Comparison between Rice Plant Traits and Color Indices Calculated from UAV Remote Sensing Images
Comparison between Rice Plant Traits and Color Indices Calculated from UAV Remote Sensing Images
复制标题
水稻植株性状与无人机遥感图像计算颜色指数的比较
DOI：
10.11450/seitaikogaku.29.11
复制
发表时间：
2017
期刊：
影响因子：
0.3
通讯作者：
大政謙次
中科院分区：
文献类型：
--
作者：
下嶋浩平;小川諭志;内藤裕貴;Milton Orlando Valencia;清水庸;細井文樹;宇賀優作;石谷学;Michael Gomez Selvaraj;大政謙次
利用遥感技术监测植物的生长过程，在加速育种进程方面具有巨大的潜力。本文研究了利用无人机系统进行水稻植株性状表型遥感分析的方法。利用无人机搭载的RGB相机拍摄了水稻生长期、开花期和灌浆期的冠层图像。通过图像处理计算典型颜色指数（r、g、B、INT、VIG、L*、a*、B*、H）。对水稻植株性状（叶面积指数、籽粒产量、地上生物量、株高、穗长、灌浆速率、分蘖数）与色泽指标进行单元回归分析。FW和GF的a* 指数与叶面积指数（决定系数R2> 0.70）和产量（R2> 0.50）呈极显著的线性关系。此外，FW和GF处理的a* 和g与株高和灌浆速率的R2均较大（R2> 0.50）.多元回归分析表明，在三个生育期，约有40%的模型产量与籽粒颜色指数的相关系数大于0.5。特别是VG处的H和INT以及H和L* 的模型密切相关（R2> 0.70）。研究结果表明，无人机彩色遥感图像分析可用于水稻叶面积指数、产量、株高和灌浆速率等4个性状的早期评价，尤其是对产量的估测更有价值。
Remote sensing technology for monitoring plant trains has a huge potential to accelerate breeding process. In this paper, we have studied on remote sensing of using an unmanned aerial vehicle (UAV) system for plant traits phenotyping in rice. The images of rice canopy were taken by a RGB camera from the UAV at three growing stages; Vegetative (VG), Flowering (FW) and Grain filling (GF). Typical color indices (r, g, b, INT, VIG, L*, a*, b*, H) were calculated by image processing. Single regression analysis was conducted between rice plant traits (leaf area index (LAI), grain yield, above ground biomass, plant height, panicle length, grain filling rate, tiller number) and color indices. The index a* at FW and GF had close liner relationships with LAI (the coefficient of determination R 2> 0.70) and grain yield (R 2> 0.50). Moreover, a* and g at FW and GF showed high R 2 with plant height and grain filling rate (R 2> 0.50). The R 2 between grain yield and color indices increased above 0.5 for about 40% of models at three growing stages by multiple regression analysis. In particular, the models of H and INT and of H and L* at VG were closely related (R 2> 0.70). Our findings show the analysis of color images taken by UAV remote sensing is useful to assessing four rice traits; LAI, grain yield, plant height and grain filling rate at early stage, and especially more available for grain yield estimation.