|FENG, AIJING - University Of Missouri|
|ZHANG, MEINA - Jiangsu Academy Agricultural Sciences|
|Sudduth, Kenneth - Ken|
|ZHOU, JIANFENG - University Of Missouri|
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/25/2019
Publication Date: 4/15/2019
Citation: Feng, A., Zhang, M., Sudduth, K.A., Vories, E.D., Zhou, J. 2019. Cotton yield estimation from UAV-based plant height. Transactions of the ASABE. 62(2):393-403.
Interpretive Summary: Remote sensing can provide 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 management and marketing 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; however, the procedure required a large amount of data processing that would need to be automated to make the method applicable for more users. This study has demonstrated the potential for using UAV-based plant-height mapping to estimate within-field yield variation in precision agriculture. As additional methods for automation of data processing are developed, 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, existing in-field methods for estimating crop yield are not efficient. The goal of this research was to evaluate the performance of a UAV-based remote sensing system with a low-cost RGB camera to estimate yield of cotton based on plant height. The UAV system took images at 50 m above ground level over a cotton field during the cotton flowering period. 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 (DEM) of the field that was used to extract plant height by calculating the difference in elevation between crop canopy and bare soil surface. Twelve ground reference points with known height were deployed in the field to validate the UAV-based height measurement. Geo-referenced yield data were aligned to the plant height map based on image features. Correlation analysis between yield and plant height was conducted row-by-row with and without row registration. Pearson correlation coefficients between yield and plant height with row registration for all individual rows were in the range of 0.66 to 0.96, which was higher than those without row registration (0.54 to 0.95). A linear regression model using plant height was able to estimate yield with root mean square error of 550 kg ha-1 and mean absolute error of 420 kg ha-1. Locations with low yield were analyzed to identify the potential reasons, and it was found that water stress and coarse soil texture, as indicated by low soil apparent electricity conductivity (ECa), contributed to the low yield. The findings indicate that the UAV-based remote sensing system equipped with a low-cost digital camera was able to estimate cotton yield with acceptable errors and monitor plant growth status.