智慧农业（中英文）2022，Vol.4Issue(1)：71-83,13.DOI:10.12133/j.smartag.SA202202004
基于无人机图像颜色与纹理特征的小麦不同生育时期生物量估算
Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images
摘要
Abstract
In order to realize the rapid and non-destructive monitoring of wheat biomass, field wheat trials were con-ducted based on different densities, nitrogen fertilizers and varieties, and unmanned aerial vehicle (UAV) was used to obtain RGB images in the pre-wintering stage, jointing stage, booting stage and flowering stage of wheat. The col-or and texture feature indices of wheat were obtained using image processing, and wheat biomass was obtained by manual field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the suitable feature index for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, the val-ues of r were between 0.463 and 0.911 (P<0.05). However, the correlation between image texture feature index and wheat biomass was poor, only 5 index values reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture features in different growth stages were used to construct estimation model of wheat biomass. The mod-els were validated using independently measured biomass data, and the correlation between simulated and measured values reached the extremely significant level (P<0.01), and root mean square error (RMSE) was smaller. The R2 of color index model in the four stages were 0.538, 0.631, 0.708 and 0.464, and RMSE were 27.88, 516.99, 868.26 and 1539.81 kg/ha, respectively. The R2 of the model combined with color and texture index were 0.571, 0.658, 0.753 and 0.515, and RMSE were 25.49, 443.20, 816.25 and 1396.97 kg/ha, respectively. This indicated that the estimated results using the models were reliable and accurate. It also showed that the estimation models of wheat biomass com-bined with color and texture feature indices of UAV images were better than the single color index models.关键词
小麦/无人机图像/颜色指数/纹理特征指数/生物量/纹理指数Key words
wheat/UAV image/color index/texture feature index/biomass/texture index分类
农业科技引用本文复制引用
戴冕,杨天乐,姚照胜,刘涛,孙成明..基于无人机图像颜色与纹理特征的小麦不同生育时期生物量估算[J].智慧农业（中英文）,2022,4(1):71-83,13.基金项目
The Natural Science Foundation of China(31671615,31701355,31872852) （31671615,31701355,31872852）
The Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) （PAPD）
