Development of Automated High-Throughput Phenotyping System for Controlled Environment Studies

Abstract.

Plant breeding has significantly improved in recent years, however, phenotyping remains a bottleneck as the process is often expensive, subjective, and laborious. Although commercial phenotyping systems are available, factors like cost, space, and need for specific controlled environment limit the affordability of these commercial phenotyping systems. A low-cost, accurate, and high-throughput phenotyping (HTP) system is highly desirable to plant breeders, physiologists, and agronomists. To solve the problem, an automated system for HTP and image processing algorithms were developed and tested. The automated platform was an integration of an aluminum framework (including two stepper motors and control components), three cameras, and a laptop. A control program was developed using LabVIEW to manage the operation of platform and sensors together. Image processing algorithms were developed in MATLAB for high-throughput analysis of images acquired by the system for estimating phenotypes/traits associated with tested plants. The phenotypes extracted were color, texture, temperature, morphology, and greenness features on a temporal scale. Using two wheat lines known with heat tolerance, the system was validated. Validation studies revealed that features such as green leaf area and green normalized difference vegetation index derived from our system showed differences between control and heat stress treatment, as well as between heat tolerant and susceptible wheat lines. This study demonstrated successful development and implementation of a low-cost automated system with custom algorithms for HTP. Improvement of such systems would further help plant breeders, physiologists, and agronomists to phenotype crops and accelerate plant breeding.