可见光光谱和机器学习的温室黄瓜霜霉病严重度定量估算

doi:10.3964/j.issn.1000-0593(2020)01-0227-06

Abstract
References
Related Citation (15)

Download: PDF (2236 KB)
Export: BibTeX | EndNote (RIS)

Abstract Accurate estimation of disease severity for downy mildew of greenhouse cucumber is a prerequisite for scientific disease control. It is of great significance to reduce the use of pesticides and to improve the quality of greenhouse cucumber, as well as farmers’ income. With the application of machine learning in the field of plant disease diagnosis, estimating the severity of plant diseases is gaining concerns. In order to increase the accuracy, this paper used the digital images of greenhouse cucumber downy mildew and machine learning methods to estimate the disease severity for downy mildew of greenhouse cucumber. A digital camera was used to collect images of greenhouse cucumber leaves with downy mildew, whose background were manually eliminated. An estimation model based on Convolutional Neural Network (CNN) was constructed with cucumber downy mildew leaf image as input. The initial symptom segmentation was achieved by using the combination of three visible color features (CVCF) and support vector machine. The segmentation results were optimized by using the speeded up robust feature (SURF) feature and morphological operation. After obtaining the segmentation image of cucumber downy mildew symptoms, the average and standard deviation of 15 color components in five color spaces of RGB, HSV, L^*a^*b^*, YCbCr and HSI were extracted. On this basis, the gray level co-occurrence matrix was used to extract four texture features of each color component, including contrast, correlation, entropy and stability, resulting in 90 features. Pearson correlation analysis was used for feature selection. Shallow machine learning estimation models, including Support Vector Machine Regression and BP Neural Network, were constructed based on the image features with high correlation with the actual severity value of downy mildew of greenhouse cucumber. Based on the three estimation models, the disease severity for downy mildew of cucumber was estimated. The accuracy of the three estimation models was quantitatively evaluated by using Coefficient of Determination (R²) and Normalized Root-Mean-Squared Error (NRMSE). The results showed that there was a good linear relationship between the severity of downy mildew of greenhouse cucumber estimated by the model and the actual values. The model based on CNN achieved the best accuracy, whose R² was 0.919 0 and NRMSE was 23.33%, followed by the model based on BPNN, with R² being 0.890 8, NRMSE being 24.64%, while the model based on SVR was the last, with R² being 0.890 1 and NRMSE being 31.08%. The evaluation results showed that by using the digital images of cucumber downy mildew and the convolution neural network estimation model, the disease severity for downy mildew of greenhouse cucumber could be accurately estimated, which could provide support to the scientific control of downy mildew of greenhouse cucumber and reduce the use of pesticides.

Key words：Greenhouse cucumber; Downy mildew; Severity; Visible spectrum; Machine learning; Estimation

Received: 2018-12-04 Accepted: 2019-04-08

ZTFLH:

TN941.1

Corresponding Authors: MA Jun-cheng E-mail: majuncheng@caas.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)01-0227-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I01/227

[1] Ma J C, Du K M, Zhang L X, et al. Computers and Electronics in Agriculture, 2017, 142: 110.
[2] WANG Xiang-yu，LI Xin-xing，ZHANG Ling-xian，et al（王翔宇，李鑫星，张领先，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2016，47(9)：266.
[3] MA Jun-cheng，DU Ke-ming，ZHENG Fei-xiang，et al（马浚诚，杜克明，郑飞翔，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2018，38(6): 1863.
[4] PENG Zhan-wu，SI Xiu-li，WANG Xue，et al（彭占武，司秀丽，王雪，等）. Journal of Chinese Agricultural Mechanization（中国农机化学报）, 2013，(2): 212.
[5] MA Jun-cheng，WEN Hao-jie，LI Xin-xing，et al（马浚诚，温皓杰，李鑫星，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2017, 48(2): 195.
[6] Wei Y, Chang R, Wang Y, et al. A Study of Image Processing on Identifying Cucumber Disease. In Li D, Chen Y. Computer and Computing Technologies in Agriculture V. CCTA 2011, IFIP Advances in Information and Communication Technology, Vol 370. Springer Berlin Heidelberg.
[7] Zhang S, Wang Z. Neurocomputing, 2016, 205(C): 341.
[8] YE Hai-jian，LANG Rui（叶海建，郎睿）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2017, 48(3): 24.
[9] Wang G, Sun Y, Wang J. Computational Intelligence and Neuroscience, 2017, 2017: 2917536.
[10] YANG Chong-shi，WANG Wan-li，LIU Geng-chun，et al（杨崇实, 王万立, 刘耕春，等）. Progress and Practice of Bioassay Method for Cucumber Downy Mildew(黄瓜霜霉病生物测定技术研究进展与实践). Annual Meeting of Pesticide Professional Committee of China Chemical Industry Association（中国化工学会农药专业委员会年会），2000.
[11] LI Jing-zhu，WANG Peng，GENG Chang-xing（李井祝，王鹏，耿长兴）. Journal of Chinese Agricultural Mechanization（中国农机化学报），2017, 38(6): 67.
[12] SUN Jun，TAN Wen-jun，MAO Han-ping，et al（孙俊，谭文军，毛罕平，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2017, 33(19): 209.
[13] Ferentinos K P. Computers & Electronics in Agriculture, 2018, 145: 311.
[14] Mohanty S P, Hughes D P, Salathé M. Frontiers in Plant Science, 2016, 7：1419.
[15] MA Jun-cheng，DU Ke-ming，ZHENG Fei-xiang，et al（马浚诚，杜克明，郑飞翔，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2018, 34(12): 186.
[16] Zhang C, Pham M, Fu S, et al. Mathematical Programming, 2018, 169(1): 277.
[17] Jin B S, Zuo Y H, Ma X D, et al. Advanced Materials Research, 2012, 433-440: 5469.
[18] LI Chang-chun，NIU Qing-lin，YANG Gui-jun（李长春，牛庆林，杨贵军）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2017, 48(8): 147.
[19] Ma J, Du K, Zhang L, et al. Computers and Electronics in Agriculture, 2018, 154: 18.