Detection of Fungal Disease on Tomato Leaves with Competitive Adaptive Reweighted Sampling and Correlation Analysis Methods
WANG Hai-long1, YANG Guo-guo1, ZHANG Yu2, BAO Yi-dan1*, HE Yong1
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2. Zhejiang Technology Institute of Economy, Hangzhou 310018, China
Abstract：Detection of grey mold on tomato leaves using hyperspectral imaging technique based on competitive adaptive reweighted sampling (CARS) and correlation analysis werestudied in this paper. Hyperspectral images of eighty healthy and eighty infected tomato leaves were captured with hyperspectral imaging systemin the spectral region of 380～1 023 nm. Spectral reflectanceof region of interest (ROI) from corrected hyperspectral image was extracted with ENVI 4.7 software. The support vector machine (SVM) model was established based on full spectral wavelengths. It obtained a good result with the discriminated accuracy of 100% in both training and testing sets. Two novel wavelength selection methods named CARS and CA were carried out to select effective wavelengths, respectively. Five wavelengths (554, 694, 696, 738 and 880 nm) and four wavelengths (527, 555, 571 and 633 nm) were obtained. Then, CARS-SVM and CA-SVM models were established based on the new wavelengths. CARS-SVM modelobtained good results with the discriminated accuracy of 100% in both training and testing sets. CA-SVM modelalso performed well with the discriminated accuracy of 91.59% in the trainingset and 92.45% in thetesting set. It demonstrated that hyperspectral imaging technique can be used for detecton of grey mold disease on tomato leaves.
王海龙，杨国国，张 瑜，鲍一丹，何 勇. 竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害[J]. 光谱学与光谱分析, 2017, 37(07): 2115-2119.
WANG Hai-long, YANG Guo-guo, ZHANG Yu, BAO Yi-dan, HE Yong. Detection of Fungal Disease on Tomato Leaves with Competitive Adaptive Reweighted Sampling and Correlation Analysis Methods. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2115-2119.
[1] YE Gong-yin(叶恭银). Plant Protection(植物保护学). Hangzhou: Zhejiang University Press(杭州：浙江大学出版社), 2006. 349.
[2] GU Yun, LI Gui-fang, ZHAO Chuan-de(顾 耘，李桂舫，赵川德). Shucai Binchonghai Yuanse Tupu Yufangzhi(蔬菜病虫害原色图谱与防治). Beijing: Chemical Industry Press(北京：化学工业出版社)，2010, 44.
[3] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72(1): 1.
[4] Wu Di, Chen J Y, Lu B Y, et al. Food Chemistry, 2012, 135: 2147.
[5] Liu F, He Y, Wang L, ea al. Food and Bioprocess Technology, 2009, 4(8): 1331.
[6] Wu Di, Nie P C, He Y, et al. Food and Bioprocess Technology, 2012, 5(4): 1402.
[7] XIE Chuan-qi, WANG Jia-yue, FENG Lei, et al(谢传奇，王佳悦，冯 雷，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(6): 1603.
[8] Williams P J, Geladi P, Britz T J, et al. Journal of Cereal Science, 2012, 55(3): 272.
[9] Bauriegel E, Giebel A, Geyer M, et al. Computers and Electronics in Agriculture, 2010, 75(2): 304.
[10] Elmasry G, Sun D W, Allen P. Journal of Food Engineering, 2012, 110(1): 127.
[11] CAI Qing-sheng(蔡庆生). Plant Physiology(植物生理学). Beijing: China Agricultural University Press(北京：中国农业大学出版社)，2011. 280.
[12] Kurtulmus F, Lee W S, Vardar A. Precision Agriculture, 2014, 15(1): 57.
[13] Sengupta S, Lee W S. Biosystems Engineering, 2014, 117(1): 51.
[14] Cortes C, Vapnik V. Machine Learning, 1995, 20(3): 273.
[15] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimca Acta, 2009, 648: 77.
[16] Wu D, Sun D W. Talanta, 2013, 111(13): 39.
[17] Lee K J, Kang S W, Delwiche S R, et al. Sensing and Instrumentation for Food Quality and Safety, 2008, 2(2): 90.
[18] Niphadkar N P, Burks T F, Qin J W, et al. International Agricultural Engineering Journal, 2013, 22(3): 41.
[19] Bulanon D M, Burks T F, Kim D G, et al. Agricultural Engineering International: CIGR Journal, 2013, 15(3)：171.
