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ARS Home » Pacific West Area » Pullman, Washington » Grain Legume Genetics Physiology Research » Research » Publications at this Location » Publication #317471

Research Project: Genetic Improvement of Cool Season Food Legumes

Location: Grain Legume Genetics Physiology Research

Title: Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Author
item Khot, Lav - Washington State University
item Zuniga, Carlos - Washington State University
item Jarolmasjed, Sanaz - Washington State University
item Sathuvalli, Vidyasagar - Oregon State University
item Vandemark, George
item Miklas, Phillip - Phil
item Carter, Aaron - Washington State University
item Pumphrey, Michael - Washington State University
item Knowles, Richard - Washington State University
item Pavek, Mark - Washington State University

Submitted to: European Journal of Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2015
Publication Date: 8/16/2015
Citation: Khot, L., Zuniga, C., Jarolmasjed, S., Sathuvalli, V., Vandemark, G.J., Miklas, P.N., Carter, A., Pumphrey, M., Knowles, R., Pavek, M. 2015. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy. 70:112-123.

Interpretive Summary: Plant breeders typically develop new crop varieties by selecting parents that have traits that the breeders would like to see in new varieties, such as improved disease resistance, higher yields, or better nutritional qualities. The most challenging aspect of the process of plant breeding is selecting parents that have good traits because of their genes, as opposed to parents that have good traits because they were grown in an environment that favored the development of good traits. Only traits that are strongly controlled by genes will be reliably expressed in new varieties. Many important traits, such as early season plant vigor, are difficult to reliably classify and their classification may be very time consuming. In this report we describe advances in the use of unmanned aerial systems (UAV) to evaluate plants for several important traits including early season plant vigor, yield, and resistance to diseases and drought stress. UAVs are small flying vehicles that are controlled remotely and used to take pictures of plants in the field. The pictures can be analyzed with computer software to identify differences in plant size, plant height, and the temperature at the top (canopy) of the plant. These differences are then compared to differences in conventionally measured traits, such as yield and seed size, to identify traits for which there is a positive relationship between the measured trait and the image data that is collected by the UAV. Using UAVs may help plant breeders to more accurately and rapidly identify plants that they can use as parents to develop improved varieties.

Technical Abstract: Global plant genetics research efforts have focused on developing high yielding, stress tolerant and disease resistant row and field crop varieties that are more efficient in their use of agronomic inputs (water, nutrients, pesticides, etc.). Until recently, a key bottleneck in such research was the lack of high-throughput sensing technologies for effective and rapid evaluation of expressed phenotypes in controlled environments and field conditions for holistic data-driven decision making and variety selection. This review focuses on technological aspects of integrating unmanned aerial vehicles with imaging systems to enhance field phenotyping capabilities. The state-of-the-art of unmanned aerial system (UAS) technology, types of imaging sensors, and data processing and management aspects for various applications including crop emergence, vigor, and characterization of yield potential of row and field crops is reviewed. The potential of using UAS imaging to evaluate resistance/susceptibility to biotic and abiotic stress for crop breeding and precision production management is discussed along with future perspectives and developments.