Table 2 Accuracy comparison between active contours, rosette tracker, incremental learning, graph segmentation, and DAPD algorithm
From: Developmental normalization of phenomics data generated by high throughput plant phenotyping systems
Method/algorithm | Accuracy (%) (A1, A2, A3, A4 and our dataset) | |||
|---|---|---|---|---|
Precision | Recall | Jaccard | Dice | |
Active contours | 42.25 (26.05) | 99.66 (26.18) | 42.19 (25.66) | 59.34 (26.20) |
Rosette Tracker | 54.29 (18.11) | 99.97 (17.79) | 54.29 (19.22) | 70.37 (15.00) |
Incremental learning | 89.87 (13.68) | 91.94 (2.96) | 83.90 (14.44) | 89.72 (12.36) |
DAPD | 93.95 (5.42) | 97.03 (6.03) | 90.76 (7.36) | 94.90 (5.78) |