An innovative UAV-based approach for estimating crop stand counts amidst weed infestation

• Image processing pipeline precisely measures crop stand counts under weed pressure. • The approach was initially developed on cotton and validated later on maize. • Method minimizes spectral reliance, using edge and row detection for scalability. • The standardized approach can be well adapted to organic or large-scale production. Unmanned aerial vehicles (UAV)-based imagery offers a rapid, cost-effective solution for estimating stand counts in row crops. However, most existing algorithms assume clean crop rows and do not account for common weed infestation. This research developed an image processing pipeline to estimate crop stand counts in both high and low weed pressure conditions. The methodology was initially developed and tested on cotton using UAV imagery collected at three ground sampling distances (GSD) of 1.4, 2.1, and 2.8 cm, across three post-emergence stages: 9, 13, and 21 days after emergence (DAE) in 2022. The approach was later validated on maize during the 2023-2024 growing seasons. The pipeline integrated edge detection, row detection, and geometrical properties while minimizing reliance on spectral characteristics. Results showed that the methodology was highly effective for stand count estimations in both high and low weed pressure situations. Highest accuracies were achieved at low GSDs and early DAE stages, with an R 2 of 0.80-0.85 between 9-13 DAE at 1.4 cm GSD for heavily weed infested cotton, and R 2 of 0.94-0.97 between 12-17 DAE at 1.8 cm GSD for maize under minimal weed infestation. Flights at higher GSD (2.1 cm) performed reasonably well (R 2 = 0.76) at early stages but showed a decline in accuracy with aggressive weed growth and overlapping cotton seedlings beyond 9 DAE. This novel standardized and scalable approach holds significant potential for improving UAV-based stand count assessments across various crops, particularly in organic and large-scale production systems.