Image-Based High-Throughput Phenotyping in Horticultural Crops

Abstract

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Climate Change—The Rise of Climate-Resilient Crops

“…High-throughput phenotyping platforms enable the consideration of morphological and physiological traits such as green area indexes (GAIs), chlorophyll content, nitrogen content, plant density at emergence, ear density, grain number and size, fraction of absorber photosynthetically active radiation (FAPAR), staygreen/senescence, crop dynamics monitoring, phenology, canopy coverage, plant health, canopy height, canopy temperature, leaf rolling, leaf angle, leaf wilting, lodging, chlorophyll fluorescence, photosynthetic status, biomass, water content, grain quality, water use efficiency, canopy structure, weed infestation, light use efficiency, nitrogen use efficiency, root development and yield [ 45 , 49 , 50 ]. Progress in HTP is attributed to the rapid development of sensor technologies, including red–green–blue (RGB), multispectral, hyperspectral, and thermal cameras; photosynthesis and fluorescence sensors; stereo cameras; and LiDAR devices [ 49 , 51 , 52 , 53 , 54 ]. For phenotyping, ground- and aerial-based platforms can be used, with sensors installed on stationary or mobile platforms, including handheld devices.…”

Unraveling the genetic and molecular basis of heat stress in cotton

“…In a recent study Chen et al (2019) used high-throughput phenotyping to describe the physiological consequences of heat and drought stress at the early flowering stage of Brassica rapa , which identified corbooxylation, phosphate use and flower volume as stress tolerance associated traits ( Chen et al, 2019 ). Hyperspectral imaging of Korean ginseng proved to significantly differentiate the heat tolerant and susceptible and genotypes with maximum precision ( Abebe et al, 2023 ). Arabidopsis hsp101 mutants were exposed to heat stress and phenotype with high-throughput phenotyping system to monitor daily changes in plant morphology and photosynthetic activity in response.…”

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning Models

“…Although the evaluation of lettuce composition is important for determining its overall quality, laboratory analyses are slow and costly. Therefore, more rapid, less expensive, and non-destructive approaches based on (hyper)spectral reflectance are being developed to assess plant phenotypes [13], response to stress [14], and postharvest quality [15]. Recently, substantial progress has been made in using reflectance data to estimate lettuce quality and composition, including pH value and the content of chlorophyll, anthocyanins, soluble solids, water, nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) [16][17][18][19][20][21][22][23][24][25].…”