[1]史培华,王远,袁政奇,等.基于冠层RGB图像的冬小麦氮素营养指标监测[J].南京农业大学学报,2020,43(5):829-837.[doi:10.7685/jnau.202001020]
 SHI Peihua,WANG Yuan,YUAN Zhengqi,et al.Estimation of wheat nitrogen nutrition indices in winter wheat based on canopy RGB images[J].Journal of Nanjing Agricultural University,2020,43(5):829-837.[doi:10.7685/jnau.202001020]
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基于冠层RGB图像的冬小麦氮素营养指标监测()
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《南京农业大学学报》[ISSN:1000-2030/CN:32-1148/S]

卷:
43卷
期数:
2020年5期
页码:
829-837
栏目:
植物科学
出版日期:
2020-09-15

文章信息/Info

Title:
Estimation of wheat nitrogen nutrition indices in winter wheat based on canopy RGB images
作者:
史培华1 王远2 袁政奇1 孙青云1 蔡善亚1 陆喜瞻13
1. 江苏农林职业技术学院农学园艺学院, 江苏 句容 212400;
2. 中国科学院南京土壤研究所, 江苏 南京 210008;
3. 扬州大学农学院, 江苏 扬州 215009
Author(s):
SHI Peihua1 WANG Yuan2 YUAN Zhengqi1 SUN Qingyun1 CAI Shanya1 LU Xizhan13
1. Department of Agronomy and Horticulture, Jiangsu Polytechnic College of Agriculture and Forestry, Jurong 212400, China;
2. Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;
3. College of Agriculture, Yangzhou University, Yangzhou 215009, China
关键词:
冠层覆盖度RGB图像氮素营养冬小麦监测
Keywords:
canopy coverageRGB imagenitrogen nutritionwinter wheatmonitoring
分类号:
S363
DOI:
10.7685/jnau.202001020
摘要:
[目的] 本文旨在探索基于冬小麦冠层RGB图像的氮素营养指标实时监测方法,为实现简便、准确的冬小麦氮素营养诊断和推荐施肥奠定基础。[方法] 基于3年次的冬小麦大田氮肥梯度试验,采用数码相机在返青期和拔节期垂直拍摄冠层RGB图像。分析图像特征参数绿红通道比值(G/R)、绿红通道差值(GMR)、红光标准化值(NRI)、绿光标准化值(NGI)、色相(H)和冠层覆盖度(CC)与植株氮素生理指标间的关系,筛选氮素营养监测指标的最优图像特征参数,构建氮素营养指标估算模型。[结果] CC与冬小麦地上部生物量、氮积累量和叶面积指数(LAI)三者间的相关系数最高,分别为0.87、0.85和0.84(P<0.01);其他特征参数与三者间的相关系数相对较低,其中H为0.81、0.77和0.79,NRI为-0.80、-0.77和-0.77,G/R为0.73、0.63和0.76,GMR为0.66、0.67和0.63。采用CC作为冬小麦氮素营养指标估算模型的输入参数,并分别使用异速生长函数和指数函数建立地上部生物量、氮积累量和LAI估算模型,异速生长函数这3个指标的估算模型R2分别为0.82、0.76和0.82(P<0.01),指数函数的R2分别为0.80、0.74和0.85(P<0.01)。利用独立试验数据对模型进行验证,异速生长函数模型预测值和观测值间的R2平均为0.89(P<0.01),地上部生物量、氮积累量和LAI预测值的均方根误差(RMSE)分别为31.09 g·m-2、1.37 g·m-2和0.16;指数函数模型预测值和观测值间的R2平均也为0.89(P<0.01),地上部生物量、氮积累量和LAI预测值的RMSE分别为28.95 g·m-2、1.34 g·m-2和0.17。[结论] 异速生长函数和指数函数模型在利用CC对冬小麦氮素营养指标进行估算时均具有较好的预测性。基于RGB图像的监测方法操作简单、准确度高,可实时获取监测结果,具有较高的推广应用价值。
Abstract:
[Objectives] This study is expected to explore the real-time estimation of nitrogen (N) nutrition indices based on winter wheat canopy RGB images,which will lay a foundation for the simple and accurate diagnosis of N status and the recommendation of fertilization.[Methods] Based on 3 years/varieties N gradient trials of winter wheat in the fields,canopy RGB images were taken by a digital camera at returning green stage and jointing stage. The relationships were analyzed between image feature parameters (the ratio of green and red channel,G/R;the difference of green and red channel,GMR;normalized redness intensity,NRI;normalized greenness intensity,NGI;hue,H;canopy coverage,CC) and crop N-related indices (shoot dry matter,shoot N accumulation and leaf area index). The N nutrition estimation models were established based on the optimal image feature parameters.[Results] The image feature para-meter CC (canopy coverage) had the highest correlation coefficients with shoot dry matter,shoot N accumulation and LAI in winter wheat,which reached 0.87,0.85 and 0.84 (P<0.01),respectively,compared with image color feature parameters H (0.81,0.77 and 0.79,respectively),NRI (-0.80,-0.77 and -0.77,respectively),G/R (0.73,0.63 and 0.76,respectively) and GMR (0.66,0.67 and 0.63,respectively) (P<0.01). Since the exponential relationships were found between CC and crop N-related indices,both allometric function and exponential function were chosen to establish the shoot dry matter,shoot N accumulation and LAI estimation models with CC. The determination coefficient (R2) in allometric function were 0.82,0.76 and 0.82 (P<0.01) for shoot dry matter,shoot N accumulation and LAI,respectively,and R2 in exponential function were 0.80,0.74 and 0.85 (P<0.01),respectively. Calibration was conducted on the two models with an independent dataset. The root mean square errors (RMSE) of shoot dry matter,shoot N accumulation and LAI in the allometric model were 31.09 g·m-2,1.37 g·m-2 and 0.16,respectively,with an average R2 of 0.89 (P<0.01) between observed values and predicted values. The RMSE of shoot dry matter,shoot N accumulation and LAI in the exponential model were 28.95 g·m-2,1.34 g·m-2 and 0.17,respectively,with the same average R2 of 0.89 (P<0.01) between observed values and predicted values.[Conclusions] These results indicate that allometric model and exponential model both give good predictions on N-related indices in winter wheat by using CC. The method of using CC in canopy RGB images for N-related indices estimation in winter wheat is simply operating with high accuracy and can obtain results in real-time,which has the potential of broadly application.

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备注/Memo

备注/Memo:
收稿日期:2020-01-11。
基金项目:江苏省自然科学基金青年基金项目(BK20170586);国家自然科学基金青年科学基金项目(31701994);江苏农林职业技术学院基金扶持类项目(2017kj07);江苏农林职业技术学院产业创新团队项目(2019kj001)
作者简介:史培华,博士,讲师,主要从事作物营养诊断和作物生长模拟模型研究,E-mail:shi_peihua@126.com。
通信作者:史培华,博士,讲师,主要从事作物营养诊断和作物生长模拟模型研究,E-mail:shi_peihua@126.com。
更新日期/Last Update: 1900-01-01