A low‐cost high‐throughput phenotyping system for automatically quantifying foliar area and greenness

Abstract

Premise: With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness. Methods: During a greenhouse experiment on the effects of abiotic stress on Brassica rapa, we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000. Results: In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not. Discussion: These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.

Rapid and low-cost screening for single and combined effects of drought and heat stress on the morpho-physiological traits of African eggplant (Solanum aethiopicum) germplasm

“…We designed an ambient and heat growth chamber using locally available materials ( Fig 1A ) to screen resilient eggplant genotypes to heat, drought, and concurrent heat and drought stress. This low-cost system is noteworthy because reducing the cost of research on genotype-phenotype relationships with an aim toward crop improvement will help researchers in disadvantaged areas improve crop yield without substantial investments in technology and expertise and contribute to global food security [ 112 ]. Moreover, the critical criteria for developing rapid screening methods are that the technique must be able to evaluate plant performance at critical stages of development, be inexpensive, and screen many plants in as short a time as possible [ 113 ].…”

Quantifying the Effect of Light Intensity Uniformity on the Crop Yield by Pea Microgreens Growth Experiments

“…High-speed automated procedures have been developed for 3D characterization of the lighting environment [27][28][29][30] as well as for quick phenotyping and monitoring of plant growth [9,15,30,31]. With the aid of a high-throughput experimental unit, one can efficiently screen a vast array of parameter settings and extract the transfer function linking growth traits to environmental parameters.…”

High-Throughput Plant Phenotyping System Using a Low-Cost Camera Network for Plant Factory

“…Although An et al [23] adopted the calibration of multiple-camera array and utilized it for plant phenotyping, the post-data collection and -processing made it difficult to access and analyze data in real time. Thrash et al [24] also proposed a low-cost wireless phenotyping system, but it was only developed for a single index and a horizontal space. Gang et al [25] developed a novel convolutional neural network-based model that can estimate various morphologic indices for plants grown in greenhouses, but its feasibility was not validated in an integrated phenotyping platform.…”