Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 71-83.doi: 10.12133/j.smartag.SA202202004

• Topic--Crop Growth and Its Environmental Monitoring • Previous Articles     Next Articles

Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images

DAI Mian1,2(), YANG Tianle1,2, YAO Zhaosheng1,2, LIU Tao1,2, SUN Chengming1,2()   

  1. 1.Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China
    2.Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
  • Received:2021-07-26 Online:2022-03-30 Published:2022-04-28
  • corresponding author: SUN Chengming E-mail:996982850@qq.com;cmsun@yzu.edu.cn
  • About author:DAI Mian (1998-), female, postgraduate, research interest: intelligent monitoring of crop growth. E-mail: 996982850@qq.com.
  • Supported by:
    The Natural Science Foundation of China(31671615);The Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)


In order to realize the rapid and non-destructive monitoring of wheat biomass, field wheat trials were conducted 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 color 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 values 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 models 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 combined with color and texture feature indices of UAV images were better than the single color index models.

Key words: wheat, UAV?image, color?index, texture?feature?index, biomass, texture index

CLC Number: