High-throughput phenotyping in cotton: a review

Abstract

Recent technological advances in cotton (Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis. High-throughput phenotyping (HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth, yield, and adaptation to biotic or abiotic stress. Researchers have conducted extensive experiments on HTP and developed techniques including spectral, fluorescence, thermal, and three-dimensional imaging to measure the morphological, physiological, and pathological resistance traits of cotton. In addition, ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems. This review paper highlights the techniques and recent developments for HTP in cotton, reviews the potential applications according to morphological and physiological traits of cotton, and compares the advantages and limitations of these HTP systems when used in cotton cropping systems. Overall, the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton. However, because of its relative novelty, HTP has some limitations that constrains the ability to take full advantage of what it can offer. These challenges need to be addressed to increase the accuracy and utility of HTP, which can be done by integrating analytical techniques for big data and continuous advances in imaging.

Cotton phenotyping and physiology monitoring with a proximal remote sensing system

“…As might be expected, some areas of high-throughput phenotyping are more developed than others and development differs among crops (Araus et al, 2018;Yang et al, 2020). Development of sensor-based phenotyping systems for cotton (Gossypium hirsutum L.) has been hampered by the relatively complex indeterminate growth habit of the plant, with wide variation in physical, spatial, and temporal patterns of growth and yield development (Pabuayon, Yazhou, Wenxuan, & Ritchie, 2019;Sharma, Mills, Snowden, & Ritchie, 2015;Xu, Li, & Paterson, 2019). Pabuayon et al (2019) reviewed the scientific literature on applications of various phenotyping platforms for cotton and compared platforms.…”

“…A variety of sensor-based phenotyping technologies and approaches are currently being developed and improved for all major and some minor agronomic crops, including both proximal (ground-based) and aerial systems (Araus, Kefau-The potential simplicity of proximal systems can also be an advantage to many users, as proximal sensor platforms can be homemade (e.g., carts) or existing equipment implements can be adapted to become phenotyping platforms (Andrade-Sanchez et al, 2014;Thompson et al, 2018). Disadvantages of proximal systems can include challenges with maneuverability on the ground, the relatively large time investment to collect data over large areas, and potential damage to the crop once the canopy closes (Pabuayon et al, 2019). Both platform types offer potential for successful use in cotton, and we have focused on proximal systems in this research.…”

“…Pabuayon et al. (2019) reviewed the scientific literature on applications of various phenotyping platforms for cotton and compared platforms. When compared with aerial systems, proximal phenotyping systems have the advantages of good resolution, minimal bias in sample selection, and relatively little disruption due to atmospheric conditions (e.g., wind).…”

Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing

“…While there are many different types of sensors that can be used on the robots for plant high throughput phenotyping, such as color, multispectral, hyperspectral, thermal cameras [19][20][21][22], Light Detecting and Ranging (LiDAR) sensors are one of the most widely used sensor systems in robotic platforms because they are less sensitive to ambient illumination and they can give accurate distance measurements without contact. LiDAR is being used increasingly in the field to generate 3D point clouds of crops for phenotypic analysis [23] as well as low-cost crop navigation [24]. With a 2D LiDAR, point clouds can be generated to determine important phenotypic traits of plants, such as canopy height and plant volume [25].…”

Analysis of cotton height spatial variability based on UAV-LiDAR

“…Farmland management includes the regulation of soil moisture, nutrition, pests, etc. These conditions will affect the growth of crops, mainly reflected by changes in crop morphological characteristics [1] , physical characteristics [2] and physiological characteristics [3,4] . The crop heights are different in different areas of the cotton field, so the acquisition of crop characteristics information has become the information base for adjusting farmland management strategies.…”