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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #354264

Research Project: Improving Irrigation Management and Water Quality for Humid and Sub-humid Climates

Location: Cropping Systems and Water Quality Research

Title: Cotton yield estimation based on plant height from UAV-based imagery data

item FENG, AIJING - University Of Missouri
item Sudduth, Kenneth - Ken
item Vories, Earl
item ZHANG, MEINA - University Of Missouri
item ZHOU, JIANFENG - University Of Missouri

Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/14/2018
Publication Date: 7/29/2018
Citation: Feng, A., Sudduth, K.A., Vories, E.D., Zhang, M., Zhou, J. 2018. Cotton yield estimation based on plant height from UAV-based imagery data. ASABE Annual International Meeting, July 29-August 1, 2018, Detroit, Michigan. Paper No. 1800483. doi:10.13031/aim.201800483.

Interpretive Summary: Remote sensing can be an efficient way to obtain information about spatial variation across fields and landscapes. Recent availability of unmanned aerial vehicle (UAV) technology has created interest in developing new remote sensing applications for use in precision agriculture. Within-season estimation of crop yield would allow farmers to make better-informed management decisions before harvest, potentially improving their profitability. In this project, cotton height as measured by a UAV was investigated as an estimator of cotton yield in a research field in Southeast Missouri. Results were promising, with a mapped yield estimation error of less than 10%. However, this level of accuracy required a large amount of “hands-on” data processing which would need to be automated to make the method of practical use. This study has demonstrated the potential for using UAV-based plant-height mapping to estimate within-field yield variation in precision agriculture. With additional automation of data processing, this approach may be useful to researchers and to farmers who are interested in obtaining within-season yield information at relatively low cost and high resolution.

Technical Abstract: Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, conventional methods of using ground sensing to estimate crop yield are not efficient. The goal of this research was to evaluate the potential of using a UAV-based remote sensing system with a low-cost RGB camera to estimate yield of cotton within season. The UAV system took images at 50 m above ground level over a cotton field at the growth stage of first flower. Waypoints and flight speed were selected to allow > 70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model of the field, which was then used to map plant height by calculating the difference in elevation between the crop canopy and the bare soil surface. Twelve ground control points (calibration objects) with known GPS coordinates and height were deployed in the field and were used as check points for geo-referencing and height calibration. Geo-referenced yield data were registered with the plant height map row-by-row. Correlation analysis between yield and plant height was conducted row-by-row both with and without row registration. With row registration, Pearson correlation coefficients between yield and plant height for individual rows were in the range of 66% to 96%, higher than those without row registration (54% to 95%). A non-parametric regression used for building a yield estimation model based on image-derived plant height was able to estimate yield with less than 10% error (root mean square error of 360.4 kg ha-1 and mean absolute error of 180.9 kg ha-1). The results indicated that the UAV-based remote sensing system equipped with a low-cost digital camera was able to estimate cotton yield with acceptable errors.