A Rapid Diagnostic Grading System for Cucumber Downy Mildew Based on Visible Light - Hyperspectral Imaging System


  • Chunyang Yao Jiangsu University, Zhenjiang, China
  • Xiaodong Zhang Jiangsu University, Zhenjiang 212013
  • Hanping Mao Jiangsu University, Zhenjiang 212013
  • Hongyan Gao Jiangsu University, Zhenjiang 212013
  • Qinglin Li Jiangsu University, Zhenjiang 212013




Cucumber Downy Mildew,Hyperspectral, Spectral angle,Disease classification,SVM


Downy mildew, a kind of cucumber disease with a high spread rate and harmfulness that is more common in the world, has a great influence on the yield of cucumbers. The rapid identification of its symptoms and the rapid classification of the post-disease characters are of great significance to the rapid diagnosis of cucumber frost mold and the proper treatment of medicine after the disease. In order to quickly and accurately classify the occurrence and the degree of cucumber downy mildew, a rapid diagnosis and classification method of cucumber downy mildew based on visible light - high spectral imaging technology was proposed in this paper. In addition, the stepwise regression method and PCA were used to reduce and extract the feature information of sensitive bands. Two kinds of acquired feature information are used as the input of the model to construct the disease degree classification detection model of the SVM classification model. The model based on the stepwise regression method is used to classify and identify downy mildew and normal leaves. In this model, the accuracy of the Sigmoid kernel function classification test is the highest, reaching 95.00%, and the recognition rate of different degrees of cucumber downy mildew disease leaves as high as 93.88, which has a high classification detection accuracy. The results show that the rapid diagnosis and classification of cucumber downy mildew can be realized by using the visible light spectral imaging system combined with the automatic classification model of SVM, which provides a new method and reference for solving the problem of cucumber downy mildew in time.


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Author Biographies

Chunyang Yao, Jiangsu University, Zhenjiang, China

School of Agricultural Equipment Engineering

Xiaodong Zhang, Jiangsu University, Zhenjiang 212013

School of Agricultural Equipment Engineering

Hanping Mao, Jiangsu University, Zhenjiang 212013

School of Agricultural Equipment Engineering 

Hongyan Gao, Jiangsu University, Zhenjiang 212013

School of Agricultural Equipment Engineering

Qinglin Li, Jiangsu University, Zhenjiang 212013

School of Agricultural Equipment Engineering


Yang Yinjuan, Ju Zhongan, Shi Yinghong, et al. Early warning model of meteorological elements of cucumber downy mildew in spring and autumn [J]. China Plant Protection Guide, 2018, 038(008):43-47,70.

Li Lin. Thoughts on pesticide reduction and control of facility vegetables[J]. Tianjin Agriculture and Forestry Science and Technology, 2019(6):24-27.

Jiang Longquan, Lu Shuai, Dong Wenyu, et al. Plant disease and insect pest detection method based on SVM machine learning, 2013. National Invention Patent

Ma Juncheng, Wen Haojie, Li Xinxing, et al. Diagnosis system of cucumber downy mildew in greenhouse based on image processing[J]. Journal of Agricultural Machinery, 2017(02):200-207.

Bai Xuebing, Yu Jianshu, Fu Zetian, et al. Segmentation and detection of cucumber powdery mildew in the joint interval of visible spectrum images[J]. Spectroscopy and Spectral Analysis, 2019, 39(11).

Zhang Aiwu, Zhang Taipei, Kang Xiaoyan, et al. Analysis of spectral changes of plant leaves before and after dust-retention under hyperspectral imaging[J]. Journal of Agricultural Engineering , 2018, 034(019): 170-176.

Jiang Jinbao, Chen Yunhao, Huang Wenjiang. Research on monitoring of winter wheat diseases by hyperspectral differential index[J]. Spectroscopy and Spectral Analysis, 2007(12):93-97.

Wang Xiangyu, Zhu Chenguang, Fu Zetian, et al. identification of cucumber powdery mildew based on visible light spectrum analysis [J]. Spectroscopy and Spectral Analysis, 2019, 39(6).

Ramalingam N, Ling P P, Derksen R C. Transactions of the the American Society of Agricultural Engineering (ASAE), 2005, 48(1): 375.

Wu Nan, Liu Junang, Yan Ruikun, et al. Inversion of water content of Camellia oleifera leaves under disease stress based on artificial neural network and hyperspectral technology [C]// 50th Anniversary Celebration and Academic Annual Conference of Chinese Plant Protection Society. 0.

Wang Zicheng, Zhu Jiaming, Chen Huayou. Regression combination prediction model based on stepwise regression screening[J]. Statistics and Decision, 2019(17).

Hu Gang, Xu Xiang, Zhang Weiming, et al. Evaluation of network node importance index contribution based on principal component analysis[J]. Acta Electronica Sinica, 2019, 47(02):104-111.

Wang Meng. Ultrasound image segmentation technology based on improved support vector machine algorithm[J]. Biomedical Engineering Research, 2019, 38(2).



How to Cite

Chunyang Yao, Xiaodong Zhang, Hanping Mao, Hongyan Gao, & Qinglin Li. (2020). A Rapid Diagnostic Grading System for Cucumber Downy Mildew Based on Visible Light - Hyperspectral Imaging System. JOURNAL OF ADVANCES IN AGRICULTURE, 11, 108-121. https://doi.org/10.24297/jaa.v11i.8779