TY - JOUR

T1 - Plant Phenomics: Fundamental Bases, Software and Hardware Platforms, and Machine Learning

AU - Demidchik, V. V.

AU - Shashko, A. Y.

AU - Bandarenka, U. Y.

AU - Smolikova, G. N.

AU - Przhevalskaya, D. A.

AU - Charnysh, M. A.

AU - Pozhvanov, G. A.

AU - Barkosvkyi, A. V.

AU - Smolich, I. I.

AU - Sokolik, A. I.

AU - Yu, M.

AU - Medvedev, S. S.

N1 - Demidchik, V.V., Shashko, A.Y., Bandarenka, U.Y. et al. Plant Phenomics: Fundamental Bases, Software and Hardware Platforms, and Machine Learning. Russ J Plant Physiol 67, 397–412 (2020). https://doi.org/10.1134/S1021443720030061

PY - 2020/5

Y1 - 2020/5

N2 - In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e., physical and biochemical characteristics, considered as a set of phenotypes of a plant organism, has emerged. Phenomics is a postgenomic discipline that actively uses the achievements of the genomic era and bioinformatics. It supplements them with standardized and statistically significant factual material on phenotypes with a high degree of detail. The technique of obtaining and analyzing information about phenotypes in phenomics is called phenotyping. High-performance phenotyping, providing digital automated analysis of large data samples, has become widespread. Recent progress in high-performance phenotyping has been associated with the development of image registration systems in various spectral regions, approaches to cultivating plant objects under standardized conditions, sensory technologies, robotics, and methods for data processing and analysis, such as computer vision and machine learning (artificial neural network). Phenomics technologies have a high information content analysis, surpassing human capabilities, performing measurements in the hyperspectral range using X-ray tomography and ultra-precise "thermal" images, and have a number of other low-invasive and precision approaches. Arrays of data obtained using phenomics technologies are recorded and processed automatically and are free from the problems of subjective assessment and inadequate statistical processing. It is assumed that phenotyping will allow for the creation of digital models of the vital activity processes and the "formation" of plant productivity at the organism level in connection with the dynamics of transcriptomes, proteomes, and metabolomes. Phenomics helps researchers transform a large amount of information received from modern sensors into new knowledge using computer data processing and modeling, reducing the distance from basic science to the practical application of results in crop production and breeding. Phenotyping is actively developing both in laboratory and in greenhouse conditions as well as on open agricultural sites, forests, and in real natural phytocenoses. The review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.

AB - In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e., physical and biochemical characteristics, considered as a set of phenotypes of a plant organism, has emerged. Phenomics is a postgenomic discipline that actively uses the achievements of the genomic era and bioinformatics. It supplements them with standardized and statistically significant factual material on phenotypes with a high degree of detail. The technique of obtaining and analyzing information about phenotypes in phenomics is called phenotyping. High-performance phenotyping, providing digital automated analysis of large data samples, has become widespread. Recent progress in high-performance phenotyping has been associated with the development of image registration systems in various spectral regions, approaches to cultivating plant objects under standardized conditions, sensory technologies, robotics, and methods for data processing and analysis, such as computer vision and machine learning (artificial neural network). Phenomics technologies have a high information content analysis, surpassing human capabilities, performing measurements in the hyperspectral range using X-ray tomography and ultra-precise "thermal" images, and have a number of other low-invasive and precision approaches. Arrays of data obtained using phenomics technologies are recorded and processed automatically and are free from the problems of subjective assessment and inadequate statistical processing. It is assumed that phenotyping will allow for the creation of digital models of the vital activity processes and the "formation" of plant productivity at the organism level in connection with the dynamics of transcriptomes, proteomes, and metabolomes. Phenomics helps researchers transform a large amount of information received from modern sensors into new knowledge using computer data processing and modeling, reducing the distance from basic science to the practical application of results in crop production and breeding. Phenotyping is actively developing both in laboratory and in greenhouse conditions as well as on open agricultural sites, forests, and in real natural phytocenoses. The review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.

KW - computer vision

KW - convolutional neural networks

KW - high-performance phenotyping

KW - machine learning

KW - phenomics platform

KW - plant phenomics

KW - plants

KW - CHLOROPHYLL FLUORESCENCE

KW - DROUGHT STRESS

KW - QUANTIFICATION

KW - CLASSIFICATION

KW - RESPONSES

KW - SHAPE

KW - WATER-STRESS

KW - GROWTH

KW - ARCHITECTURE

KW - LIGHT ACCLIMATION

UR - http://www.scopus.com/inward/record.url?scp=85084844241&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/980167a1-29fb-3fcd-ad82-bd2c829639a0/

U2 - 10.1134/S1021443720030061

DO - 10.1134/S1021443720030061

M3 - Обзорная статья

VL - 67

SP - 397

EP - 412

JO - Russian Journal of Plant Physiology

JF - Russian Journal of Plant Physiology

SN - 1021-4437

IS - 3

ER -