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.

Keywords: horticultural crop; image analysis; phenomics; phenotyping; sensor.

Figure 1

Schematic overview of image-based high-throughput phenotyping in horticultural crops. UAP, unmanned aerial platform; MAP, manned aerial platform; RGB, red–green–blue; LiDAR, light detection and ranging; X-ray CT, X-ray computed tomography; MRI, magnetic resonance imaging.

Figure 2

Examples of images from commonly used sensors in high-throughput phenotyping and their spectral range. (a) RGB; (b) NIR [71]; (c) SWIR [72]; (d) thermal (Qubit phenomics, Canada); (e) X-ray [73]; (f) MRI [66]; (g) fluorescence; (h) LiDAR and photogrammetry point cloud (Pix4D S.A., Prilly, Switzerland). RGB and fluorescence images were captured in our lab.

Figure 3

Statistics of image-based high-throughput phenotyping studies in horticultural crops during the past two decades. (a) The number of annual publications related to image-based phenotyping of horticultural crops. (b) Major areas of research using image-based high-throughput phenotyping. (c) Annual number of publications with different search keywords. (d) The type of publications related to image-based high-throughput phenotyping of horticultural crops. (e) Number of image-based high-throughput phenotyping studies by country (top 20). (f) Type of imaging techniques for high-throughput phenotyping of horticultural crops. Note: the data were obtained from the Scopus (https://www.scopus.com) database (Elsevier, The Netherlands) accessed on 21 February 2023. The publications were searched using keywords: horticultural crop, fruit, vegetable, ornamental, flower, and sensor types (RGB, thermal, hyperspectral, multispectral, X-ray CT, MRI, LiDAR, ToF, Raman, ChlF) within the search results of high-throughput phenotyping using image analysis.