|
|
|
|
|
|
| Hyperspectral Imaging for Detection of Leguminivora Glycinivorella Based on 3D Few-Shot Meta-Learning Model |
| GUI Jiang-sheng1, FEI Jing-yi1, FU Xia-ping2 |
1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China |
|
|
|
|
Abstract In order to reduce the influence of leguminivora glycinivorella on soybean production and quality, and to realize the rapid detection of leguminivora glycinivorella, this paper proposed a leguminivora glycinivorella detection model based on 3D-Realtion Network (3D-RN) model. Firstly, collect the hyperspectral images of 20 soybeans that are attached to eggs, larvae, gnawed and normal soybeans, respectively, and extract the region of interest (ROI) to establish a 3D-RN model based on hyperspectral images. The accuracy of the final model reached 82%±2.50%. Compared to the Model-Agnostic Meta-Learning (MAML) and Matching Network (MN) models, the 3D-RN model can fully measure the distance between sample features, and the recognition effect is greatly improved. Thus, this research shows that the 3D-RN model based on the hyperspectral image can detect leguminivora glycinivorella in a small number of samples. The method of combining few-shot meta-learning with hyperspectral provides a new idea for pest detection.
|
|
Received: 2020-07-11
Accepted: 2020-11-26
|
|
|
|
[1] LI Zhao-jun, TIAN Ru-mei, PU Yan-yan, et al(李照君，田汝美，蒲艳艳，等). Soybean Science(大豆科学), 2020, 39(4): 577.
[2] GAO Yu, SHI Shu-sen(高 宇，史树森). Soybean Science (大豆科学), 2016, 35(6): 1025.
[3] DAI Yu-ting, ZHOU Bo, WANG Jun(代雨婷, 周 博, 王 俊). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(3): 313.
[4] Tanaka F, Magariyama Y, Miyanoshita A. Food Chemistry, 2020, 303, 125381.
[5] Cui S, Ling P, Zhu H, et al. Sensors, 2018, 18(2): 378.
[6] Nanni L, Maguolo G, Pancino F. Ecological Informatics, 2020, 57: 101089.
[7] LUO Qing-qing, HUANG Tie-cheng, CHEN Shu-jiang, et al(罗青青, 黄铁成, 陈蜀江, 等). Jiangsu Journal of Agricultural Sciences (江苏农业学报), 2019, 35(4): 798.
[8] Li W, Chen P, Wang B, et al. Scientific Reports, 2019, 9(1)：1.
[9] Rustia D J, Lin C E, Chung J, et al. Journal of Asia-Pacific Entomology, 2020, 23(1)：17.
[10] LI Cui-ling, JIANG Kai, MA Wei, et al(李翠玲，姜 凯，马 伟，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(1): 253.
[11] Lin T L, Chang H Y, Chen K H. Journal of Internet Technology, 2020, 21(2)：605.
[12] Sung F, Yang Y, Zhang L, et al. Computer Vision and Pattern Recognition, 2018, 2018: 1199 (doi: 10.1109/CVPR.2018.00131).
[13] Finn C, Abbeel P, Levine S. Proc of Machine Loarning Research, 2017, 70: 1126.
[14] Vinyals O, Blundell C, Lillicrap T, et al. Matching Networks for One Shot Learning. Proceedings of the Neural Information Processing Systems Conference, 2016. 3630. |
| [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] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
| [3] |
XIA Ming-ming1, 2, LIU Jia3, WU Meng1, 2, FAN Jian-bo1, 2, LIU Xiao-li1, 2, CHEN Ling1, 2, MA Xin-ling1, 2, LI Zhong-pei1, 2, LIU Ming1, 2*. Three Dimensional Fluorescence Characteristics of Soluble Organic Matter From Different Straw Decomposition Products Treated With Calcium Containing Additives[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 118-124. |
| [4] |
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. |
| [5] |
GU Yi-lu1, 2，PEI Jing-cheng1, 2*，ZHANG Yu-hui1, 2，YIN Xi-yan1, 2，YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
| [6] |
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. |
| [7] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [8] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [9] |
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. |
| [10] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [11] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2，WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
| [12] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
| [13] |
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. |
| [14] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [15] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
|
|
|
|
