Spatial Resolution as a Factor for Efficient UAV-Based Weed Mapping—A Soybean Field Case Study

Abstract

The influence of spatial resolution on classification accuracy strongly depends on the research object. With regard to unmanned aerial vehicle (UAV)-based weed mapping, contradictory results on the influence of spatial resolution have been attained so far. Thus, this study evaluates the effect of spatial resolution on the classification accuracy of weeds in a soybean field located in Belm, Lower Saxony, Germany. RGB imagery of four spatial resolutions (0.27, 0.55, 1.10, and 2.19 cm ground sampling distance) corresponding to flight altitudes of 10, 20, 40, and 80 m were assessed. Multinomial logistic regression was used to classify the study area, using both pixel- and object-based approaches. Additionally, the flight and processing times were monitored. For the purpose of an accuracy assessment, the producer’s, user’s, and overall accuracies as well as the F1 scores were computed and analyzed for statistical significance. Furthermore, McNemar’s test was conducted to ascertain whether statistically significant differences existed between the classifications. A linear relationship between resolution and accuracy was found, with a diminishing accuracy as the resolution decreased. Pixel-based classification outperformed object-based classification across all the resolutions examined, with statistical significance (p < 0.05) for 10 and 20 m. The overall accuracies of the pixel-based approach ranged from 80 to 93 percent, while the accuracies of the object-based approach ranged from 75 to 87 percent. The most substantial drops in the weed-detection accuracy with regard to altitude occurred between 20 and 40 m for the pixel-based approach and between 10 and 20 m for the object-based approach. While the decline in accuracy was roughly linear as the flight altitude increased, the decrease in the total time required was exponential, providing guidance for the planning of future UAV-based weed-mapping missions.

Keywords

• weed mapping
• precision farming
• UAV
• multinomial logistic regression
• object-based classification