We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface (‘TillerCounter’ program) allows adjusting the automatic detection interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting. The key contributions of our work include: 1. An efficient method for imaging straw tillers based on bundle assembly. 2. An extensive study of the obtained image quality and comparison with the ground truth data from manual counting. 3. Demonstration of the approach’s high accuracy using correlation analysis (Pearson correlation coefficient R=0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5%).
Saravanan et al. (Sun,) studied this question.