应用数字图像技术进行水稻氮素营养诊断

【目的】研究田间试验条件下水稻不同生育期冠层图像色彩参数(G、NRI、NGI、NBI、G/R和G/B)及植株氮素营养指标(叶片含氮量、植株全氮含量、生物量、氮素累积量和冠层NDVI值)的时空变化特征,并分析两者间的相关性,确立水稻氮素营养诊断的最佳色彩参数和方程模型,为探明数码相机在水稻上的适宜性及精确诊断水稻氮素营养状况提供理论基础。【方法】于2013年5月9月在湖北省武汉市华中农业大学试验基地(30°28'08'N,114°21'36'E)采用不同施氮处理的田间试验,以籼型两系杂交稻"两优6326"为供试作物,设置4个施氮水平:0、75、150和225 kg/hm2(分别以N0、N75、150和N225表示),3次重复,随机区组排列。分别在水稻分蘖期、拔节期、孕穗期和灌浆期采用数码相机(Nikon-D700,1200万像素)获取水稻冠层图像,应用Adobe photoshop7.0软件直方图程序提取图像的红光值R、绿光值G和蓝光值B,研究数码相机进行水稻氮素营养诊断色彩参数,确定植株氮素营养指标诊断模型。【结果】较对照(N0)相比,分蘖期、拔节期、孕穗期和灌浆期3个施氮处理水稻地上部生物量、叶片含氮量、植株全氮含量、氮素累积量、冠层NDVI值和成熟期产量增幅分别平均为40.7%98.0%、42.4%72.4%、36.2%85.3%、125.5%209.1%、51.3%60.6%和60.1%117.0%,差异显著。水稻不同生育期各冠层数字化指标G、NRI、NGI、NBI、G/R和G/B与上述氮素营养参数相关性差异较大,且以数字图像红光标准化值NRI表现最佳,建议作为应用数码相机进行水稻氮素营养诊断的最佳冠层图像色彩参数指标。进一步分析表明,可以用统一的线性回归方程来描述不同生育期、不同氮素水平下水稻植株氮素营养指标随冠层色彩参数NRI的变化模式。【结论】数码相机进行水稻氮素营养诊断测试结果稳定,具有快速、便捷、非破坏性等优点,冠层色彩参数NRI与水稻氮素营养指标和产量之间均表现出较好的相关性,满足氮素营养无损诊断的需求,对实时、快速监测水稻长势状况及氮素营养丰缺水平具有较高的可行性,有望发展成为新时期作物氮素营养无损诊断技术的潜力。

Diagnosis of N nutrition of rice using digital image processing technique

【Objectives】 The spatial and temporal distribution of color indexes of canopy (G, NRI, NGI, NBI, G/R and G/B) and the indexes of N nutrition in rice plants were studied to determine the best color parameters and regression equations for nitrogen with a digital camera, and provide a theoretical basis and technical approach for monitoring plant nitrogen status of rice and precision management of nitrogen fertilization. 【Methods】 Field experiments were carried out from May to September 2013 in rice growing season at the Experimental Farm of Huazhong Agricultural University (30°28′ 08′′N, 114°21′36′′E). A two-line indica hybrid (Liangyou6326) was chosen as test cultivar. Four N treatments: N 0, 75, 150 and 225 kg/ha were designed and recorded as N0, N75, N150, N225, respectively. A color digital camera (D700, Nikon, Japan) with a resolution of 12.0 mega pixels was employed to capture color images of rice canopy at the tillering, jointing, booting and filling stage. The image resolution was 1936×1296 pixels of 14 bit for red, green and blue. The images of size 2.72 MB were transferred in joint photographic experts group (JPEG) format to a computer and processed with Adobe Photoshop 7.0 to extract color information for studying possibility of using digital image analysis method for plant N status diagnosis and for determining the best color parameters and regression equations in rice. 【Results】 Compared with N0, the biomass, leaf N content, plant total N content, N accumulation and canopy NDVI values of rice with N treatments at the tillering stage, jointing stage, booting stage and filling stage are averagely increased by 40.7%-98.0%, 42.4%-72.4%, 36.2%-85.3%, 125.5%-209.1% and 51.3%-60.6%, respectively, at mature stage, the average increase of yield are from 60.1% to 117.0%. Compared with other plant canopy color parameters, the normalized redness intensity (NRI), calculated as R/(R+G+B), is much better as it has better correlations with the index of N-nutrient in rice plants, rice yield and canopy NDVI values at the different growth stages. An integrated linear regression equation could be used for describing the relationship between NRI and leaf N content, plant total N content, N accumulation, canopy NDVI values, yield at different growing stages and nitrogen levels of rice. 【Conclusions】 The digital image processing technique is usable in detecting the N nutrition in rice and has advantages of fast, stable results, easy to apply, and non-destructive. Simultaneously, the color parameter NRI has a preferable interrelation with the index of N-nutrition and yield than others, thus NRI is suitable for rapid diagnosis of nitrogen nutrition, and the digital image processing technique method shows the potential of being used in fast rice nitrogen diagnosis without damage.