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ARS Home » Pacific West Area » Pullman, Washington » Grain Legume Genetics Physiology Research » Research » Publications at this Location » Publication #367087

Research Project: Improving Genetic Resources and Disease Management for Cool Season Food Legumes

Location: Grain Legume Genetics Physiology Research

Title: Imaged-based phenotyping of flowering intensity in cool-season crops

item ZHANG, CHONGYAM - Washington State University
item CRAINE, WILSON - Washington State University
item QUIROS, JUAN - Washington State University
item McGee, Rebecca
item Vandemark, George
item DAVIS, JAMES - University Of Idaho
item BROWN, JACK - University Of Idaho
item HULBERT, SCOTT - Washington State University
item SANKARAN, SINDHUJA - Washington State University

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2020
Publication Date: 3/6/2020
Citation: Zhang, C., Craine, W., Quiros, J., McGee, R.J., Vandemark, G.J., Davis, J., Brown, J., Hulbert, S., Sankaran, S. 2020. Imaged-based phenotyping of flowering intensity in cool-season crops. Sensors. 20(5). Article 1450.

Interpretive Summary: The timing of flowering and the length of time that a plant flowers are important factors influencing survival and yield. For many crops including cereals, pulses, oilseeds, and fruits the part of the plant that is consumed as food is directly derived from flowering events. Breeders must be able to evaluate timing and duration of flowering to develop improved varieties that maintain yield despite adverse biological and environmental factors including disease and drought. Unfortunately, most breeders rely on their ability to visually determine timing and duration of flowering, and visual assessment is a subjective, inconsistent, and relatively slow process. Over the past decade great advances have been made in using remote sensing technologies, which typically are cameras mounted to 'drones', to evaluate many plant traits including yield, maturity, and disease resistance. The objective of this study was to determine if remote sensing technologies could be used to estimate timing and duration of flowering. We studied this using four different crops grown in Washington, two oilseed crops, camelina and canola, and two pulse crops, pea and chickpea. We found that flowers could be detected on all four crops using different cameras, but that the simplest camera, a 'red-green-blue' camera such as are on most cell phones, could only detect and measure flowers on canola and pea, which are much larger than flowers of camelina and chickpea. More expensive 'multi-spectral' cameras that can measure light outside the range of normal human vision may be required to use this approach for crops that produce small or inconspicuous flowers.

Technical Abstract: The timing and duration of flowering is a key agronomic trait that is often associated with robustness of a variety in escaping stresses (e.g. drought). Visual assessment is a standard protocol used to determine timing and duration of flowering, which is low-throughput and subjective, and can also limit the frequency of data acquisition. In order to enhance the accuracy and efficiency of assessing flowering traits, this study evaluated various sensing techniques to monitor flowering in four different cool-season crops (canola, camelina, chickpea, and pea) using multiple sensors at different spatial resolution (proximal and remote sensing). Proximal sensing using the consumer-grade RGB and multispectral (NIR-G-B) cameras were able to detect flowers of different color and sizes demonstrating moderate to high correlation coefficients with visual rating (r = 0.36 to 0.89) across all four crops. The RGB camera integrated with unmanned aerial system accurately detected and quantified larger flowers such as of winter canola (r = 0.66 to 0.84), spring canola (r = 0.39 to 0.77), and pea (r = 0.31 to 0.72), but not for camelina or chickpea, which produce smaller flowers. Our results suggest it is possible to use remote sensing techniques to monitor flowers in multiple varieties of different crops. The accuracy of this apprach can be increased by adapting the camera and altitude of data acquisition to flower size and color.