Comparative Analysis of Image Binarization Algorithms for UAV-Based Soybean Canopy Extraction Across Growth Stages for Image Labelling

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to develop a methodology for extracting a dataset of soybean canopy that changes according to the degree of soybean growth using different binarization algorithms. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC), Structural Similarity Index Measure (SSIM), and Mean Absolute Error (MAE). The study judged that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices are highly accurate and stable in extracting soybean canopy according to soybean growth when using algorithms such as Adaptive and Otsu. These indices are particularly suitable for extracting soybean canopy from UAV-based data, which can expand the scope of precision analysis in the agricultural sector and provide data for the advancement of precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing.