• ISSN 1008-505X
  • CN 11-3996/S
张玲, 陈新平, 贾良良. 基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J]. 植物营养与肥料学报, 2018, 24(1): 261-269. DOI: 10.11674/zwyf.17193
引用本文: 张玲, 陈新平, 贾良良. 基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究[J]. 植物营养与肥料学报, 2018, 24(1): 261-269. DOI: 10.11674/zwyf.17193
ZHANG Ling, CHEN Xin-ping, JIA Liang-liang. Parameter research of using UAV-based visible spectral analysis technology in dynamical diagnosis of nitrogen status of summer maize[J]. Journal of Plant Nutrition and Fertilizers, 2018, 24(1): 261-269. DOI: 10.11674/zwyf.17193
Citation: ZHANG Ling, CHEN Xin-ping, JIA Liang-liang. Parameter research of using UAV-based visible spectral analysis technology in dynamical diagnosis of nitrogen status of summer maize[J]. Journal of Plant Nutrition and Fertilizers, 2018, 24(1): 261-269. DOI: 10.11674/zwyf.17193

基于无人机可见光遥感的夏玉米氮素营养动态诊断参数研究

Parameter research of using UAV-based visible spectral analysis technology in dynamical diagnosis of nitrogen status of summer maize

  • 摘要:
    目的 近年来应用无人机进行作物生长、营养和植保信息的快速提取受到广泛关注,但其对作物全生育期营养状况的动态诊断需要明确适宜的色彩参数。本研究通过田间氮水平试验,以无人机为平台利用可见光光谱对夏玉米不同生育期的冠层氮素营养进行监测,对基于可见光RGB图像的色彩参数与传统氮素诊断指标的相关性进行分析,并比较色彩参数的变异系数以探明夏玉米不同生育时期氮素营养诊断的最佳色彩参数。
    方法 于2015年6—10月,在河北省中国农业大学曲周试验基地设置不同氮水平田间试验,以夏玉米郑单958为供试作物,设5个施氮水平:0、102、145、189和250 kg/hm2 (分别以CK、70%OptN、OptN、130%OptN、ConN表示),4次重复。分别在夏玉米六叶期 (V6)、十叶期 (V10)、吐丝期 (VT)、籽粒建成期 (R2)、乳熟期 (R4) 应用无人机可见光遥感技术获取夏玉米冠层图像,采用Adobe Photoshop软件经过一些图像处理后选用直方图程序提取图像的红光值R、绿光值G、蓝光值B、亮度值L,研究由此计算的12个色彩参数与传统氮素诊断指标 (植株氮浓度、生物量和吸氮量) 的相关性,结合相关系数和变异系数的大小综合分析筛选夏玉米不同生育时期氮素营养诊断的最佳色彩参数。
    结果 红光值 (R)、绿光值 (G)、亮度值 (L)、绿光标准化值G/(R + G + B)、蓝光标准化值B/(R + G + B)、绿光与红光的比值 (G/R)、绿光与蓝光的比值 (G/B)、绿光与亮度的比值 (G/L)、红绿蓝植被指数 (RGBVI) 等在不同生育时期均与夏玉米的植株氮浓度、生物量和吸氮量有很好且一致的相关性,结合图像色彩参数的变异系数综合分析后,G/(R + G + B)、G/L在各生育时期与夏玉米常规的氮营养诊断指标有极显著的相关性 (P < 0.01),相关系数介于0.641~0.944之间,且变异系数小而稳定,介于0.93%~4.30%之间,优于其他光谱参数,可作为基于无人机可见光技术用于各时期氮素营养动态诊断的最佳色彩参数。
    结论 应用无人机可见光遥感进行夏玉米氮素营养动态诊断具有结果可靠、便捷、高效、非破坏性的优点,本研究结果对应用该技术进行较大区域的作物氮素营养动态诊断提供了科学依据。

     

    Abstract:
    Objectives Unmanned aerial vehicle (UAV) shows superiority in obtaining canopy information related to crops growth, nutrition and plant protection recently. For the practical application of this method in the diagnosis of dynamical nitrogen (N) status, a serious of visible spectrum parameters are need to set up at different stages of summer maize.
    Methods Field experiment was conducted from June to October in 2015 at the Quzhou Research Base of China Agricultural University in Hebei Province. The summer maize Zhengdan958 was chosen as test cultivar. Five N treatments of N 0, 102, 145, 189 and 250 kg/hm2 was designed and recorded as CK, OptN70%, OptN, OptN130%, ConN, respectively. The experiment has four replicates. The canopy images of summer maize at 6-leaf stage (V6) , 10-leaf stage (V10), tasseling stage (VT), blister stage (R2), filling stage (R4) were captured by using UAV-based visible spectrum technique. The canopy images were processed with Adobe Photoshop to extract color information of red (R), green (G), blue (B) and light (L) intensity. The correlation between the monitored 12 visible spectrum parameters calculated from R, G, B, L and plant N concentration, biomass and N uptake, the coefficient of variation (%) of visible spectrum parameters were researched to determine the best visible spectrum parameters for N status diagnosis of summer maize at different stages.
    Results Field data showed that the redness intensity (R), greenness intensity (G), lightness intensity (L), normalized greenness intensity G/(R + G + B), normalized blueness intensity B/(R + G + B), the ratio of greenness and redness (G/R), the ratio of greenness and blueness (G/B), the ratio of greenness and lightness (G/L), the RGB vegetation index (RGBVI) had significant relationships with N concentration, N uptake and as well as plant biomass at all stages. Taking the coefficient of variation of visible spectrum parameters account, G/(R + G + B), G/L had higher correlation coefficient (r values 0.641–0.944) and smaller and stable coefficient of variation, ranged from 0.93% to 4.30%, compared with other parameters. So G/(R+G+B) and G/L can be the best visible spectrum parameters for N status diagnosis of summer maize at different stages.
    Conclusions The UAV-based visible spectrum technique is usable in detecting N status dynamically and has the advantages of stable results, convenient and fast, high-efficiency and non-destructive. This study provides a scientific basis for promoting the application of UAV-based visible spectrum technique to diagnose crops N status dynamically on large scales.

     

/

返回文章
返回