Segmentation and Detection of Cucumber Powdery Mildew by Comparison of Near-Infrared and Fluorescence Spectra
XU Ji-tong1, JIN Hai-rong1, TONG Wen-yu1, ZHANG Zhe1, GUO Yu-hang1, TIAN Su-bo1. 2, NING Xiao-feng1, 2*
1. College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
2. Key Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs，Shenyang 110866, China
Abstract：As a disease with fast transmission speed and high frequency, cucumber powdery mildew will deal a serious blow to cucumber yield once it breaks out; therefore, it is of great significance for the identification and early prevention of cucumber powdery mildew. This study, used a portable spectrometer to collect the reflectance curves of near-infrared (NIR) spectral and the intensity curves of fluorescence spectral of cucumber leaves. LI-6400 photosynthetic meter was used to measure the photosynthetic rate of cucumber leaves, and we also collected the image information of cucumber leaves.Firstly, powdery mildew was classified by image segmentation. Secondly, the Pearson Correlation between net photosynthetic rate and spectrum was analyzed. Finally, Finally, a powdery mildew detection model was established using qualitative analysis and quantitative prediction methods combined with photosynthetic rate indexes of cucumber leaves. The results showed that the cucumber leaf region was segmented by binarization as the region of interest (ROI), and the powdery mildew spot area could be extracted effectively according to the color difference between RGB and L*a*b* color space. Pearson Correlation analyzed the correlation between the photosynthetic rate and the spectrum. Results showed that the photosynthetic rate and the spectrum were negatively correlated. The correlation weakened with the increasing reflectivity and spectral intensity, which indicated that it is feasible to predict the photosynthetic rate using bands with intense spectral correlations. After comparing the prediction accuracy, the qualitative model was finally analyzed by the Subspace Discriminant algorithm in Ensemble Learner, and the NIR spectrum model was stable, and the recognition accuracy was high. The PLSR model was used for quantitative analysis, and the MSC was used as a preprocessing method to effectively remove spectral interference information, of which theR2 obtained by the NIR spectrum model was high, and the RMSEP was smaller than the RMSEC. In addition, the predicted results of the NIR spectral model were more similar to the expected values, and the healthy samples were clearly distinguished from the ones with powdery mildew infection, indicating that the model is highly robust. The above results showed that the image recognition system and the photosynthetic rate detection model based on NIRspectroscopy could be used to identify cucumber powdery mildew and classification quickly and accurately, which provided a method and reference for the diagnosis of cucumber disease.
Key words：Cucumber powdery mildew; Near-infrared spectrum; Fluorescence spectrum; Segmentation and grading of disease spots; Photosynthetic rate
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