to measure biomass of individual species in a mixed sward using convolutional neural networks.

Neveu, P., Tireau, A., Hilgert, N., Nègre, V., Mineau-Cesari, J., Brichet, N., et al. (2018). Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytol. 221, 588–601. doi: 10. 1111/nph. 15385

Papoutsoglou, E. A., Faria, D., Arend, D., Arnaud, E., Athanasiadis, I. N., Chaves, I., et al. (2020). Enabling reusability of plant phenomic datasets with MIAPPE 1. 1. New Phytol. 227, 260–273. doi: 10. 1111/nph. 16544

Pommier, C., Michotey, C., Cornut, G., Roumet, P., Duchêne, E., Flores, R., et al. (2019). Applying FAIR principles to plant phenotypic data management in GnpIS. Plant Phenomics 2019, 1–15. doi: 10. 34133/2019/1671403

Selby, P., Abbeloos, R., Backlund, J. E., Basterrechea Salido, M., Bauchet, G., Benites-Alfaro, O. E., et al. (2019). BrAPI—an application programming interface for plant breeding applications. Bioinformatics 35, 4147–4155. doi: 10. 1093/bioinformatics/btz190

Shrestha, R., Matteis, L., Skofic, M., Portugal, A., McLaren, G., Hyman, G., et al. (2012). Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice. Front. Physiol . 3: 326. doi: 10. 3389/fphys. 2012. 00326

Tardieu, F., Cabrera-Bosquet, L., Pridmore, T., and Bennett, M. (2017). Plant phenomics, from sensors to knowledge. Curr. Biol. 27, R770–R783. doi: 10. 1016/j. cub. 2017. 05. 055

Watt, M., Fiorani, F., Usadel, B., Rascher, U., Muller, O., and Schurr, U. (2020). Phenotyping: new windows into the plant for breeders. Annu. Rev. Plant Biol . 71, 689–712. doi: 10. 1146/annurev-arplant-042916-041124

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3: 160018. doi: 10. 1038/sdata. 2016. 18