可见光谱图像联合区间的黄瓜白粉病分割与检测

doi:10.3964/j.issn.1000-0593(2019)11-3592-07

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Abstract Powdery mildew, as a kind of cucumber disease with high outbreak frequency, spreads very fast, often leads to yield reduction and can’t achieve the expected economic benefits. Especially in serious years of disease outbreak, the reduction of cucumber in some areas was as high as 20%. This paper proposed a subinterval interval partial least squares regression (SI-PLSR) based on visible spectrum image for cucumber powdery mildew non-destructive detection. We usedCanon EOS 800D and Ocean Optics USB2000+ optical fiber spectrometer to collect visible spectral images and reflectivity curves of 200 cucumber powdery mildew leaves. Firstly, we used wavelet transform and watershed algorithm to extract the target leaves from the real-timevisible spectral images of cucumber powdery mildew leaves. Secondly, The Otsu algorithm optimized by Gauss fitting was used to segment the powdery mildew lesion. Thirdly, we established the PLSR in 350～1 100 nm band and calculated the cross validation root-mean-square error (RMSE_CV). At the other hand, 350~1 100 nm was divided into 20 sub-intervals, and established the PLSRindependently. The sub-intervals of RMSE_CV smaller than the full band were selected to form the joint interval. Finally, the SI-PLSR model was established based on powdery mildew lesions images and joint interval. Results show that 188 target leaves were extracted from 200 susceptible leaves visible spectral images successfully of which 157 were more than 95% and 31 were between 90% and 95%. The success rate was 94.00%. The average misclassification rate of powdery mildew was 5.81%. The average false negative was 1.55% and the average false positive was 4.26%. PLSR was established for 20 sub-intervals, and the results showed that the RMSE_CV values of the 5, 6, 7, 11, 12, 13 and 19 sub-intervals were lower than those of the full-band modeling, indicating that the spectral information of these seven sub-intervals contributed greatly to the identification of powdery mildew, which was relative to the wavebands of 470～520, 530～580 and 700～780 nm showing peaks. Therefore, these 7 sub intervals should be selected to establish the joint interval. The principal component number of SI-PLSR model was 7. R_C, R_V and RMSEC, RMSEV were 0.975 2, 0.907 3 and 0.919 5, 1.091. Compared with the full band PLSR model, the R_C and R_V of SI-PLSR was closer to 1, and the RMSEC and RMSEV were smaller. The above results showed that the SI-PLSR model proposed in this paper which effectively removed redundant information in visible spectral data and enhanced the stability of the model can be used to identify cucumber powdery mildew quickly and accurately, providing a method and reference for the diagnosis of cucumber diseases.

Key words：Visible spectrum; Subinterval interval; SI-PLSR; Computer vision

Received: 2018-09-02 Accepted: 2019-02-04

ZTFLH:

O433.4

Corresponding Authors: LI Xin-xing E-mail: lxxcau@cau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)11-3592-07 OR https://www.gpxygpfx.com/EN/Y2019/V39/I11/3592

[1] MA Jun-cheng，WEN Hao-jie, ZHANG Ling-xian, et al(马浚诚, 温皓杰, 张领先, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(2): 195.
[2] Lebeda A. Journal of Phytopathology, 2010, 108(1): 71.
[3] ZHANG Peng, ZHU Yu-qiang, WANG Li-li, et al(张鹏，朱育强，王丽莉，等). Chinese Agricultural Science Bulletin(中国农学通报), 2017, 33(21): 134.
[4] Vatchev T, Maneva S. Crop Protection, 2012, 42(4): 16.
[5] Joe M M, Islam M R, Karthikeyan B, et al. Crop Protection, 2012, 42: 141.
[6] HUANG Shuang-ping, QI Long, MA Xu, et al(黄双萍, 齐龙, 马旭, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(1): 212.
[7] Kuska M T, Mahlein A K. European Journal of Plant Pathology, 2018. 152(4)：1.
[8] Bai X, Li X, Fu Z, et al. Computers & Electronics in Agriculture, 2017, 136: 157.
[9] Ma J, Du K, Zhang L, et al. Computers & Electronics in Agriculture, 2017, 142(142): 110.
[10] Sui Y Y, Wang Q Y, Yu H Y. Spectroscopy & Spectral Analysis, 2016, 36(6): 1779.
[11] Li H N, Feng J, Yang W P, et al. Spectrum-Based Method for Quantitatively Detecting Diseases on Cucumber Leaf. 4th International Congress on Image and Signal Processing. IEEE, 2011, 4: 1971.
[12] West A G, Goldsmith G R, Matimati I, et al. Rapid Communications in Mass Spectrometry, 2011, 25(16): 2268.
[13] LIU Yan-de, XIAO Huai-chun, SUN Xu-dong, et al(刘燕德, 肖怀春, 孙旭东, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018，38(2)：528.
[14] ZHAO Juan, PENG Yan-kun(赵娟, 彭彦昆). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(7): 279.
[15] TANG Xiao-dong, LIU Man-hua, ZHAO Hui(汤晓东, 刘满华, 赵辉, 等). Journal of Electronic Measurement and Instrument(电子测量与仪器学报), 2010, 24(4): 385.
[16] Mizushima A, Lu R. Computers & Electronics in Agriculture, 2013, 94(94): 29.