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| Study on the Response Model of Spectral Signatures of Vegetation Leaves on the Stress Level for Sooty Mould |
| YANG Xing-chuan1, 2, 3, LUO Hong-xia3, ZHAO Wen-ji1, 2*, CHENG Yu-si3, WANG Hao-fei1, 2 |
1. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing 100048, China
2. Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
3. School of Geographical Sciences, Southwest University, Chongqing 400715, China |
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Abstract Sooty mould is one of very common plant diseases in the tropical and subtropical regions of southern China, which does great harm to China’s agricultural production. Monitoring and forecasting this disease provide a significant foundation and basis for the implementation of effective governance measures. To establish hyperspectral based data for Sooty mould severity level inversion model, this study collected 50 Cinnamomum Septentrionale samples’ hyperspectral data by ASD FieldSpec HandHeld type spectrometer in Beibei, Chongqing. Leaf area data was obtained through the digital camera and ENVI software, and the sooty mould severity was calculated by the ratio of sooty mould area and the whole leaf area. Then, a regression model was established by the maximum correlation between the sooty disease severity and the spectral reflectance data. This study shows that health leaves at around 560 nm band have obvious reflection peak, and when the severity of Sooty mould increases, the reflection peak gradually disappears. The spectral reflectance is negatively correlated with Sooty mould disease level in visible light and near infrared band, and the 500～650 and 720～850 nm are the sensitive spectral bands of Sooty mould. The correlation maximum point is in the 550 nm band, and the correlation coefficient reaches -0.72. By the analysis of Sooty mould severity with multi band spectral reflectance data, this study concludes that the 785 nm band has the largest correlation with disease severity and its regression model Sooty mould. The coefficient of determination (R2) reaches 0.875. Through the significance test and the prediction accuracy of the model, the conic model established at 785 nm is the best, which proved that the conic model based on the 785 nm band is effective in inversion of Sooty mould severity in single leaf scale.
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Received: 2016-09-13
Accepted: 2017-01-15
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Corresponding Authors:
ZHAO Wen-ji
E-mail: zhwenji1215@163.com
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[1] Mullay M. Westcott’s Plant Disease Handbook (7th Edition). Springer Netherlands, 2013.
[2] Gould A B. Plant & Pest Advisory Landscape Nursery & Turf, 2012, 18(10): 1.
[3] Chomnunti P, Hongsanan S, Aguirre-Hudson B, et al. Fungal Diversity，2014，66: 1.
[4] Ghafoor A, Hafiz A. Proc. Pakistan Sci. Conf. Karachi Part Ⅲ, 2015.
[5] HUANG Mu-yi, WANG Ji-hua, HUANG Wen-jiang(黄木易, 王纪华, 黄文江). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2003, (6): 154.
[6] CAI Cheng-jing, MA Zhan-hong, WANG Hai-guang(蔡成静, 马占鸿, 王海光). Acta Phytopathologica Sinica(植物病理学报), 2007, (1): 77.
[7] Ilias F, Bensehaile S, Medjdoub K, et al. African Journal of Microbiology Research, 2015, 9(15): 1075.
[8] Byrami F, Khodaparast S A, Pedramfar H. Journal of Crop Protection, 2013, (2): 369.
[9] JI Li, LI Jia-qi, LI Qiang(吉 莉, 李家启, 李 强). China Population, Resources and Environment(中国人口. 资源与环境), 2014, S1: 371.
[10] Huang W, Hu T, Chen H, et al. Plant Physiology & Biochemistry, 2013, 70(70C): 411.
[11] LI Xiao-long, QIN Feng, ZHAO Long-lian(李小龙, 秦 丰, 赵龙莲). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(2): 367.
[12] Rumpf T, Mahlein A K, Steiner U, et al. Computers and Electronicsin Agriculture，2010，74(1)： 91.
[13] Weng Q H. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. CRC Press USA, 2011.
[14] Jones C D, Jones J B, Lee W S. Computers and Electronics in Agriculture, 2010, 74(2): 329.
[15] Delwiche S R, Kim M S. Biological Quality and Precision Agriculture Ⅱ, 2000, 4203: 13. |
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