Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
Source abstract
Abstract
The efficiency of hyper- and multispectral imaging (HSI and MSI) has gained considerable attention in research on plant phenotyping. This is due to their ease of use while being considered a nondestructive technology. Unlike current point-scanned spectroscopy, both HSI and MSI extract spatial and spectral information while covering a wide range of a plant body. Moreover, it is necessary to equip the extracted information with multivariate calibration techniques, followed by model evaluation. To date, the application of HSI and MSI for monitoring plant growth under a controlled environment is emerging and showing a good trend. Our systematic literature review discusses spectroscopy imaging techniques and their chemometric approaches as a sustainable sensor technology to detect plant traits. In conclusion, we also explore the possibility of carrying out HSI and MSI during plant trait analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Remote Sensing in Agriculture · Leaf Properties and Growth Measurement
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, Chungdae-ro, Seowon-gu, Cheongju 28644, Republic of Korea
Funding
Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) · Ministry of Agriculture, Food and Rural Affairs (MAFRA)
References (100+)
Show source-provided references
1. FAO (2018). The Future of Food and Agriculture—Alternative Pathways to 2050, FAO.
2. A Meta-Analysis of Projected Global Food Demand and Population at Risk of Hunger for the Period 2010–205010.1038/s43016-021-00322-9 ↗
3. Benefits to Plant Health and Productivity From Enhancing Plant Microbial Symbionts10.3389/fpls.2020.610065 ↗
4. Food Security and Nutrition in the World
5. Food Security and Canada’s Agricultural System Challenged by COVID-1910.1111/cjag.12227 ↗
6. Implications of the Russia–Ukraine War for Global Food Security10.1038/s41562-022-01391-x ↗
7. Public Health Risks Related to Food Safety Issues in the Food Market: A Systematic Literature Review10.1186/s12199-019-0825-5 ↗
8. Uyttendaele, M., Franz, E., and Schlüter, O. (2016). Food Safety, a Global Challenge. Int. J. Environ. Res. Public Health, 13.10.3390/ijerph13010067 ↗
9. The Sources of Chemical Contaminants in Food and Their Health Implications10.3389/fphar.2017.00830 ↗
10. Tunny, S.S., Amanah, H.Z., Faqeerzada, M.A., Wakholi, C., Kim, M.S., Baek, I., and Cho, B.K. (2022). Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables. Sensors, 22.10.3390/s22051775 ↗
11. Darwish, A., Ricci, M., Zidane, F., Vasquez, J.A.T., Casu, M.R., Lanteri, J., Migliaccio, C., and Vipiana, F. (2022). Physical Contamination Detection in Food Industry Using Microwave and Machine Learning. Electronics, 11.10.3390/electronics11193115 ↗
12. Momtaz, M., Bubli, S.Y., and Khan, M.S. (2023). Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review. Foods, 12.10.3390/foods12010199 ↗
13. Detection of Coconut (Cocos Nucivera) Sugar Adulteration in Palm (Arenga Pinnata Merrill) Sugar by Fourier Transform Infrared (Ft-Ir) Spectroscopy10.26656/fr.2017.5(S2).013 ↗
14. Genetically Modified Foods: Safety, Risks and Public Concerns—A Review10.1007/s13197-012-0899-1 ↗
15. Hyperspectral and Multispectral Imaging for Evaluating Food Safety and Quality10.1016/j.jfoodeng.2013.04.001 ↗
16. Hyperspectral Imaging—An Emerging Process Analytical Tool for Food Quality and Safety Control10.1016/j.tifs.2007.06.001 ↗
17. Plant Salinity Stress: Many Unanswered Questions Remain10.3389/fpls.2019.00080 ↗
18. Zhao, S., Zhang, Q., Liu, M., Zhou, H., Ma, C., and Wang, P. (2021). Regulation of Plant Responses to Salt Stress. Int. J. Mol. Sci., 22.10.3390/ijms22094609 ↗
19. Differential Physiological Response to Heat and Cold Stress of Tomato Plants and Its Implication on Fruit Quality10.1016/j.jplph.2021.153581 ↗
20. Delineating the Role of Plant Stature towards Heat Stress Tolerance in Field Pea (Pisum sativum L.)10.1016/j.heliyon.2023.e14539 ↗
21. Physiological and Molecular Responses of Roots Differ from Those of Leaves in Spinach Plants Subjected to Short-Term Drought Stress10.1016/j.sajb.2022.09.032 ↗
22. Seleiman, M.F., Al-Suhaibani, N., Ali, N., Akmal, M., Alotaibi, M., Refay, Y., Dindaroglu, T., Haleem Abdul-Wajid, H., and Leonardo Battaglia, M. (2021). Drought Stress Impacts on Plants and Different Approaches to Alleviate Its Adverse Effects. Plants, 10.10.3390/plants10020259 ↗
23. The Application of Genomics and Bioinformatics to Accelerate Crop Improvement in a Changing Climate10.1016/j.pbi.2016.02.002 ↗
24. Plant Abiotic Stress Tolerance: Insights into Resilience Build-Up10.1007/s12038-020-00088-5 ↗
25. Comparative Analysis of Long Noncoding RNAs in Angiosperms and Characterization of Long Noncoding RNAs in Response to Heat Stress in Chinese Cabbage10.1038/s41438-021-00484-4 ↗
26. Engineering Heat Tolerance in Potato by Temperature-dependent Expression of a Specific Allele of HEAT-SHOCK COGNATE 7010.1111/pbi.12760 ↗
27. Tansley Review No. 102: Plant Hybridization10.1046/j.1469-8137.1998.00315.x ↗
28. Modernization in Plant Breeding Approaches for Improving Biotic Stress Resistance in Crop Plants10.3906/tar-1406-176 ↗
29. Physiological, Biochemical, and Molecular Mechanisms of Heat Stress Tolerance in Plants10.3390/ijms14059643 ↗
30. Physiological and Biochemical Responses to High Light and Temperature Stress in Plants10.1016/j.envexpbot.2017.05.002 ↗
31. Modelling Photosynthetic Responses to Temperature of Grapevine (Vitis vinifera Cv. Semillon) Leaves on Vines Grown in a Hot Climate10.1111/j.1365-3040.2011.02471.x ↗
32. Kim, G., Lee, H., Wi, S.H., and Cho, B.K. (2022). Snapshot-Based Visible-Near Infrared Multispectral Imaging for Early Screening of Heat Injury during Growth of Chinese Cabbage. Appl. Sci., 12.10.3390/app12189340 ↗
33. Gas Chromatography-Mass Spectrometry and Liquid Chromatography-Mass Spectrometry Metabolomics Platforms: Tools for Plant Oligosaccharides Analysis
34. Determination of Tetracyclines and Their Epimers in Agricultural Soil Fertilized with Swine Manure by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry
35. Nondestructive Detection of the Bioactive Components and Nutritional Value in Restructured Functional Foods10.1016/j.cofs.2022.100986 ↗
36. Evaluation of Bioactive Compounds from Ficus carica L. Leaf Extracts via High-Performance Thin-Layer Chromatography Combined with Effect-Directed Analysis10.1016/j.chroma.2023.464241 ↗
37. Nondestructive Prediction of Pepper Seed Viability Using Single and Fusion Information of Hyperspectral and X-ray Images10.1016/j.sna.2022.114151 ↗
38. Rapid Seed Viability Prediction of Sophora Japonica by Improved Successive Projection Algorithm and Hyperspectral Imaging10.1016/j.infrared.2022.104143 ↗
39. Non-Destructive Prediction of Protein Contents of Soybean Seeds Using near-Infrared Hyperspectral Imaging10.1016/j.infrared.2022.104365 ↗
40. Amanah, H.Z., Wakholi, C., Perez, M., Faqeerzada, M.A., Tunny, S.S., Masithoh, R.E., Choung, M.G., Kim, K.H., Lee, W.H., and Cho, B.K. (2021). Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds. Appl. Sci., 11.10.3390/app11114841 ↗
41. Vis/NIR Hyperspectral Imaging Distinguishes Sub-Population, Production Environment, and Physicochemical Grain Properties in Rice10.1038/s41598-020-65999-7 ↗
42. Faqeerzada, M.A., Perez, M., Lohumi, S., Lee, H., Kim, G., Wakholi, C., Joshi, R., and Cho, B.K. (2020). Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds. Appl. Sci., 10.10.3390/app10186569 ↗
43. Using Wavelet Textural Features of Visible and Near Infrared Hyperspectral Image to Differentiate Between Fresh and Frozen–Thawed Pork10.1007/s11947-014-1330-x ↗
44. Dharmawan, A., Masithoh, R.E., and Amanah, H.Z. (2023). Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins. Foods, 12.10.3390/foods12112112 ↗
45. Jiang, H., Hu, Y., Jiang, X., and Zhou, H. (2022). Maturity Stage Discrimination of Camellia Oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging. Molecules, 27.10.3390/molecules27196318 ↗
46. Nondestructive Detection of Maturity of Watermelon by Spectral Characteristic Using NIR Diffuse Transmittance Technique10.1016/j.scienta.2019.108718 ↗
47. Pattern Recognition-Based Raman Spectroscopy for Non-Destructive Detection of Pomegranates during Maturity10.1016/j.saa.2020.118127 ↗
48. Zhang, M., Shen, M., Pu, Y., Li, H., Zhang, B., Zhang, Z., Ren, X., and Zhao, J. (2022). Rapid Identification of Apple Maturity Based on Multispectral Sensor Combined with Spectral Shape Features. Horticulturae, 8.10.3390/horticulturae8050361 ↗
49. Close-Range Hyperspectral Imaging of Whole Plants for Digital Phenotyping: Recent Applications and Illumination Correction Approaches10.1016/j.compag.2020.105780 ↗
50. Close Range Hyperspectral Imaging of Plants: A Review10.1016/j.biosystemseng.2017.09.009 ↗
51. Hyperspectral Imaging and 3D Technologies for Plant Phenotyping: From Satellite to Close-Range Sensing10.1016/j.compag.2020.105621 ↗
52. Applications of Hyperspectral Imaging in Plant Phenotyping10.1016/j.tplants.2021.06.009 ↗
53. Nondestructive Detection of Rape Leaf Chlorophyll Level Based on Vis-NIR Spectroscopy10.1016/j.saa.2019.117202 ↗
54. Hyperspectral Remote Sensing of Plant Pigments10.1093/jxb/erl123 ↗
55. Hyperspectral and Multispectral Satellite Sensors for Mappingchlorophyll Content in a Mediterranean Pinus sylvestris L. plantation
56. A Review of Band Assignments in near Infrared Spectra of Wood and Wood Components10.1255/jnirs.955 ↗
57. Near Infra Red Spectroscopy-An Overview
58. Snapshot Spectral Imaging with Parallel Metasystems10.1126/sciadv.abc7646 ↗
59. Hyperspectral Image Analysis Techniques for the Detection and Classification of the Early Onset of Plant Disease and Stress10.1186/s13007-017-0233-z ↗
60. Multispectral Imaging for Presymptomatic Analysis of Light Leaf Spot in Oilseed Rape10.1186/s13007-019-0389-9 ↗
61. Faqeerzada, M.A., Lohumi, S., Kim, G., Joshi, R., Lee, H., Kim, M.S., and Cho, B.K. (2020). Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method. Sensors, 20.10.3390/s20205855 ↗
62. Improving Rice Nitrogen Stress Diagnosis by Denoising Strips in Hyperspectral Images via Deep Learning10.1016/j.biosystemseng.2022.05.001 ↗
63. Improving the Accuracy of the Hyperspectral Model for Apple Canopy Water Content Prediction Using the Equidistant Sampling Method10.1038/s41598-017-11545-x ↗
64. Temporal Dynamics of Maize Plant Growth, Water Use, and Leaf Water Content Using Automated High Throughput RGB and Hyperspectral Imaging10.1016/j.compag.2016.07.028 ↗
65. High Throughput in Vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging10.3389/fpls.2017.01348 ↗
66. The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum)10.3389/fpls.2019.01380 ↗
67. Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis10.3389/fchem.2018.00576 ↗
68. Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle10.1007/s12393-016-9147-1 ↗
69. Determination of the Total Volatile Basic Nitrogen (TVB-N) Content in Pork Meat Using Hyperspectral Fluorescence Imaging10.1016/j.snb.2017.12.102 ↗
70. Ren, J., Wang, R., Liu, G., Feng, R., Wang, Y., and Wu, W. (2020). Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images. Remote Sens., 12.10.3390/rs12071104 ↗
71. Study on Outlier Detection Method of the near Infrared Spectroscopy Analysis by Probability Metric10.1016/j.saa.2022.121473 ↗
72. Principal Component Analysis Applied to Fourier Transform Infrared Spectroscopy for the Design of Calibration Sets for Glycerol Prediction Models in Wine and for the Detection and Classification of Outlier Samples10.1021/jf035431q ↗
73. Application of Principal Component Analysis to Detect Outliers and Spectral Deviations in Near-Field Surface-Enhanced Raman Spectra10.1016/S0003-2670(01)01267-3 ↗
74. Amanah, H.Z., Tunny, S.S., Masithoh, R.E., Choung, M.G., Kim, K.H., Kim, M.S., Baek, I., Lee, W.H., and Cho, B.K. (2022). Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques. Foods, 11.10.3390/foods11020232 ↗
75. Shen, T., Zhang, C., Liu, F., Wang, W., Lu, Y., Chen, R., and He, Y. (2020). High-Throughput Screening of Free Proline Content in Rice Leaf under Cadmium Stress Using Hyperspectral Imaging with Chemometrics. Sensors, 20.10.3390/s20113229 ↗
76. Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology10.3389/fpls.2021.640914 ↗
77. Hyperspectral Imaging with Chemometrics for Non-Destructive Determination of Cannabinoids in Floral and Leaf Materials of Industrial Hemp (Cannabis sativa L.)10.1016/j.compag.2022.107387 ↗
78. Quantitative Detection of Benzoyl Peroxide in Wheat Flour Using Line-Scan Short-Wave Infrared Hyperspectral Imaging10.1016/j.snb.2021.130997 ↗
79. A Systematic Review on Hyperspectral Imaging Technology with a Machine and Deep Learning Methodology for Agricultural Applications10.1016/j.ecoinf.2022.101678 ↗
80. Hyperspectral Technique Combined with Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce10.3389/fpls.2022.927832 ↗
81. A Review of Deep Learning Used in the Hyperspectral Image Analysis for Agriculture10.1007/s10462-021-10018-y ↗
82. Estimation of Nitrogen Content in Cucumber Plant (Cucumis sativus L.) Leaves Using Hyperspectral Imaging Data with Neural Network and Partial Least Squares Regressions10.1016/j.chemolab.2021.104404 ↗
83. Automatic Detection of Multi-Type Defects on Potatoes Using Multispectral Imaging Combined with a Deep Learning Model10.1016/j.jfoodeng.2022.111213 ↗
84. Detection of Fusarium Head Blight in Wheat Using Hyperspectral Data and Deep Learning10.1016/j.eswa.2022.118240 ↗
85. Detection of Heavy Metal Lead in Lettuce Leaves Based on Fluorescence Hyperspectral Technology Combined with Deep Learning Algorithm10.1016/j.saa.2021.120460 ↗
86. Combining Multicolor Fluorescence Imaging with Multispectral Reflectance Imaging for Rapid Citrus Huanglongbing Detection Based on Lightweight Convolutional Neural Network Using a Handheld Device10.1016/j.compag.2022.106808 ↗
87. Chlorophyll Detector Development Based on Snapshot-Mosaic Multispectral Image Sensing and Field Wheat Canopy Processing10.1016/j.compag.2022.106999 ↗
88. Alfalfa (Medicago sativa L.) Crop Vigor and Yield Characterization Using High-Resolution Aerial Multispectral and Thermal Infrared Imaging Technique10.1016/j.compag.2021.105999 ↗
89. Diagnosis of Nitrogen Status in Winter Oilseed Rape (Brassica napus L.) Using in-Situ Hyperspectral Data and Unmanned Aerial Vehicle (UAV) Multispectral Images10.1016/j.compag.2018.05.026 ↗
90. The Coefficient of Determination R-Squared Is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation10.7717/peerj-cs.623 ↗
91. Sensitivity of Spectral Vegetation Indices for Monitoring Water Stress in Tomato Plants10.1016/j.compag.2019.104860 ↗
92. Vapor Pressure Deficit Constrains Transpiration and Photosynthesis in Holm Oak: A Comparison of Three Methods during Summer Drought10.1016/j.agrformet.2022.109218 ↗
93. Discrimination of Water Stress in Pepper Using Thermography and Leaf Turgor Pressure Probe Techniques10.1016/j.agwat.2021.106942 ↗
94. Physiological and Biochemical Changes during Drought and Recovery Periods at Tillering and Jointing Stages in Wheat (Triticum aestivum L.)10.1038/s41598-018-21441-7 ↗
95. Hyperspectral Image Analysis for Water Stress Detection of Apple Trees10.1016/j.compag.2011.04.008 ↗
96. Early Drought Stress Detection in Cereals: Simplex Volume Maximisation for Hyperspectral Image Analysis10.1071/FP12060 ↗
97. Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging10.5307/JBE.2015.40.4.335 ↗
98. Assessing Canopy PRI from Airborne Imagery to Map Water Stress in Maize10.1016/j.isprsjprs.2013.10.002 ↗
99. Zhang, L., Zhang, H., Niu, Y., and Han, W. (2019). Mapping Maizewater Stress Based on UAV Multispectral Remote Sensing. Remote Sens., 11.10.3390/rs11060605 ↗
100. Non-Destructive Analysis of Plant Physiological Traits Using Hyperspectral Imaging: A Case Study on Drought Stress10.1016/j.compag.2022.106806 ↗