光谱学与光谱分析 |
|
|
|
|
|
Identification Methods of Crop and Weeds Based on Vis/NIR Spectroscopy and RBF-NN Model |
ZHU Deng-sheng1,PAN Jia-zhi2,HE Yong2* |
1. Department of Information Engineering, Jinghua College of Profession & Technology, Jinghua 321017, China 2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China |
|
|
Abstract The automated recognition of crop and weed by using Vis/NIR spectral in field is one of hottest research branches of agriculture engineering. If the recognition is efficient and effective, then the variate operations of herbicide or fertilizer spraying in field could be realized. Many researches have pointed out that the reflectance rate of green plant leaves could be used to identify the varieties. As the colors and surface textures of crop and weed were change in different living phases,these changes may exert great influence on the reflectance spectral of plant leaves. Vis/NIR spectra of three weeds and one crop in two different terms were recorded by spectral meter ASD FieldSpec Pro FR. Its wave band is from 325 to 1 075 nm. The scan time was 270 ms. The scanning times of per sample was set to 30 times. Firstly, 23 days after the planting of soybean, some soybean leaves and weeds leave were picked from the field, and brought to lab to record spectral. The lighting condition was controlled by an artificial halogen bulb. Secondly, on the 45th day, the same experiment was done. The three weeds were goosegrass, alligator alternanthera and emarginate amaranth. The crop was soybean seedling. Totally 378 samples were drawn for two terms. As one original reflectance spectrum contains 651 float numbers, the total data volume was huge. Using wavelet transform to compress data volume and extract characteristic spectral data was tried. The result was 114 float numbers per sample. Among them, 250 samples from two terms were used as input data to build artificial neural network model, and those left were used to check the validation. Radial basis function neural network model is widely used in pattern recognition problems. It is a nonlinear and self adaptive parallel. By assigning a 1 by 4 vector to each spectral samples, the source data could be used to build an RBF-NN model. All the samples were assigned these standard output data. Then, the left 128 samples were used to check the performance of the model. The result is that only 3 samples from the second term of goose grass were wrongly classified as alligator alternanthera, which showed that RBF neural network have strong ability to differentiate spectra of species of plant, and that there was no massive difference of NIR spectra of one plant in different life periods. This demonstrated that the NIR spectra could be used to identify crop from weed with no need to care about the living stages of these plants.
|
Received: 2007-01-19
Accepted: 2007-04-26
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] MAO Wen-hua, WANG Yi-ming, ZHANG Xiao-chao, et al(毛文华, 王一鸣, 张小超, 等). Transactions of the Chinese Society of Agriculture Engineering(CSAE)(农业工程学报), 2004, 20(5): 43. [2] Goel P K, Prasher S O, Patel R M, et al. Transactions of the ASAE, 2002, 45(2): 443. [3] MAO Wen-hua, WANG Yue-qing, WANG Yong-ming, et al(毛文华, 王月青, 王踊鸣, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(6): 83. [4] Slaughter D C, Lanini W T, Giles D K. Transactions of the ASAE, 2004, 47(6): 1907. [5] Reyer Zwiggelaar. Crop Protection, 1998, 17(3): 189. [6] Malthus T J, Maderia A C. Remote Sensing of Environ, 1993, 45: 107. [7] Borregaard T, Nielsen H, Norgaard L, et al. J. Agric. Enging. Res., 2000, 75(4): 389. [8] Jurado M Exposito, Lopez F Granados, Atenciano S, et al. Crop Protection, 2003, 22: 1177. [9] TANG Yan-feng, ZHANG Zhuo-yong, FAN Guo-qiang, et al(汤彦丰, 张卓勇, 范国强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(5): 715. [10] 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. [11] Lu R, Ariana D. Applied Engineering in Agriculture, 2002, 18(5): 585. [12] ZHU Bin, ZHENG Qing-ming, QIN Lu-ping, et al(朱 斌, 郑清明, 秦路平, 等). Academic Journal of Second Millitary Medical University(第二军医大学学报), 2003, 24(4): 455. [13] HE Yong, SONG Hai-yan, Annia G P, et al. Lecture Notes in Computer Science, 2005, 3644: 859. [14] Adams M L, Philpot W D,Norvell W A,et al. International Journal of Remote Sensing,1999,20(18):3663. [15] CHENG Yi-shong, HAO Er-bo, HU Chun-sheng, et al(程一松, 郝二波, 胡春胜, 等). Resources Science(资源科学), 2003, 25(1): 193. [16] Antihus Hernández Gómez, Yong He,Annia García Pereira. Journal of Food Engineering, 2006,77(2):313. [17] Uno Y, Prasher S O, Lacroix R, et al. Computers and Electronics in Agriculture, 2005, 47: 149. [18] Ramesh Gautam, Suranjan Panigrahi, David Franzen. Biosystems Engineering 2006, 95(3): 359. [19] ZHAO Mei-fang, LUO A-li, WU Fu-chao,et al(赵梅芳, 罗阿理, 吴福朝, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(2): 377. [20] TIAN Gao-you, YUAN Hong-fu, LIU Hui-ying, et al(田高友, 袁洪福, 刘慧颖, 等). Chinese Journal of Analytical Chemistry(分析化学), 2004, 32(9):1125.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
[4] |
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. |
[5] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[6] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[7] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[8] |
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. |
[9] |
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. |
[10] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[15] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
|
|
|
|