光谱学与光谱分析 |
|
|
|
|
|
Estimating Rice Brown Spot Disease Severity Based on Principal Component Analysis and Radial Basis Function Neural Network |
LIU Zhan-yu1,HUANG Jing-feng1*,TAO Rong-xiang2,ZHANG Hong-zhi2 |
1. Institute of Agricultural Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China 2. Institute of Plant Protection and Microbiology,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021,China |
|
|
Abstract An ASD Field Spec Pro Full Range spectrometer was used here to acquire the spectral reflectance of healthy and disease leaves cut from rice plants in the field. The leaf disease severity of rice brown spot was determined by estimating the percentage of infected surface area of rice leaves in the laboratory through phytopathologist’s observation. Three steps were taken to estimate leaf disease severity of rice brown spot. The first step was that different spectra transforming methods, namely, resampling spectrum (10 nm interval), the first- and second-order derivative spectrum based on raw hyperspectral reflectance, were conducted. The second step was that the principal component analysis (PCA) was examined to obtain the principal components (PCs) from the above transformed spectra to reduce the spectra dimensions of hyperspectral reflectance and simplify the data structure of hyperspectra. The last step was that the resampling and PCs spectra entered the Radial Basis Function neural network (RBFN) as the input vectors, and the disease severity of rice brown spot entered RBFN as the target vectors. RBFN is an effective feed forward propagation neural network, which is based on the linear combinations of corresponding radial basis functions. In general RBFN can be used to solve the problems such as regression or classification with high operation rate and efficient extrapolation capability, and quickly designed with zero error to approximate functions. The total dataset (n=262) was divided into two subsets, in which three quarters (n=210) was the training subset to train the neural network, and the remaining quarter (n=52) was the testing dataset to conduct the performance analysis of neural network. The spread constants of RBFN and various data processing methods were investigated in detail. The best prediction result was obtained by PCs spectra based on the first-order derivative using RBFN model, the root mean square of prediction error(RMSE)was small (7.73%) in the testing dataset, and the next was the resampling spectra with RMSE of 8.75%. This research demonstrated that it was feasible and reliable to estimate the disease severity of rice brown spot based on PCA-RBFN and hyperspectral reflectance at the leaf level.
|
Received: 2007-05-28
Accepted: 2007-09-02
|
|
Corresponding Authors:
HUANG Jing-feng
E-mail: hjf@zju.edu.cn
|
|
[1] Brenchley G H. Annual Review of Phytopathology, 1968, 6: 1. [2] West J S, Bravo C, Oberit R, et al. Annual Review of Phytopathology, 2003, 41: 593. [3] Jackson R D. Annual Review of Phytopathology, 1986, 24: 265. [4] Penuelas J, Filella L. Trends in Plant Science, 1998, 3(4): 151. [5] Nilsson H E. Annual Review of Phytopathology, 1995, 33: 489. [6] GAO Ning, SHAO Lu-shou(高 宁,邵陆寿). Computers and Agriculture(计算机与农业), 2003, (7): 16. [7] Adams M L, Philpot W D, Norvell W A. International Journal of Remote Sensing, 1999, 20(18): 3363. [8] Riedell W E. and Blackmer T M. Crop Science, 1999, 39: 1835. [9] Kobayashi T, Kanda E, Kitada K, et al. Pytopathology, 2001, 91(3): 316. [10] HUANG Mu-yi, WANG Ji-hua, HUANG Wen-jiang, et al(黄木易, 王纪华, 黄文江, 等). Transactions of the CSAE(农业工程学报), 2003, 19(6): 154. [11] Vigier B J, Elizabeth P, Strachan Ian B. IEEE Geosciences and Remote Sensing Letters, 2004, 1(4): 255. [12] Malthus T J, Madeira A C. Remote Sensing of Environment, 1993, 45: 107. [13] TANG Jian, YE Gong-yin, YANG Bao-jun, et al(唐 健, 叶恭银, 杨保军,等). Chinese Journal of Rice Science(中国水稻科学), 2001, 15(4): 317. [14] Haykin S. Neural Networks: A Comprehensive Foundation, Second Edition(神经网络原理,第2版). Beijing: China Machine Press(北京: 机械工业出版社), 2004, 183. [15] Ingebritsen S E, Lyon R J P. International Journal of Remote Sensing, 1985, 6(5): 687. [16] HE Yong, LI Xiao-li, SHAO Yong-ni(何 勇, 李晓丽, 邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(5): 850. [17] Cloutis E A. International Journal of Remote Sensing, 1996, 17(12): 2215. [18] Rundquist D, Han L, Schalles J, et al. Photogramme Engineering Remote Sensing, 1996, 62: 195. [19] Center for the Research and Development of FeiSi Technology(飞思科技产品研发中心编). The Theory Artificial of Neutral Networks and Its Realization in Matlab7(神经网络理论与Matlab7实现). Beijing: Electronics Industry Press(北京: 电子工业出版社), 2006. 116. [20] Carter G A. International Journal of Remote Sensing, 1994, 15(3): 697.
|
[1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[2] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[9] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[13] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[14] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[15] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
|
|
|
|