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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #376976
Research Project: Molecular Genetic and Proximal Sensing Analyses of Abiotic Stress Response and Oil Production Pathways in Cotton, Oilseeds, and Other Industrial and Biofuel Crops

Location: Plant Physiology and Genetics Research

Title: Upland cotton (Gossypium hirsutum L.) fuzzy seed counting by image analysis

Author
item Herritt, Matthew
item JONES, DON - Cotton, Inc
item Thompson, Alison

Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/12/2020
Publication Date: 10/1/2020
Citation: Herritt, M.T., Jones, D., Thompson, A.L. 2020. Upland cotton (Gossypium hirsutum L.) fuzzy seed counting by image analysis. Journal of Cotton Science. 24:112-120.

Interpretive Summary: This study describes an inexpensive, quick, and accurate method to quantify cottonseed traits from fuzzy seed for use in breeding programs. Analysis using the fuzzy seed imaging method identified less variation and fewer outliers in the cotton imaged seed index compared the traditional seed index which indicates the imaging method is less prone to human error. The imaging method also provides opportunities to calculate seed characteristics that were previously too time intensive to consider in large breeding programs.

Technical Abstract: In recent years cottonseed size has reduced as a result of the substantial fiber yield increases cotton breeders have made. Small cottonseed size has been associated with reduced germination, low seedling vigor and stand establishment, and creates production problems for downstream whole seed users. The potential loss in revenue to the cotton industry, due to small seed size, is substantial and has prompted a renewed effort by breeders to generate high-yielding, high-quality varieties with increased seed size. To aid these efforts and enable a better understanding of the effects of seed characteristics on fiber, a fuzzy seed imaging method was developed. The method utilizes inexpensive, off-the-shelf equipment and an open source image processing pipeline to derive the number of seed, seed index, seed area, height, width, and perimeter. The time to image the seed and process the image takes less than three minutes per sample on average. The seed counts and seed index were strongly correlated with manual measurements at r = 0.967 and 0.693 respectively. Associations among the seed characteristics and fiber indicate seed area, when used to calculate lint density could be a useful selection criterion for breeders to increase both yield and seed size.