1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information，Ministry of Agriculture and Rural Affairs, Beijing 100097, China
Abstract：Tomato leaves in the infection of disease occurred after the first internal physiological reaction, the naked eye can not observe, from the blade infection to the appearance of visible disease spots, for the left disease latent period. In order to achieve the tomato leaf surface did not see obvious disease spots of gray mold latent period diagnosis. This paper is for the inoculation samples for leaf coding, daily tracking, and collection of all encoded leaf sample hyperspectral image data, the establishment of tomato leaf sample sequence hyperspectral data set. Based on the tracked leaf samples, hyperspectral data from the same location area a few days before the appearance of visible spots with the naked eye were used for detection and analysis as latent period data. In order to establish the diagnosis of the latent period of tomato leaf gray mold disease and the classification model of different disease plaque levels, the classification model based on the teaching learning-based optimization algorithm (TLBO) optimization extreme learning machine (ELM) is used to model. The input weight and hidden layer deviation of ELM are optimized by the TLBO algorithm, and the model classification performance is improved. Data modeling was obtained in the near-infrared hyperspectral band 388-1007nm band to obtain 5 levels of interest, and 213 hyperspectral data were sampled, including health (56), latent (42), small disease plaque (43), major disease plaque (39) and severe disease (33). The best-performing wavelet filtering transformations (Discrete Wavelet Transform, DWT) filter each type of data in the sample data separately by comparing different spectral preprocessing methods. After DWT filtering, the five class spectral curves between the 610 and 840 nm bands can be distinguished significantly, containing 91 wavelengths and a larger wavelength. Therefore, competitive adaptive reweighted sampling is used (Competitive Adaptive Reweighted Sampling, CARS) to repeated the preferred feature wavelength 3 times in the 610~840 nm band using DWT pre-treated spectral data and combined to remove duplicates to obtain 9 feature bands: 694, 696, 765, 767, 769, 772, 778, 838 and 840 nm. Finally, three classification models FC-TLBO-ELM, DWT-TLBO-ELM, DWT-CARS-TLBO-ELM, were selected for experimental comparison, in which DWT-CARS-TLBO-ELM detection accuracy was up to 100%, and the potential recall rate was 100%. Using the minimum time of 0.068 9 s, it is shown that the model can realize the high-precision diagnosis and high-precision classification of the disease degree of gray mold disease during the latent breeding period of tomato ash mold, and provide a theoretical basis for the early prevention and treatment of tomato ash mold disease and the precise application of medicine.
Key words：Time series hyperspectral data; Classification of botrytis degree; Hyperspectral gleying diagnosis; ELM; TLBO
[1] YANG Ying-ru, WU Hua-rui, ZHANG Yan, et al(杨英茹，吴华瑞，张 燕，等). Journal of Chinese Agricultural Mechenization(中国农机化学报)，2021，42(9)：177.
[2] JIA Fang-fang, HONG Quan-chun, SONG Wei-yi(贾方方，洪权春，宋唯一). Chinese Journal of Eco-Agriculture(中国生态农业学报)，2017，25(6)：805.
[3] QIN Li-feng, ZHANG Xi, ZHANG Xiao-xi（秦立峰，张 熹，张晓茜）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2020，51(11)：212.
[4] MOU Duo-duo, LIU Lei（牟多铎，刘 磊）. Remote Sensing Technology and Application（遥感技术与应用），2019，34(1)：115.
[5] LU Bing, SUN Jun, YANG Ning，et al（芦 兵, 孙 俊, 杨 宁，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019，39(8)：2515.
[6] CHEN Qian-shu, WEN Xiao-tao, HE Jian, et al(陈芊澍，文晓涛，何 健，等). Geophysical Propecting for Petroleum(石油物探)，2021，60(1)：149.
[7] Sevinc E, Dokeroglu T. Turkish Journal of Electrical Engineering & Computer Sciences，2019, 27: 1523.
[8] GAO Xi-wen, JIA Ke-li, MAO Hong-xin, et al（高曦文，贾科利，毛鸿欣，等）. Modern Electronics Technique（现代电子技术），2021，44(3)：155.
[9] SHI Yan, SUN Dong-mei, XIONG Jing, et al. Chinese Pharmaceutical Journal（中国药学杂志），2018，53(14)：1216.
[10] LIU Si-jia, TIAN You-wen, ZHANG Fang, et al(刘思伽，田有文，张 芳，等). Food Science(食品科学)，2017，38(8)：277.
[11] Mobley P R，Kowalski B R, Workman J R J J, et al. Applied Spectroscopy Reviews，1996，31(4)：347.
[12] Goel P K，Prasher S O，Patel R M，et al. Computers and Electronics in Agriculture，2003，39(2)：67.
[13] Barnes R J, Dhanoa M S, Susan J L. Applied Spectroscopy，1989，43(5)：772.
[14] XUE Qing-sheng, TIAN Zhong-tian, HAO Xi-jie, et al（薛庆生，田中天，郝锡杰，等）. Acta Optica Sinica（光学学报），2020，40(17)：201.
