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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #333312
Title: Harvester-based sensing system for cotton fiber-quality mapping

Author
item SCHIELACK, V - Texas A&M University
item THOMASSON, J - Texas A&M University
item Sui, Ruixiu
item GE, Y - University Of Nebraska

Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/22/2016
Publication Date: 12/30/2016
Citation: Schielack, V.P., Thomasson, J.A., Sui, R., Ge, Y. 2016. Harvester-based sensing system for cotton fiber-quality mapping. Journal of Cotton Science. 20:386-393.

Interpretive Summary: Cotton farm revenue is determined by two major factors, yield and fiber quality, both of which vary significantly across farm fields. Precision agriculture in cotton production attempts to maximize profitability by exploiting information on field spatial variability to optimize the fiber yield and quality. For precision agriculture to be economically viable, collection of spatial variability data within a field must be automated and incorporated into normal harvesting and ginning operations. In collaboration with the Professor at Texas A&M University, Researcher at USDA-ARS Crop Production Systems Research Unit in Stoneville, MS designed and built an automated prototype system that uses image processing to estimate the micronaire value of cotton fiber during harvest. The prototype system developed shows promise for in-situ measurement of cotton fiber quality, specifically micronaire, and can enable creation of fiber quality maps to improve crop management and ultimately profitability.

Technical Abstract: Precision agriculture in cotton production attempts to maximize profitability by exploiting information on field spatial variability to optimize the fiber yield and quality. For precision agriculture to be economically viable, collection of spatial variability data within a field must be automated and incorporated into normal harvesting and ginning operations. An automated prototype system that uses image processing to estimate the micronaire value of cotton fiber during harvest was designed and built. The system was based on a camera with a visible Indium Gallium Arsenide (VisGaAs) detector sensitive to a broad range of visible and near-infrared (NIR) energy. Image processing algorithms were developed to identify foreign matter in the images so that it could be excluded from the measurement of reflectance in three NIR wavebands. After the effects of foreign matter were removed, the NIR reflectance measurements had a strong relationship to standard micronaire measurements, even though the measurements were made on seed cotton, which has a high level of foreign matter compared to fiber samples. A simplified version of the system could be constructed from a similar camera with only three optical band-pass filters at 650, 1550, and 1650 nm. The prototype system developed shows promise for in-situ measurement of cotton fiber quality, specifically micronaire, and can enable creation of fiber quality maps to improve crop management and ultimately profitability.