|
|
|
|
|
|
Optimized Detection Models for Wheat Black Tip Disease and Multiple Classification Results |
WU Ting-ting1, 2, 3, YU Ke-qiang1, 2, 3, ZHANG Hai-hui1, 2, 3*, FENG Yi4*, ZHANG Xiao1, WANG Hui-hui1 |
1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China
4. College of Agronomy, Northwest A&F University, Yangling 712100, China |
|
|
Abstract In order to explore the feasibility of detecting wheat kernel black tip (BT) disease and investigating an optimized classification model based on mainstream machine learning algorithms, a large amount of 2 760 wheat kernels spectral data of Vis/NIR bands (579~1 099 nm) were collected by self-made spectral acquisition platform. After pretreated with standard normal variate correction (SNV) of 600~1 045 nm bands, 7 kinds of data sets were established. Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) of spectral data dimensionality reduction methods, and four machine learning algorithms, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Random Forest (RF) and AdaBoost, were adopted to develop eight classification models. Results showed that Vis/NIR spectrums combined with all the machine learning methods could be used to detect BT disease with accuracies ranging from 93.3% to 98.6%, which indicated that Vis/NIR would be the more effectively compared to NIR. As SPA-SVM possessed a high average classification accuracy and PCA-AdaBoost showed better generalization performance than other algorithms, considering practical purposes, these two algorithms were adopted as optimized models in 2-category classification, 3-category classification and 4-category classification for various degrees of BT detection. Results indicated that the classification accuracies declined gradually with the classification number increasing, but the detection accuracy of non-diseased wheat kernel tended to be stable with an accuracy of more than 87.2%. Taken together, SPA-SVM performed better than PAC-AdaBoost in wheat BT disease detection. The models and conclusions of this research are intended to lead to the streamlining of VIS/NIR spectroscopy in automated wheat black tip inspection as well as to provide criteria for high speed sorting.
|
Received: 2018-03-19
Accepted: 2018-08-08
|
|
Corresponding Authors:
ZHANG Hai-hui, FENG Yi
E-mail: zhanghh@nwsuaf.edu.cn;fengyi1455@126.com
|
|
[1] Fox G, Watson L, Kelly A, et al. Developing an NIRS Method for Assessing Black Point in Single Kernels of Malting Barley. Proceedings of the 2012 World Brewing Congress, Portland, OR, 2012.
[2] Delwiche S R, Yang I C, Graybosch R A. Computers & Electronics in Agriculture, 2013, 98(7): 62.
[3] Armstrong Paul R, Maghirang Elizabeth B, Pearson Tom C. Cereal Chemistry, 2015, 92(4): 358.
[4] USDA-GIPSA-FGIS. Qrain Inspection Handbook Ⅱ. USA: Federal Grain Inspection Service, 2014. 1325.
[5] CHENG Shu-xi, XIE Chuan-qi, WANG Qiao-nan, et al(程术希, 谢传奇, 王巧男, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(5): 1362.
[6] Galvao R K, Araujo M C, Jose G E, et al. Talanta, 2005, 67(4): 736.
[7] ElMasry G, Sun D-W, Allen P. Food Research International, 2011, 44(9): 2624.
[8] ZHOU Zhi-hua(周志华). Machine Learning(机器学习). Beijing: Tsinghua University Press (北京:清华大学出版社), 2016. 10.
[9] Liu D, Sun D W, Zeng X A. Food & Bioprocess Technology, 2014, 7(2): 307. |
[1] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[2] |
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. |
[3] |
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. |
[4] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[5] |
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. |
[6] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
[7] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[8] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[9] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
[10] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
[11] |
HU Wen-feng1, 2, TANG Wei-hao1, LI Chuang1, WU Jing-jin1, MA Qing-fen1, LUO Xiao-chuan1, WANG Chao2, TANG Rong-nian1*. Estimating Nitrogen Concentration of Rubber Leaves Based on a Hybrid Learning Framework and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2050-2058. |
[12] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
[13] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
[14] |
FENG Ying-chao1, HUANG Yi-ming2*, LIU Jin-ping1, JIA Chen-peng2, CHEN Peng1, WU Shao-jie2*, REN Xu-kai3, YU Huan-wei3. On-Line Monitoring of Laser Wire Filling Welding Process Based on Emission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1927-1935. |
[15] |
JIANG Chuan-li1, ZHAO Jian-yun1, 2*, DING Yuan-yuan1, ZHAO Qin-hao1, MA Hong-yan1. Study on Soil Water Retrieval Technology of Yellow River Source Based on SPA Algorithm and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1961-1967. |
|
|
|
|