|FENG, AIJING - University Of Missouri|
|ZHOU, JIANFENG - University Of Missouri|
|Sudduth, Kenneth - Ken|
Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/28/2020
Publication Date: 5/30/2020
Citation: Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2020. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms. Remote Sensing. 12(11):1764. https://doi.org/10.3390/rs12111764.
Interpretive Summary: It is important to assess crop stand counts in the seedling stage to aid in making replanting decisions. While the conventional method of manually counting plants is time consuming and labor intensive, remote sensing can provide an efficient way to obtain information. Unmanned aerial vehicle (UAV) technology has created interest in developing new remote sensing applications for use in precision agriculture, including early determination of plant stand density. In this project, data collected two weeks after planting from a sensor mounted on a UAV were investigated as an estimator of cotton plant density in a research field in Southeast Missouri. Much of the effort concentrated on distinguishing seedlings from soil and weeds, and the results obtained were more accurate than commercial software. This study has demonstrated the potential of using UAV-based imaging to estimate cotton stand density early enough in the season for remedial action. This approach may be useful to researchers and to farmers who are interested in obtaining early-season plant population estimations at relatively low cost and high resolution.
Technical Abstract: Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it di'cult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes e'ciently and build panoramas for each narrow band. The normalized di'erence vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence.