Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms

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 difficult 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 efficiently and build panoramas for each narrow band. The normalized difference 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.