A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics
Source abstract
Abstract
China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Measurement and Detection Methods · Spectroscopy Techniques in Biomedical and Chemical Research · Remote Sensing and Land Use
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Physics and Mechatronic Engineering, Guizhou Minzu University, Guiyang 550025, China
School of Equipment Manufacturing Polytechnic, Office of Academic Research, Guiyang 550008, China
School of Mechanical Engineering, Guizhou Institute of Technology, Guiyang 550003, China
Funding
Natural Science Foundation of Guizhou Minzu University · Doctoral funding project of Guizhou Minzu University · Guizhou Province Science and Technology Support Program Project · Guiyang Science and Technology Support Plan Project · Guizhou Provincial Department of Education supports the scientific research platform
References (100+)
Show source-provided references
1. Development and Prospect of Hyperspectral Imager and Its Application10.11728/cjss2021.01.022 ↗
2. Airborne Imaging Spectrometer: A New Tool for Remote Sensing10.1109/TGRS.1984.6499168 ↗
3. Operational Modular Imaging Spectrometer
4. Image Fusion for Hyperspectral Date of PHI and High-Resolution Aerial Image
5. Yang, Z.H., Han, J.F., Gong, D.P., and Li, Z.X. (2003). The Development and Application of Hyperspectral-Remote-Sensing Technology. Hydrogr. Surv. Charting, 55–58.
6. Hyperspectral Imaging Technology in Application of Agricultural Products Detection
7. Overview of the Application of Hyperspectral Imaging Technology in Agriculture
8. Progress in Research on Rapid and Non-Destructive Detection of Seed Quality Based on Spectroscopy and Imaging Technology
9. Application of Airborne Hyperspectral Imaging Technology to the Ecological Environment Protection of Jiangsu, Anhui and Zhejiang Provinces at Yangtze River Economic Belt
10. Tao, H.H., Yao, Y.T., and Lin, W.J. (2021). Research on the Application of UAV Technology in Monitoring Water Quality Pollution in Rivers and Lakes. China Hydropower Electrif., 42–45.
11. Application of Hyper-Spectral Remote Sensing Technology in Environmental Protection
12. Extraction of Cancer Information from Liver Biopsy with Hyperspectral Imaging Technology
13. Discussions of Hyperspectral Imaging for Medical Diagnostics
14. Recent Advances of Hyperspectral Imaging Application in Biomedicine
15. Application of Hyperspectral Imaging Technology in the Protection of Palace Museum Calligraphy and Painting Cultural Relics
16. Application of Large-Format Hyperspectral Scanning System in Cultural Relics Research
17. Comprehensive Review of the Application of Hyperspectral Imaging Technology in the Analysis of Ancient Chinese Calligraphy and Painting
18. Recent Progress in Technologies for Non-destructive Detection of Fruit Diseases and Pests
19. Research Progress on the Application of Hyperspectral Imaging Technology in Tea Production
20. Study on Early Detection of Gray Mold on Tomato Leaves Using Hyperspectral Imaging Technique
21. Hyperspectral Imaging Detection of Cercospora Leaf Spot of Muskmelon
22. Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined with Deep Learning
23. Identification of Cucumber Disease and Insect Pest Based on Hyperspectral Imaging
24. Status and Prospects of Target Classification Methods Based on Hyperspectral Images
25. Wang, D., Luan, Y.Q., Tan, Z.J., and Wei, W. (2024). Detection of Pesticide Residues in Broccoli Based on Hyperspectral Imaging and Convolutional Neural Network. J/OL. Food Ind. Technol., 1–15. [2024-08-09].
26. Identification of Ginkgo Fruit Species by Hyperspectral Image Combined With PSO-SVM
27. The Discrimination of Organic and Conventional Eggs Based on Hyperspectral Technology
28. Mix Convolutional Neural Networks for Hyperspectral Wheat Variety Discrimination
29. Application of Hyperspectral Imaging Technology in the Classification of Tobacco Leaves and Foreign Matter
30. Identification of Sorghum Breed by Hyperspectral Image Technology
31. Monitoring of Nitrogen Content in Winter Wheat Based on UAV Hyperspectral Imagery
32. Wu, C.Q., Tong, Q.X., and Zheng, L.F. (2005). Pretreatment of Flied and Image Spectra Data. Remote Sens. Technol. Appl., 50–55.
33. Fan, N.N., Li, Z.Y., Fan, W.Y., Wang, B.Y., Tan, B.X., and Liu, G.F. (2007). Preprocessing of PHI-3 Hyperspectral Data. Res. Soil Water Conserv., 283–290.
34. Research on Dimensionality Reduction Methods for Hyperspectral Data
35. Wang, X.H., Xiao, P., and Guo, J.M. (2006). Research on Hyperspectral Data Dimensionality Reduction Techniques. Bull. Soil Water Conserv., 89–91.
36. Su, H.J., and Du, P.J. (2006). Study on Feature Selection and Extraction of Hyperspectral Data. Remote Sens. Technol. Appl., 288–293.
37. Research on Feature Extraction of Soil Hyperspectral Data Based on Manifold Learning
38. Advancement of Hyperspectral Image Processing and Information Extraction10.1109/TGRS.2015.2473705 ↗
39. Comparison of two Methods for Atmospheric Correction of Hyper-spectral Thermal Infrared Data
40. A Highly Efficient Atmospheric Correction Method for HJ-1A/HSI and the Exploration on Its Application Capability
41. Atmospheric Correction of Hyperspectral Remote Sensing Image Based on FLAASH
42. Discrimination Model of Tobacco Leaf Field Maturity Based on Hyperspectral Imaging Technology
43. Research on Hyperspectral Remote Sensing Estimation of SPAD Value of Camellia Oleifera Leaves Based on Different Preprocessing Methods
44. Preprocessing Methods of Near-Infrared Reflectance Spectral Data for Coal Gangue Identification
45. Research on Hyperspectral Data Acquisition and Preprocessing Methods of Water-Injected Lamb
46. Overview of the Least Squares Fitting Principle of Savitzky-Golay Smoothing Filters
47. Optimizing Savitzky-Golay Parameters and Its Smoothing Pretreatment for FTIR Gas Spectra
48. Data Preprocessing of Broad-Spectrum Tunable-Diode-Laser Absorption Spectroscopy
49. Support Vector Machine Identification of Fresh Apricot Defects with Different Spectral Pre-Processing Methods
50. Development and Prospect of Hyperspectral Target Classification Technology
51. Non-destructive Detection of Moisture Content in Fresh Sweet Corn Based on Hyperspectral Technology
52. External Quality Detection of Potatoes Based on Hyperspectral Technology
53. External Defect Detection of Eggplants Based on Hyperspectral Imaging
54. Non-destructive Identification of Soybean Varieties based on Hyperspectral Imaging Technology and GBDT
55. Wang, B.N. (1983). Derivative Spectrophotometry Method. Chin. J. Anal. Chem., 149–158.
56. Estimating Canopy Leaf Water Content in Wheat Based on Derivative Spectra
57. Extraction of First Derivative Spectrum Features of Soil Organic Matter via Wavelet De-Noising
58. Research on effective bands for identifying minor apple damage based on hyperspectral imaging
59. Progress and effect of spectral data pretreatment in NIR analytical technique
60. Study on the Selection of Spectral Preprocessing Methods
61. Optimal Application of Combined Multiple Scattering Correction and Savitzky-Golay Smoothing Model in Near-Infrared Spectral Analysis of Soil Organic Matter
62. Lu, Y.J., Qu, Y.L., and Song, M. (2007). Research on the Correlation Chart of Near Infrared Spectra by Using Multiple Scatter Correction Technique. Spectrosc. Spectr. Anal., 877–880.
63. Research on identification technology of fresh and frozen-thawed beef based on near-infrared hyperspectral imaging
64. A Nondestructive Method Identifying Varieties of Green Tea Based on Near Infrared Spectroscopy and Chemometrics
65. Nondestructive Identification of Soybean Milk Powder Based on Near Infrared Spectroscopy and Optimized Pretreatment Method
66. Wu, Y.R., Jin, X.K., Feng, J.Q., Zhang, H.F., Qiu, Y.J., Yang, J.Y., Cong, M.F., and Zhu, C.Y. Cotton Impurity Detection Based on Hyperspectral Imaging Technology. J/OL. Advanced Textile Technology 1–11 [2024-08-13].
67. Determination of Storage year of white Tea Based on Hyperspectral Imaging Technology
68. Study on Denoising Near Infrared Spectra of Wood Based on Wavelet Transform
69. Xie, J.C., Zhang, D.L., and Xu, W.L. (2002). Overview on Wavelet Image Denoising. J. Image Graph., 3–11.
70. Wen, L., Liu, Z.S., and Ge, Y.J. (2002). Several Methods of Wavelet Denoising. J. Hefei Univ. Technol. (Nat. Sci.), 167–172.
71. Qin, X., and Shen, L.S. (2000). Wavelet Transform and Its Application in Spectral Analysis. Spectrosc. Spectr. Anal., 892–897.
72. Study on Non-destructive Detection of Peanut Water Content Based on Hyperspectral Imaging Technology and BO-XGBoost
73. Dimensionality Reduction for Hyperspectral Remote Sensing: Advances, Challenges, and Prospects10.11834/jrs.20210354 ↗
74. Dimensionality Reduction Algorithm for Hyperspectral Image Based on Self-Supervised Learning
75. An Overview of Hyperspectral Image Feature Extraction
76. Xiao, P., and Wang, X.H. (2006). Hyperspectral Data Classification based on Feature Extraction. J. Northwest Univ. (Nat. Sci. Ed.), 497–500.
77. Dimensionality Reduction Method for Multispectral Data Based on PCA and ICA10.3788/GXJS20144002.0180 ↗
78. Hyperspectral Identification of Rice Diseases and Pests based on Principal Component Analysis and Probabilistic Neural Network
79. Study on the Hyperspectral Remote Sensed Image Classify based on PCA and SVM
80. Tian, Y., Zhao, C.H., and Ji, Y.X. (2007). The Principal Component Analysis Applied to Hyperspectral Remote Sensing Image Dimensional Reduction. Nat. Sci. J. Harbin Norm. Univ., 58–60.
81. Feng, Y., He, M.Y., Song, J.H., and Wei, J. (2007). ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images. J. Electron. Inf. Technol., 2871–2875.
82. Hyperspectral Remote Sensing Image Classification Based on ICA and SVM Algorithm
83. Independent Component Analysis for Spectral Unmixing in Hyperspectral Remote Sensing Image
84. Li, H.T., Gu, H.Y., Zhang, B., and Gao, L.R. (2007). Research on Hyperspectral Remote Sensing Image Classification Based on MNF and SVM. Remote Sens. Inf., 12–103.
85. Feature Extraction of Hyperspectral Remote Sensing Image Based on Kernel Minimum Noise Fraction Transformation
86. Oil Spill Information Extraction Method Based on Airborne Hyperspectral Remote Sensing Data
87. Classification and Feature Extraction of Hyperspectral Images based on Improved Minimum Noise Fraction Transformation
88. Analysis of Chlorophyll in Gannan Navel Orange with Algorithm of GA and SPA Based on Hyperspectral
89. Band Selection for Hyperspectral Imagery based on Combination of Genetic Algorithm and Ant Colony Algorithm
90. Determination of Conductivity in Sweet Corn Seeds with Algorithm of GA and SPA Based on Hyperspectral Imaging Technique
91. A New Data Dimension Reduction Method Based on Convolution in The Application of Authenticity Identification of Traditional Chinese Medicine LongGu10.1088/1742-6596/2504/1/012035 ↗
92. Feng, L.L., Wang, C.P., Wu, T.J., and Zhang, J.S. (2024). Dimensionality Reduction Method for Manifold Learning Based on Variational Autoencoder. J/OL J. Comput.-Aided Des. Comput. Graph., 1–7.
93. Tan, K., and Du, P.J. (2008). Hyperspectral Remote Sensing Image Classification Based on Support Vector Machine. J. Infrared Millim. Waves, 123–128.10.3724/SP.J.1010.2008.00123 ↗
94. Wood Species Recognition with Microscopic Hyper-Spectral Imaging and Composite Kernel SVM
95. Study on Maturity Discrimination of Hami Melon with Hyperspectral Imaging Technology Combined with Characteristic Wavelengths Selection Methods and SVM
96. Wheat Leaf Area Index Inversion with Hyperspectral Remote sensing based on Support Vector Regression Algorithm
97. Research on the Classification Method of Benthic Fauna Based on Hyperspectral Data and Random Forest Algorithm
98. Lithological Classification and Analysis Using Hyperion Hyperspectral Data and Random Forest Method
99. Variety Recognition of Chinese Cabbage Seeds by Hyperspectral Imaging Combined with Machine Learning
100. Hyperspectral Image Classification Combing Local Binary Patterns and K-nearest Neighbors Algorithm