|
|
|
|
|
|
| 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] |
LIU Yan-de, WANG Shun. Research on Non-Destructive Testing of Navel Orange Shelf Life Imaging Based on Hyperspectral Image and Spectrum Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1792-1797. |
| [2] |
CHEN Yuan-zhe1, WANG Qiao-hua1, 2*, TIAN Wen-qiang1, XU Bu-yun1, HU Jian-chao1. Nondestructive Determinations of Texture and Quality of Preserved Egg Gel by Hyperspectral Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1985-1992. |
| [3] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [4] |
JI Jiang-tao1, 2, LI Peng-ge1, JIN Xin1, 2*, MA Hao1, 2, LI Ming-yong1. Study on Quantitative Detection of Tomato Seedling Robustness
in Spring Seedling Transplanting Period Based on VIS-NIR
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1741-1748. |
| [5] |
ZHENG Yi1, 2, 3, WANG Yao1, 2, LIU Yan1, 2*. Study on Classification and Recognition of Mountain Meadow Vegetation Based on Seasonal Characteristics of Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1939-1947. |
| [6] |
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*. Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1965-1973. |
| [7] |
JIANG Rong-chang1, 2, GU Ming-sheng2, ZHAO Qing-he1, LI Xin-ran1, SHEN Jing-xin1, 3, SU Zhong-bin1*. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1385-1392. |
| [8] |
JI Rong-hua1, 2, ZHAO Ying-ying2, LI Min-zan2, ZHENG Li-hua2*. Research on Prediction Model of Soil Nitrogen Content Based on
Encoder-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1372-1377. |
| [9] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [10] |
JIANG Qing-hu1, LIU Feng1, YU Dong-yue2, 3, LUO Hui2, 3, LIANG Qiong3*, ZHANG Yan-jun3*. Rapid Measurement of the Pharmacological Active Constituents in Herba Epimedii Using Hyperspectral Analysis Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1445-1450. |
| [11] |
ZHAO Yong1, HE Men-yuan1, WANG Bo-lin2, ZHAO Rong2, MENG Zong1*. Classification of Mycoplasma Pneumoniae Strains Based on
One-Dimensional Convolutional Neural Network and
Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1439-1444. |
| [12] |
WENG Shi-zhuang*, CHU Zhao-jie, WANG Man-qin, WANG Nian. Reflectance Spectroscopy for Accurate and Fast Analysis of Saturated
Fatty Acid of Edible Oil Using Spectroscopy-Based 2D Convolution
Regression Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1490-1496. |
| [13] |
ZHANG Yu-yang, CHEN Mei-hua*, YE Shuang, ZHENG Jin-yu. Research of Geographical Origin of Sapphire Based on Three-Dimensional Fluorescence Spectroscopy: A Case Study in Sri Lanka and Laos Sapphires[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1508-1513. |
| [14] |
DAI Ruo-chen1, TANG Huan2*, TANG Bin1*, ZHAO Ming-fu1, DAI Li-yong1, ZHAO Ya3, LONG Zou-rong1, ZHONG Nian-bing1. Study on Detection Method of Foxing on Paper Artifacts Based on
Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1567-1571. |
| [15] |
LI De-hui1, WU Tai-xia1*, WANG Shu-dong2*, LI Zhe-hua1, TIAN Yi-wei1, FEI Xiao-long1, LIU Yang1, LEI Yong3, LI Guang-hua3. Hyperspectral Indices for Identification of Red Pigments Used in Cultural Relic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1588-1594. |
|
|
|
|
