基于数字图像技术的冬油菜氮素营养诊断

Diagnosing Nitrogen Nutrition Status of Winter Rapeseed via Digital Image Processing Technique

【目的】利用田间氮肥梯度试验探讨数字图像技术对冬油菜氮素营养无损评估预测的可行性,明确该技术的最佳数码参数和方程模型,为数字图像技术进行冬油菜氮素无损诊断提供依据。【方法】2013—2014年在湖北省武穴市开展不同施氮处理田间试验,以冬油菜为试验材料,设置不同氮素水平(0、90、180、270和360kg·hm-2),分别于六叶期、十叶期、蕾薹期和开花期,利用数码相机获取冠层数字图像数据,同时采集植株样品分析其生长特征值,研究其相关性并建立氮素营养参数的方程模型。利用2014—2015年独立氮肥水平试验,对上述方程模型拟合精度进行验证并绘制1﹕1线性关系图。【结果】数字图像红光值(R)、红光标准化值(NRI)和绿光与蓝光比值(G/B)与冬油菜氮营养状况常规诊断指标地上部生物量、叶片氮浓度和叶绿素浓度等呈负相关关系,而绿光值(G)、蓝光值(B)、绿光与红光比值(G/R)、蓝光与红光比值(B/R)、绿光标准化值(NGI)和蓝光标准化值(NBI)则与上述指标呈正相关关系,红光标准化值(NRI)与其他数码参数相比能更好地表征冬油菜的氮素营养状况,蕾薹期红光标准化值NRI与氮肥用量、地上部生物量、叶片氮浓度、叶绿素浓度、氮素吸收量和氮营养指数之间的关系可分别用线性方程y(t·hm-2)=-8.003x+2.706、y(t·hm-2)=-106.072x+38.200、y(g·kg-1)=-692.99x+261.84、y(mg·g-1)=-12.750x+5.665、y(kg·hm-2)=-4087.416x+1414.274和y=-27.198x+9.812来表达,其相关性达到极显著水平。2014—2015年独立试验模型检验结果表明,叶片氮浓度、叶绿素浓度和氮营养指数实测值与预测值的决定系数R2分别为0.917**、0.746**和0.953**;均方根误差RMSE分别为0.821、0.330和0.228;相对误差RE%分别为26.32%、28.57%和28.39%,模型预测精度较好。【结论】数字图像技术可以用于冬油菜氮素营养的评估预测,评估时期为蕾薹期(包括)之前均可,最佳预测参数为红光标准化值NRI,参数的最佳方程模型为直线方程函数。

【Objective】 In order to provide a scientific basis for digital image processing technique in nitrogen nondestructive diagnosis of winter rapeseed, a field experiment was carried out to explore the feasibility of digital image processing technique in nitrogen nondestructive diagnosis, determine the best digital parameter and regression equation. 【Method】 A field experiment was conducted with different nitrogen application rates（0, 90, 180, 270 and 360 kg·hm^-2）. The pictures of winter rapeseed canopy were obtained with a digital camera, meanwhile, the conventional nitrogen diagnosis parameters of aboveground biomass, nitrogen concentration in leaf and leaf chlorophyll content at six-leaf period, ten-leaf period, bud period and blooming period, were determined and the correlations were analyzed. 【Result】 The red color intensity（R）, normalized redness intensity（NRI） and the ratio of greenness and blueness intensity（G/B） had significant inverse correlations with the conventional N diagnosis parameters. But green color intensity（G）, blue color intensity（B）, the ratio of greenness and redness intensity（G/R）, the ratio of blueness and redness intensity（B/R）, the normalized greenness intensity（NGI） and the normalized blueness intensity（NBI） showed a significant positive correlation. Compared with other digital index, the normalized redness intensity（NRI） showed a prominently and very prominently inverse relationship with conventional N parameters and the regression equation with N application rate, aboveground biomass, leaf N concentration, chlorophyll in leaf, N uptake and N nutrition index is y（t·hm^-2）=-8.003x＋2.706, y（t·hm^-2）=-106.072x＋38.200, y（g·kg^-1）=-692.99x＋261.84, y（mg·g^-1）=-12.750x＋5.665, y（kg·hm^-2）=-4087.416x＋1414.274 and y=-27.198x＋9.812, the correlation values（R^2） is 0.917＊＊, 0.746＊＊ and 0.953＊＊ between measured and estimated leaf N concentration, chlorophyll in leaf and NNI, RMSE and RE were 0.821, 26.32%, 0.330, 28.57% and 0.228, 28.39%, respectively. 【Conclusion】Digital image processing technique can be used in nitrogen nondestructive diagnosis of winter rapeseed, the stage before bud period is crucial period with NRI as the index for the N nitrogen diagnosis using digital image processing technique, and NRI is the best diagnosis index with linear regression equation.