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Aiming at the spectral image acquisition of cucumber downy mildew, the study proposed the optimal selection method of spectral imaging characteristic wavelengths combining the mean-variance analysis and analysis of variance. In this study, the variation degree and range of reflection spectrum was quantitatively measured for both healthy leaves and disease spots in cucumber plants. The analysis showed that the three sensitive wavebands (493 nm 533 nm, 561 nm 653 nm, and 689 nm 698 nm) could properly reflect the inter class difference in spectral reflectance between the dorsal part of lesion and healthy leaves. When the selected combination of spectrum was used as the reference for disease spectral imaging acquisition, it showed that the choice of sensitive wavebands can better reflect the difference in the characteristics between lesion and healthy leaves. It has laid the foundation for obtaining the effective spectral images of disease and simplifying the algorithm for image identification. 摘要 针对黄瓜霜霉病的光谱图像获取,提出了基于均值方差法和方差差异分析相结合的光谱图像特征波长优 化选取方法,定量衡量了黄瓜作物健康叶片和病斑的反射光谱差异程度及差异范围,提取了 493 nm 533 nm、561 nm 653 nm、689 nm 698 nm 三个敏感波段,分析表明上述波段能较好反映病斑与健康叶片正面 的类间光谱反射差异,并将选定的光谱组合作为病害光谱图像获取的参考依据,表明敏感波段的选择能较好地 反映病斑与叶片间的特性差异,为获取病害有效光谱图像和简化图像识别算法奠定了基础。 INTRODUCTION The characteristics of the reflectance spectrum of any plant vary under the influence of disease and biological stress; however, there are rules for the variation (Manjunath et al., 2014; Shi et al., 2016; Bandaru et al., 2016). There have been many studies for detecting plant diseases and biological stress from the perspective of spectral analysis and spectral imaging at home and abroad. For instance, (Wei et al., 2014) used three wavelengths (518, 711 and 980 nm) to differentiate the maturity in persimmons into four stages: immature, early mature, fully mature and over mature; and the accuracy can be as high as 95.3%. (Sirisomboon et al., 2013) used near-infrared spectroscopy (NIRS) to detect rice mould infections and found that the error rate could be reduced to 18.723%, and the deviation to 4.613%. (Mahlein et al., 2013) studied the waveband between 450 and 950nm and developed a specific spectral disease index (SDIS) for crop disease detections, and the result showed that the index is highly accurate and sensitive. (Min et al., 2005) found the wavelengths related to chlorophyll are 450, 550, 660 and 719 nm, and those related to protein are 2054 and 2180 nm. (Kong et al., 2013) used the spectroscopic analysis together with image processing and stoichiometric methods, established a least squares support vector machine (LSSVM) model for the early diagnosis of grey mould in stems of tomatoes. The result suggested that using the hyperspectral imaging technology to diagnose the grey mould disease of tomato stems at early stage is feasible. (Jiang et al., 2013) used three sensitive wavelengths (532, 610 and 700nm) to measure chlorophyll content in tomato leaves and acquired outstanding results. The unique reflectance characteristics at different wavelengths in a plant’s spectrum can be effectively transformed into the expression of target information and detection information of spectral imaging via analyses (Sun et al., 2016; Ma et al., 2016; Story et al., 2010). Vol. 57, No. 1 / 2019 30 For efficient spectral imaging of plant diseases in complex environments, it requires analyses of their spectral properties and selection of characteristic wavebands (Ye et al., 2016; Polat et al., 2014). The aim of this study was to use spectral analysis methods to determine the optimal spectral wavebands that could diagnose the downy mildew disease, to increase the grey scale contrast of the corresponding spectral image object and background, and to provide a useful basis for the subsequent image processing. MATERIAL AND METHODS  Experiment Materials The cucumber for experiment was the cultivar "Zhongke 958”, grown with a trailing method. Spectral data collection was conducted in the solar greenhouse at Shang-Zhuang Experimental Station of China Agricultural University in November 2015. Owing to the need for supportive materials in trailing cultivation, such as bamboo poles, nylon slings and clamps, there were a considerable amount of background information in the spectral imaging acquisition results. In order to study the cucumber downy mildew and the spectral characteristics in cucumbers, leaves and complex background, 11 types of samples, including the dorsal and ventral sides of leaves with lesions caused by downy mildew, the dorsal and ventral sides of the healthy leaves, cucumbers, dried leaves, bamboo poles, green nylon slings, black clamps, dry soil and wet soil, were selected as the main objects for spectral data collection.  Choice of Instrument The spectral reflectance data was collected using the FieldSpec Pro VNIR handheld spectrometer (Analytical Spectral Devices, Inc., USA). The spectrometer measures wavelengths ranging between 325 nm and 1075 nm, and the spectral resolution is at 1 nm. Prior to data collection, the standard whiteboard was used to calibrate and eliminate systematic errors. After the calibration, the spectral reflectance was measured in the 11 types of samples. The sensor probe was vertically pinned and the sampling height was 20 mm. The measured object was at the same level as the standard white board in measurement. 20 probe points were randomly selected on each sample, with each point being tested for 5 times. The mean of the 5 measurements was then used as the final measurement for each testing point. The raw spectral reflectance data of each measured point was acquired at the waveband between 325 nm and 1075 nm. The measurements were saved in Microsoft EXCEL format and further analyzed using Matlab.  Data processing method  Spectral reflectance analysis A highly consistent spectra and an identical trend of reflectance was observed in the dorsal and ventral sides of lesion and healthy leaves. In the meantime, the distribution of healthy and lesion parts was spatially intersected. Here, a mean-variance analysis was used for an emphasis on analysing the volatility of the spectra for the dorsal and ventral sides of lesion and healthy leaves. The method was described as follows: By defining the estimated value for the mean reflectivity of sample x is:

THE OPTIMAL CHOICE OF THE CHARACTERISTIC WAVELENGTHS IN SPECTRAL IMAGING FOR CUCUMBER DOWNY MILDEW