Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan.
Sensors (Basel). 2022 Jul 25;22(15):5547. doi: 10.3390/s22155547.
The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive stages under flooded conditions. Unlike popular deep-learning-based approaches requiring training data and computational resources, we propose a simple image-processing pipeline following the empirical principles of synchronously emerging leaves and tillers in rice morphogenesis. Field images were taken by an unmanned aerial vehicle at a very low flying height for UAV imaging-1.5 to 3 m above the rice canopy. Subsequently, the proposed image-processing pipeline was used, which includes binarization, skeletonization, and leaf-tip detection, to count the number of long-growing leaves. The tiller number was estimated from the number of long-growing leaves. The estimated tiller number in a 1.1 m × 1.1 m area is significantly correlated with the actual number of tillers, with 60% of hills having an error of less than ±3 tillers. This study demonstrates the potential of the proposed image-sensing-based tiller-counting method to help agronomists with efficient, non-destructive field phenotyping.
水稻分蘖数的增加显著影响着粮食产量。然而，这通常只能通过手动计数刚长出的分蘖来衡量，最常见的方法是人工触摸计数。本研究开发了一种在淹水条件下，用于估算营养生长和生殖生长阶段分蘖数的高效、无损的方法。与需要训练数据和计算资源的流行的基于深度学习的方法不同，我们提出了一种简单的图像处理流水线，遵循水稻形态发生中同步出现的叶片和分蘖的经验原则。田间图像由无人机在非常低的飞行高度拍摄，用于无人机成像——距离水稻冠层 1.5 到 3 米。随后，使用提出的图像处理流水线，包括二值化、骨架化和叶尖检测，来计数长生长叶的数量。从长生长叶的数量估算分蘖数。在 1.1 m×1.1 m 的区域内估算的分蘖数与实际分蘖数显著相关，60%的丘块误差小于±3 个分蘖。本研究表明，基于图像感应的分蘖计数方法具有帮助农学家进行高效、无损田间表型分析的潜力。
