Chapter 16 - High-throughput phenotyping: the latest research tool for sustainable crop production under global climate change scenarios

Abstract

Climatic factors affect various stages of plant growth, thereby affecting agricultural productivity and production stability. The growth and development of various tissues and organs during the complete growth period of plants are affected by changes in climate and environment. The genotype is the internal cause for the expression of the phenotype, while the environment is the external conditions for the appearance of various morphological characteristics. With the rapid progress of high-throughput plant phenotype measurement technology, combined with genomics, bioinformatics, and big data computing, plant phenotyping will greatly promote the process of functional genomics research and crop molecular breeding and efficient cultivation. Phenotypes can effectively track the links between genotypes, environmental factors, and phenotypes. Without detailed phenotype data, it is difficult to fully understand the complex effects of genomic and environmental factors on plant phenotypes. Therefore, difficulties in plant scientific research are gradually shifting from genetic analysis to phenotypic analysis. Plant phenotyping began at the end of the 20th century, and its core was to obtain high-quality, reproducible trait data, and then quantify the interactions of genotype and environment and their impact on key traits related to yield, quality, and stress resistance. As more and more plant and trait parameters must be measured quickly and accurately, many of the world's top scientific research institutions have shifted their research focus to solving practical problems such as experimental design, quantitative analysis, and interpretation of conclusions. Characterizing key traits through phenotypic analysis can provide big data-based decision support for breeding, cultivation, and agricultural practices. Plant genomes have undergone rapid development in recent years, but the lack of sufficient phenotypic data has limited human ability to parse quantitative trait genetics. This can be addressed by developing a plant phenotypic information collection platform and performing image analysis. High-throughput, automated, high resolution plant phenotypic information collection platforms and analytical technologies are critical to the acceleration of plant improvement and breeding, increasing yield, and resistance to pests and diseases. These systems are used to analyze genomic information and quantitatively study complex traits related to growth, yield, and adaptation to biotic or abiotic stress. It is an important pathway for integration, which can fulfill the gaps between genomic information and plant phenotypic plasticity. With the growth in the demands of scientific research and the development of imaging sensor technology, it has become possible to collect high-throughput, high-efficiency, high-precision, low-error, and low cost automatic phenotypic information. The data monitored by imaging sensors is objective and can monitor and analyze plants in real time. Therefore, automatic phenotype information collection technology has begun to be widely used in plant phenotype information collection platforms. With the development of modern and intelligent agricultural equipment, scholars hope to discover functionally diverse species and compare performance and plant response to the environment in a large number of plants. To generate a correlation between phenotypic traits, genes, environment, and expression, high-throughput plant phenotype information collection has been invented. High-throughput refers to the ability to measure more samples and/or more data points than manual phenotyping, not only with a high number of samples per unit time, but also with the synchronization and efficiency of data processing and parameter acquisition. The hardware platform scans the source data to match. High-throughput phenotyping relies on rapid transportation, automated sensing, data acquisition, data analysis methods and technical equipment, and is carried out by various sensors, such as CCD camera, near-infrared instrument, infrared instrument, thermal imager, spectral imager, fluorescence imager, etc., to monitor indoor and field plants, so as to obtain more phenotypic parameters in a short time. Due to the huge potential of plant phenotype information collection technology in the agricultural field, scientific research institutions and enterprises in various countries are actively developing and constructing high-throughput plant phenotype information collection platforms. Phenotypic data collection and analysis methods are the core part of plant phenotyping research, including indoor and outdoor parts. Modern indoor high-throughput plant phenotype detection platforms generally detect plants closely related to plant genetics and variation through automated transmission equipment and integrated sensors (such as visible light, near-infrared, far-infrared, fluorescence, multispectral, laser, hyperspectral, etc.). The acquisition of a plant dynamic growth and developmental phenotype dataset. Its advantages are high resolution, high controllability and high-throughput, which can provide high-quality multidimensional images and experimental data for subsequent plant phenotyping. Whether it is aboveground or underground, indoor phenotype monitoring usually takes the individual plant as the unit and describes the population characteristics by collecting the characteristics of the individual plant. Depending on the image sensor accuracy, the extracted phenotypic data can often be accurate down to the tissue cell level. Thus, the scale division of indoor phenotypic traits can range from populations to histiocytes. Field-oriented plant phenotyping platforms provide accurate and continuous collection of single leaves or plant organs, single plants, small plots, and entire farms from proximal to long distances, mainly including vehicle-mounted, self-propelled, gantry, and suspension as well as several types such as drones, aerial remote sensing and spectral satellite imaging. The field high-throughput platform mainly includes two types of field machinery and UAVs equipped with multi-sensors, which can achieve rapid and non-destructive acquisition of plant population phenotype information under field conditions. The combination of agricultural machinery and equipment with a multi-sensor platform can effectively reduce the variation in measurement results but is limited by the distribution of crops and soil conditions after irrigation. Therefore, it is difficult to quickly realize cross-regional applications, the operation efficiency is low, and it cannot be used over a large range. However, the rapid analysis of field crop phenotype information based on an UAV-equipped multi-sensor platform has high technical efficiency and low cost and is suitable for complex farmland environments. It has a wide range of applications in the analysis of crop height, chlorophyll content, LAI, disease susceptibility, drought stress sensitivity, nitrogen content and yield, making it an important means to obtain crop phenotype information. Plant roots are an important part of plants and have very important functions such as water and nutrient absorption and transport, organic matter storage, plant anchoring, and interaction with soil. Since roots grow underground, the core of collecting root phenotypes is how to visualize roots growing under natural conditions. Therefore, the collection and analysis of root phenotypic traits has become the focus and challenge of biological and phenotypic research. At present, the research progress of cultivation and improvement based on root traits is very slow, and the screening of root traits is still a very time-consuming task. Researchers must continue to develop systematic and large-scale high-throughput root phenotyping platforms suitable for different cultivation goals, so as to accelerate the screening of root traits and the understanding of root function, and to gain an in-depth understanding of how root traits are related to whole plant stress resistance strategies correlate to increase crop productivity and ultimately successfully identify key root traits for crop improvement. In recent years, with the continuous development of remote sensing and related sensor technologies, a variety of non-destructive plant detection technologies have emerged, providing effective means for crop phenotype monitoring, disease and insect pest monitoring, and crop identification. RGB cameras are a relatively common sensor device in crop phenotyping technology. In the experiment, a digital camera with visible light imaging on a UAV can be used to improve the efficiency of data collection and achieve rapid acquisition of high-definition images. It can be used to monitor crop emergence during agricultural production, rate, flowering dynamics, canopy coverage, and lodging. Thermal imaging sensors use an infrared detector and an optical imaging lens to receive infrared radiation energy in a photosensitive element infrared detector. Infrared imaging technology is used to measure plant canopy temperature to infer plant water use efficiency and photosynthesis efficiency. It is also used to measure the response of crops to osmotic stress such as salinity or drought. It can also measure the impact of other abiotic stresses on organisms and detect the presence of pests in the grain. Because the crop canopy temperature changes with time, conventional handheld infrared temperature measurement equipment is limited by its low measurement efficiency, and it is difficult to be widely used in large-scale breeding areas. However, UAVs are equipped with thermal imagers for breeding areas, where canopy temperature provides a new efficient and reliable method. At present, thermal infrared technology is increasingly used in the field of precision agriculture. To ensure stable agricultural production, increased production and food security, optimized agricultural production structure, and a reduction in the use of pesticides and fertilizers, it is necessary to accurately monitor and warn the occurrence of diseases during agricultural production. Experts at home and abroad have carried out a series of thermal infrared imaging technology to detect crop disease research. The importance of data interpretation in phenotyping research cannot be overstated. With the continuous development of phenotyping platforms and related technologies at home and abroad, the current plant high-throughput phenotyping platform can obtain massive digital images, point cloud data, spectral imaging and thermal imaging data, and undergo geometric correction, radiometric correction, and data modeling. And a series of processing processes, and finally realize the use of remote sensing means to analyze plant phenotype information. High-throughput phenotyping is expected to become the latest tool for sustainable production under global climate change. However, at present, there are few plant species as the research objects of plant phenotype information, and there is a lack of analysis and identification of plant phenotype information in complex natural environments. The breadth and depth of plant phenotype analysis research must be continuously expanded to establish a unified plant phenotype monitoring system and specifications, form a network resource sharing library, and strengthen mutual cooperation among developers of various phenotype platforms. Using modern mathematical analysis methods, the rapidity and effectiveness of the algorithm can be achieved in image processing and recognition software design to improve the analysis ability of the analysis module of the plant phenotype monitoring system. It is necessary to use multi-domain knowledge to carry out comprehensive control and establish background expert decision-making support systems to automatically monitor and analyze target plants in real time.