Crop height estimation based on UAV images: methods, errors, and strategies

Authors: XIE, TIANJIN - Huazhong Agricultural University; LI, JIJUN - Huazhong Agricultural University; Yang, Chenghai; JIANG, ZHAO - Huazhong Agricultural University; CHEN, YAHUI - Huazhong Agricultural University; GUO, LIANG - Huazhong Agricultural University; ZHANG, JIAN - Huazhong Agricultural University

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2021
Publication Date: 6/30/2021
Citation: Xie, T., Li, J., Yang, C., Jiang, Z., Chen, Y., Guo, L., Zhang, J. 2021. Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture. 185:106155. https://doi.org/10.1016/j.compag.2021.106155.
DOI: https://doi.org/10.1016/j.compag.2021.106155

Interpretive Summary: Unmanned aerial vehicles (UAVs) have emerged as a promising platform for determining observable crop traits, such as crop height, rapidly and cost-effectively in the field. Accurate estimation of crop height using remotely sensed data typically requires additional information such as ground and crop canopy structure models, known geographical positioning points, and ground truth data of crop height within a field. This study examined the impact of limiting the use of these additional data types on the accuracy and cost of crop height acquisition. Comparing four limited data set based methods showed that while crop height estimation errors varied significantly using normal processing techniques, these errors could be reduced using improved techniques to provide for the data quality required at reduced input costs. The results and improved processes developed in this study provide practical strategies and practices to estimate crop height with a reasonable balance between accuracy and cost for use in scientific research and agricultural production.

Technical Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising platform for determining the dynamic phenotypic traits of crops in the field in a rapid and cost-effective manner. Crop height is a common and important phenotypic trait, and its acquisition with high accuracy usually requires spatial auxiliary (SA) information, such as a digital terrain model in the early growing season, digital surface models later in the season, ground control points, and ground truth of crop height. The reasonable selection of SA information involves balancing the cost and accuracy of crop height acquisition, but this problem has not been systematically studied and it needs to be resolved urgently in the agricultural industry. In this study, we compared four rapeseed height estimation methods using UAV images collected at three growth stages based on the structure from motion algorithm, where one method had complete data and the other three had incomplete SA information. To reduce the crop height estimation errors with incomplete data, improved methods were developed to construct the missing SA information. The optimum results were obtained using complete SA information, where R2 was 0.932 and the root mean square error (RMSE) was 0.026 m. For crop height acquisition using incomplete data, the R2 values could be controlled above 0.445 and RMSE below 0.146 m. In this study, systematic strategies were developed for selecting appropriate methods to acquire crop height with reasonable accuracy while balancing the cost requirement for use in scientific research and agricultural production.