Smartphone app uses AI to count rapeseed plants 97% accurately in fields
Researchers have developed a smartphone-based system that automatically counts rapeseed seedlings with near-perfect accuracy, dramatically cutting the labor and time required for crop assessment. The technology could help seed breeders and farmers make faster decisions about yield and field management, reducing manual counting costs across large breeding programs.
Originaltitel: Oblique-view video tracking and density-based counting: accurate counting of late-stage rapeseed seedlings for breeding assessment
Accurate counting of late-stage rapeseed seedlings is critical for yield estimation and field management, while traditional manual counting is inefficient and labor-intensive, calling for an automated counting method. A novel video tracking and counting method (CropTriangulator) was proposed, which uses smartphone-captured videos to achieve row-based accurate counting based on oblique view and target density distribution. It integrates three core components: YOLOv11n was selected for its balanced detection accuracy and inference speed after model comparison; an adaptive DBSCAN (AdapDBSCAN) algorithm was designed to eliminate non-target seedlings by dynamically adjusting parameters to address perspective distortion; the SORT algorithm was adopted for tracking and counting, with permanent ID marking to ensure uniqueness when seedlings cross frame boundaries. Experiments on 20 test videos (10 for 45° oblique view, 10 for 90° vertical view) showed that CropTriangulator achieved an average counting accuracy of 97.13% at 45° (14% higher than 90°), with the R-squared of 45° row-based counts reaching 0.917. AdapDBSCAN reduced over-counting compared with fixed-parameter DBSCAN, and SORT had a much lower ID switch rate (8.47%) than DeepSORT (36.05%). The 45° oblique view is proven optimal for rapeseed seedling counting. The proposed CropTriangulator provides a low-cost and efficient solution for automated row-based counting in complex field environments, supporting precise yield estimation and scientific field management decisions. The video comparing the effects of the CropTriangulator method is available at: https://github.com/Possibility007/Comparison-of-counting-results.git.