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

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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

戴 冕1,2(),杨天乐1,2姚照胜1,2,刘 涛1,2,孙成明1,2()   

  1. 1.扬州大学 农学院,江苏省作物遗传生理重点实验室/江苏省作物栽培生理重点实验室,江苏 扬州 225009
    2.扬州大学,江苏省粮食作物现代产业技术协同创新中心,江苏 扬州 225009
  • 收稿日期:2021-07-26 出版日期:2022-03-30 发布日期:2022-04-28

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)

摘要:

为实现小麦生物量田间快速无损监测,开展基于不同密度、氮肥和品种处理的田间试验,应用无人机获取小麦越冬前期、拔节期、孕穗期和开花期4个时期的RGB图像,通过影像处理获取小麦颜色指数和纹理特征参数,并同时期通过田间取样获取小麦生物量;分析不同颜色指数和纹理特征参数与小麦生物量的关系,筛选出适合小麦生物量估算的颜色和纹理特征指数。结果表明,不同时期图像颜色指数和小麦生物量均有较高的相关性,且大部分达到极显著相关水平;图像纹理特征指数与小麦生物量的相关性较差,只有少数指标达到显著或极显著相关水平。基于上述结果,研究利用相关性最高的颜色指数或颜色指数与纹理特征指数结合构建小麦不同生育时期的生物量估算模型,并通过独立的实测生物量数据对模型进行了验证,模型模拟值与实测值之间的相关性均达到了极显著水平(P<0.01),RMSE均较小。其中,颜色指数模型在4个时期的R2分别为0.538、0.631、0.708和0.464,RMSE分别为27.88、516.99、868.26和1539.81 kg/ha。而颜色和纹理指数结合的模型在4个时期的R2分别为0.571、0.658、0.753和0.515,RMSE分别为25.49、443.20、816.25和1396.97 kg/ha,说明模型估算的结果是可靠的,且精度较高。同时结合无人机图像颜色和纹理特征指数的小麦生物量估测模型的效果要优于单一颜色指数模型。研究可为小麦田间长势实时监测与生物量估算提供新的手段。

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

Abstract:

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

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