Rapid Detection of Tomato Mosaic Disease in Incubation Period by Infrared Thermal Imaging and Near Infrared Spectroscopy
ZHU Wen-jing1,2, LI Lin1,2, LI Mei-qing1,2, LIU Ji-zhan1,2, WEI Xin-hua1,2
1. Key Laboratory of Modern Agricultural Equipment and Technology，Ministry of Education & Jiangsu Province，Jiangsu University，Zhenjiang 212013，China
2. School of Agricultural Equipment Engineering，Jiangsu University，Zhenjiang 212013，China
Abstract：The lagging diagnosis method of tomato mosaic disease results in untimely and excessive application of pesticide. The conventional nondestructive testing methods were unable to be applied at early recognition in the incubation period. In this study, the infrared thermal imaging information acquisition system was designed. The efficiency and accuracy of this system were also tested. The main components of the system included a shell box, an infrared thermal image acquirer, a temperature and lift controller, a heating plate and a lift load table. The system developed in this study has the capacity to adjust the shooting temperature manually according to the requirements of the temperature range in a typical experiment. To test the precision of the system, the non-resistant tomatos variety L-402 were cultivated by Institute of Vegetables of Liaoning Academy of Agricultural Sciences in the Venlo type greenhouse of the Ministry & Provinces?Co - construction Key Laboratory of modern agricultural equipment and technology of Jiangsu University. The virus (Tobacccco mosaic virus, ToMV) infection experiment was conducted by using the method of leaf surface friction before the flowering stage. In the virus infection experiment, tomato plants were divided into three groups. The severe infection group (SI) was inoculated with the original virus solution. The Low-grade infection group (LI) was inoculated with diluted virus solution (500 times dilution by phosphate buffer). The control group (CG) was sprayed with equal amount of phosphate buffer. After 10 days of inoculation, spots began to appear on leaves of tomato plants in SI group, suggesting that the first 9 days were the incubation period of tomato mosaic disease. Infrared thermal imaging system was used to collect infrared thermal imaging of those three groups with a total sample size of 144 during the incubation period. The maximum temperature difference (MTD) of the leaf table was calculated to characterize the change of leaf temperature in continuous 9 days during the incubation period. The MTD value of the leaves in the CG group was statistical non-significant, but the MTD value of the leaves in both LI and SI groups was significantly changed after inoculation with the infection time of the virus. After six days of inoculation, the maximum difference of MTD value can reached 1.63 ℃. The difference gradually narrowed down from 7 days, indicating that the virus were spread to more and more regions on the infected leaves and raised the temperature of the whole leaf. Two spectral acquisition methods were conducted. The first one was Thermal-imaging collection method (TCM). During TCM, spectra were intensively acquired during the temperature mutation region which was calculated based on the MTD value from the infrared thermograph. The second method was to acquire spectrum on randomly selected points on the tip, middle, and base of leaves without focusing on the location of the lesion. This spectrum acquisition method was recognized as random collection method (RCM). The principle of TCM to select the effective position for the three spectral acquisition points was that the average MTD value of the mutation zone in the LI group was 0.3, 0.7 and 0.5 ℃ higher than those in the CG group on the 3rd, 6th and 9th day after inoculation respectively.The average MTD value of the mutation zone in the SI group was 0.5, 1.2 and 0.8 ℃ higher than those in the CG group on the 3rd, 6th and 9th day after inoculation, respectively. Lesion position met the above criteria could be considered as an optional area for TCM. All samples were identified by using Support Vector Machine (SVM) algorithm for discriminant analysis. The principal component analysis (PCA) was used to compress the spectral information of 2 151 wavelength points. The cumulative variance contribution rate of the first six principal components has reached 99%. The samples of 3, 6 and 9 d were divided into the correction set and the prediction set at the ratio of 2∶1, and the disease degree of the prediction set samples was identified. The total recognition rates of the models established by the two methods are 92.59% and 99.77%, respectively. In the spectral recognition model established by TCM, only one sample from LI group after 3 d was unable to be identified and mistaken into CG group. Despite this sample, the remaining recognition rate reached 100%. The results showed that it is feasible to use near infrared spectroscopy to identify tomato mosaic disease at early stage. Using infrared thermal imagingin combination with near-infrared spectroscopy technique allows us to establish higher recognition rate models for identification of tomato mosaic disease during incubation period. This study provided an alternative method for the development of follow-up control process, and created a new model to break through the bottleneck of the early precise pesticide spraying of crops. It overcame the point source of NIR sampling randomness and helped to establish a more accurate intelligent pesticide application system in greenhouse.
朱文静，李 林，李美清，刘继展，魏新华. 红外热成像与近红外光谱结合快速检测潜育期番茄花叶病[J]. 光谱学与光谱分析, 2018, 38(09): 2757-2762.
ZHU Wen-jing, LI Lin, LI Mei-qing, LIU Ji-zhan, WEI Xin-hua. Rapid Detection of Tomato Mosaic Disease in Incubation Period by Infrared Thermal Imaging and Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(09): 2757-2762.
[1] Liu Q, Gu Y L, Wang S H, et al. Journal of Spectroscopy, 2015, (1): 11.
[2] WANG Hai-chao, GAO Xiong, CHEN Tie-ying, et al(王海超，高 雄，陈铁英，等). Journal of Agricultural Mechanization Research(农机化研究)，2015, (10): 1.
[3] MIAO Teng, ZHAO Chun-jiang, GUO Xin-yu, et al(苗 腾，赵春江，郭新宇，等).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2014, 32(4): 169.
[4] CHEN Bin, TIAN Gui-hua(陈 斌，田桂华). Jiangsu Agricultural Sciences(江苏农业科学)，2014, (42): 1.
[5] LI Zhen, SHI Zhi-xing, WANG Cheng, et al(李 真，史智兴，王 成，等). Journal of Agricultural Mechanization Research(农机化研究)，2016, (1): 232.
[6] Masri A A, Hau B, Dehne H W, et al. European Journal of Plant Pathology, 2016, 147(4): 1.
[7] Maes W H, Baert A, Huete A R, et al.Agricultural and Forest Meteorology, 2016, (226): 119.
[8] Maes W H, Minchin P E, Snelgar W P, et al. Functional Plant Biology, 2014, (41)：1207.
[9] LI Guang-jun(李光君). Journal of Shanxi Agricultural Sciences(山西农业科学)，2016, 44(368): 1467.
[10] Vapnik V. The Nature of Statistical Learning Theory. NY: Springer-Verlag, 1995.
[11] BIAN Zhao-qi, ZHANG Xue-gong(边肇祺，张学工). Pattern Recognition(模式识别). Beijing：Tsinghua University Press(北京：清华大学出版社)，2002.
[12] XU Xiao-long, JIANG Huan-yu, HANG Yue-lan(徐小龙，蒋焕煜，杭月兰). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2012, 28(5): 145.
