Skip to main content
SpringerLink
Log in

Hyperspectral imagery applications for precision agriculture - a systemic survey

Multimedia Tools and Applications volume 81, pages 3005–3038 (2022)Cite this article

Abstract

Hyperspectral imaging has been extensively investigated as an emerging, promising technique for measuring the quality and protection of horticultural and agricultural products over the past 20 years. This technology evolved from remote sensing and joins the machine vision and point spectroscopy realms to provide superior image segmentation for defect and contamination detection. In this paper, we have incorporated spatial and spectral information into hyperspectral imaging techniques. It can efficiently and non-destructively provide helpful information on both external physical and internal chemical characteristics of agricultural and food products. This paper has reviewed the sum of crucial aspect applications of hyperspectral imaging in precision agriculture. This paper reviews hyperspectral imaging’s current and past development in the agriculture industry, such as classification, chromatic, climatic, convergence, etc. This analysis aims to refer to potential work on key issues and promote deployed end-user solutions to fulfill the existing global sustainability objectives.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Abdel-Rahman EM, Mutanga O, Odindi J et al (2017) Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms. Comput Electron Agric 132:21–33. https://doi.org/10.1016/j.compag.2016.11.008

    Article  Google Scholar 

  2. Abdulridha J, Ampatzidis Y, Kakarla SC, Roberts P (2020) Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precis Agric 21:955–978. https://doi.org/10.1007/s11119-019-09703-4

    Article  Google Scholar 

  3. Al-Gaadi KA, Hassaballa AA, Tola E et al (2016) Prediction of potato crop yield using precision agriculture techniques. PLoS ONE 11:1–16. https://doi.org/10.1371/journal.pone.0162219

    Article  Google Scholar 

  4. Al Makdessi N, Ecarnot M, Roumet P, Rabatel G (2019) A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat. Precis Agric 20:237–259. https://doi.org/10.1007/s11119-018-9613-2

    Article  Google Scholar 

  5. Arias F, Zambrano M, Broce K et al (2020) Hyperspectral imaging for rice cultivation: Applications, methods and challenges. AIMS Agric Food 6:273–307. https://doi.org/10.3934/AGRFOOD.2021018

    Article  Google Scholar 

  6. Banerjee S, Shanmugam P (2021) Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters. Adv Space Res 67:266–289. https://doi.org/10.1016/j.asr.2020.09.045

    Article  Google Scholar 

  7. Bangelesa F, Adam E, Knight J et al (2020) Predicting soil organic carbon content using hyperspectral remote sensing in a degraded mountain landscape in Lesotho. Appl Environ Soil Sci. https://doi.org/10.1155/2020/2158573

  8. Barbin D, Elmasry G, Sun DW, Allen P (2012) Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci 90:259–268. https://doi.org/10.1016/j.meatsci.2011.07.011

    Article  Google Scholar 

  9. Barbin DF, Elmasry G, Sun DW, Allen P (2012) Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal Chim Acta 719:30–42. https://doi.org/10.1016/j.aca.2012.01.004

    Article  Google Scholar 

  10. Barbin DF, Elmasry G, Sun DW, Allen P (2013) Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem 138:1162–1171. https://doi.org/10.1016/j.foodchem.2012.11.120

    Article  Google Scholar 

  11. Bianchini V, de Mascarin JM, Silva GM et al (2021) Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality. Plant Methods 17. https://doi.org/10.1186/s13007-021-00709-6

  12. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29:59–107. https://doi.org/10.1080/07352681003617285

    Article  Google Scholar 

  13. Caporaso N, Whitworth MB, Fisk ID (2018) Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl Spectrosc Rev 53:667–687

    Article  Google Scholar 

  14. Chang CI (2003) Hyperspectral imaging: techniques for spectral detection and classification (Vol 1). Springer Science & Business Media

  15. Chen S, Zhang F, Ning J et al (2015) Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem 172:788–793. https://doi.org/10.1016/j.foodchem.2014.09.119

    Article  Google Scholar 

  16. Chen Y, Wang J, Liu G et al (2019) Hyperspectral estimation model of forest soil organic matter in Northwest Yunnan Province, China. Forests 10:217. https://doi.org/10.3390/f10030217

    Article  Google Scholar 

  17. Chen YR, Chao K, Kim MS (2002) Machine vision technology for agricultural applications. Comput Electron Agric 36:173–191. https://doi.org/10.1016/S0168-1699(02)00100-X

    Article  Google Scholar 

  18. Chen Z, Wang J, Wang T et al (2021) Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform. Comput Electron Agric 183:105996. https://doi.org/10.1016/j.compag.2021.105996

    Article  Google Scholar 

  19. Costa C, Antonucci F, Pallottino F et al (2011) Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol 4:673–692. https://doi.org/10.1007/s11947-011-0556-0

    Article  Google Scholar 

  20. Cubero S, Aleixos N, Moltó E et al (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4:487–504. https://doi.org/10.1007/s11947-010-0411-8

    Article  Google Scholar 

  21. da Conceição RRP, Simeone MLF, Queiroz VAV et al (2021) Application of near-infrared hyperspectral (NIR) images combined with multivariate image analysis in the differentiation of two mycotoxicogenic Fusarium species associated with maize. Food Chem 344:128615. https://doi.org/10.1016/j.foodchem.2020.128615

    Article  Google Scholar 

  22. Dixit Y, Hitchman S, Hicks TM et al (2020) Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant. Meat Sci. https://doi.org/10.1016/j.meatsci.2020.108410

    Article  Google Scholar 

  23. Du L, Shi S, Yang J et al (2016) Using different regression methods to estimate leaf nitrogen content in rice by fusing hyperspectral LiDAR data and laser-induced chlorophyll fluorescence data. Remote Sens 8:526. https://doi.org/10.3390/rs8060526

    Article  Google Scholar 

  24. Du Z, Zeng X, Li X et al (2020) Recent advances in imaging techniques for bruise detection in fruits and vegetables. Trends Food Sci Technol 99:133–141. https://doi.org/10.1016/j.tifs.2020.02.024

    Article  Google Scholar 

  25. ElMasry G, Sun DW, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J Food Eng 117:235–246. https://doi.org/10.1016/j.jfoodeng.2013.02.016

    Article  Google Scholar 

  26. Eshkabilov S, Lee A, Sun X et al (2021) Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars. Comput Electron Agric 181:105968. https://doi.org/10.1016/j.compag.2020.105968

    Article  Google Scholar 

  27. Fact MR (2019) Hyperspectral imaging market forecast, trend analysis & competition tracking - global market insights 2019 to 2029. https://www.factmr.com/report/4570/hyperspectral-imaging-market. Accessed 1 May 2021

  28. Falkovskaya A, Gowen A (2020) Literature review: spectral imaging applied to poultry products. Poult Sci 99:3709–3722

    Article  Google Scholar 

  29. Fei B (2020) Hyperspectral imaging in medical applications. Data Handling in Science and Technology. Elsevier Ltd,  Amsterdam, pp 523–565

  30. Femenias A, Bainotti MB, Gatius F et al (2021) Standardization of near infrared hyperspectral imaging for wheat single kernel sorting according to deoxynivalenol level. Food Res Int 139. https://doi.org/10.1016/j.foodres.2020.109925

  31. Feng L, Zhu S, Liu F, He Y, Bao Y, Zhang C (2019) Hyperspectral imaging for seed quality and safety inspection: a review. Plant Methods 15(1):1–25

  32. Forchetti DAP, Poppi RJ (2017) Use of NIR hyperspectral imaging and multivariate curve resolution (MCR) for detection and quantification of adulterants in milk powder. LWT - Food Sci Technol 76:337–343. https://doi.org/10.1016/j.lwt.2016.06.046

    Article  Google Scholar 

  33. Foster AJ, Kakani VG, Mosali J (2017) Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precis Agric 18:192–209. https://doi.org/10.1007/s11119-016-9455-8

    Article  Google Scholar 

  34. Fowler SM, Ponnampalam EN, Schmidt H et al (2015) Prediction of intramuscular fat content and major fatty acid groups of lamb M. longissimus lumborum using Raman spectroscopy. Meat Sci 110:70–75. https://doi.org/10.1016/j.meatsci.2015.06.016

    Article  Google Scholar 

  35. Fox G, Manley M (2014) Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals. J Sci Food Agric 94:174–179. https://doi.org/10.1002/jsfa.6367

    Article  Google Scholar 

  36. Gao Z, Khot LR, Naidu RA, Zhang Q (2020) Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging. Comput Electron Agric 179:105807. https://doi.org/10.1016/j.compag.2020.105807

    Article  Google Scholar 

  37. Gao D, Li M, Zhang J et al (2021) Improvement of chlorophyll content estimation on maize leaf by vein removal in hyperspectral image. Comput Electron Agric 184:106077. https://doi.org/10.1016/j.compag.2021.106077

    Article  Google Scholar 

  38. Geipel J, Bakken AK, Jørgensen M, Korsaeth A (2021) Forage yield and quality estimation by means of UAV and hyperspectral imaging. Precis Agric 1–27. https://doi.org/10.1007/s11119-021-09790-2

  39. Gevaert CM, Suomalainen J, Tang J, Kooistra L (2015) Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. J Sel Top Appl Earth Obs Remote Sens 8:3140–3146. https://doi.org/10.1109/JSTARS.2015.2406339

    Article  Google Scholar 

  40. Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing. Science 228:1147–1153. https://doi.org/10.1126/science.228.4704.1147

    Article  Google Scholar 

  41. Gold KM, Townsend PA, Chlus A et al (2020) Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens 12:286. https://doi.org/10.3390/rs12020286

    Article  Google Scholar 

  42. Gorretta N, Nouri M, Herrero A et al (2019) Early detection of the fungal disease “apple scab” using SWIR hyperspectral imaging. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. IEEE Computer Society

  43. Gowen AA, O’Donnell CP, Taghizadeh M et al (2008) Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus). J Chemom 22:259–267. https://doi.org/10.1002/cem.1127

    Article  Google Scholar 

  44. Grafton M, Kaul T, Palmer A et al (2019) Technical note: regression analysis of proximal hyperspectral data to predict soil pH and Olsen P. Agriculture 9:55. https://doi.org/10.3390/agriculture9030055

    Article  Google Scholar 

  45. Green RO, Eastwood ML, Sarture CM et al (1998) Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ 65:227–248. https://doi.org/10.1016/S0034-4257(98)00064-9

    Article  Google Scholar 

  46. Guo L, Zhang H, Shi T et al (2019) Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma 337:32–41. https://doi.org/10.1016/j.geoderma.2018.09.003

    Article  Google Scholar 

  47. Guo L, Sun X, Fu P et al (2021) Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas. Geoderma 398. https://doi.org/10.1016/j.geoderma.2021.115118

  48. Guo J, Zhang J, Xiong S et al (2021) Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling. Precis Agric 1–25 https://doi.org/10.1007/s11119-021-09804-z. 

  49. Gutiérrez S, Wendel A, Underwood J (2019) Ground based hyperspectral imaging for extensive mango yield estimation. Comput Electron Agric 157:126–135. https://doi.org/10.1016/j.compag.2018.12.041

    Article  Google Scholar 

  50. He X, Yan C, Jiang X et al (2021) Classification of aflatoxin B1 naturally contaminated peanut using visible and near-infrared hyperspectral imaging by integrating spectral and texture features. Infrared Phys Technol 114:103652. https://doi.org/10.1016/j.infrared.2021.103652

    Article  Google Scholar 

  51. Hong Y, Guo L, Chen S et al (2020) Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm. Geoderma 365:114228. https://doi.org/10.1016/j.geoderma.2020.114228

    Article  Google Scholar 

  52. Hu J, Peng J, Zhou Y et al (2019) Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens 11:736. https://doi.org/10.3390/rs11070736

    Article  Google Scholar 

  53. Hu N, Li W, Du C et al (2021) Predicting micronutrients of wheat using hyperspectral imaging. Food Chem 343:128473. https://doi.org/10.1016/j.foodchem.2020.128473

    Article  Google Scholar 

  54. Huang B, Yan S, Xiao L et al (2018) Label-free imaging of nanoparticle uptake competition in single cells by hyperspectral stimulated Raman scattering. Small 14:1703246. https://doi.org/10.1002/smll.201703246

    Article  Google Scholar 

  55. Huang L, Ding W, Liu W et al (2019) Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines. J Plant Pathol 101:1035–1045. https://doi.org/10.1007/s42161-019-00334-2

    Article  Google Scholar 

  56. Ishida T, Kurihara J, Viray FA et al (2018) A novel approach for vegetation classification using UAV-based hyperspectral imaging. Comput Electron Agric 144:80–85. https://doi.org/10.1016/j.compag.2017.11.027

    Article  Google Scholar 

  57. Jawaid S, Talpur FN, Sherazi STH et al (2013) Rapid detection of melamine adulteration in dairy milk by SB-ATR-Fourier transform infrared spectroscopy. Food Chem 141:3066–3071. https://doi.org/10.1016/j.foodchem.2013.05.106

    Article  Google Scholar 

  58. Ji Y, Sun L, Li Y et al (2019) Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine. Infrared Phys Technol 99:71–79. https://doi.org/10.1016/j.infrared.2019.04.007

    Article  Google Scholar 

  59. Jin X, Yang G, Li Z et al (2018) Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data. Precis Agric 19:1–17. https://doi.org/10.1007/s11119-016-9469-2

    Article  Google Scholar 

  60. Jiang H, Ru Y, Chen Q et al (2021) Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork. Spectrochim Acta A Mol Biomol Spectrosc 249. https://doi.org/10.1016/j.saa.2020.119307

  61. Kamruzzaman M, Elmasry G, Sun DW, Allen P (2011) Application of NIR hyperspectral imaging for discrimination of lamb muscles. J Food Eng 104:332–340. https://doi.org/10.1016/j.jfoodeng.2010.12.024

    Article  Google Scholar 

  62. Khan MJ, Khan HS, Yousaf A et al (2018) Modern trends in hyperspectral image analysis: a review. IEEE Access 6:14118–14129

    Article  Google Scholar 

  63. Kim DM, Zhang H, Zhou H et al (2015) Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Sci Rep 5:1–11. https://doi.org/10.1038/srep15919

    Article  Google Scholar 

  64. Kimiya T, Sivertsen AH, Heia K (2013) VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets. J Food Eng 116:758–764. https://doi.org/10.1016/j.jfoodeng.2013.01.008

    Article  Google Scholar 

  65. Koppanati RK, Qamar S, Kumar K, Systems C (2019) ICICCS 2018 1820–1825. https://doi.org/10.1109/ICCONS.2018.8662840

  66. Kumar K (2019) EVS-DK: Event video skimming using deep keyframe. J Vis Commun Image Represent 58:345–352. https://doi.org/10.1016/J.JVCIR.2018.12.009

    Article  Google Scholar 

  67. Kumar K, Shrimankar DD (2018) F-DES: Fast and deep event summarization. IEEE Trans Multimed 20:323–334. https://doi.org/10.1109/TMM.2017.2741423

    Article  Google Scholar 

  68. Kumar K, Shrimankar DD (2018) Deep event learning boosT-up approach: DELTA. Multimed Tools Appl 77:20. https://doi.org/10.1007/S11042-018-5882-Z

    Article  Google Scholar 

  69. Kumar K, Kumar A, Bahuguna A (2017) D-CAD: Deep and crowded anomaly detection. ACM International Conference Proceeding Series 100–105. https://doi.org/10.1145/3154979.3154998

  70. Kumar K, Shrimankar DD, Singh N (2017) Event BAGGING: A novel event summarization approach in multiview surveillance videos. Proceedings of 2017 International Conference on Innovations in Electronics, Signal Processing and Communication, IESC 2017 106–111. https://doi.org/10.1109/IESPC.2017.8071874

  71. Kumar K, Shrimankar D, Navjot N (2018) Eratosthenes sieve based key-frame extraction technique for event summarization in videos. Multimed Tools Appl 77:7383–7404. https://doi.org/10.1007/S11042-017-4642-9

    Article  Google Scholar 

  72. Lan W, Jaillais B, Renard CMGC et al (2021) A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices. Postharvest Biol Technol 175:111497. https://doi.org/10.1016/j.postharvbio.2021.111497

    Article  Google Scholar 

  73. Lassalle G, Fabre S, Credoz A et al (2021) Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices. Sci Rep 11:1–13. https://doi.org/10.1038/s41598-020-79439-z

    Article  Google Scholar 

  74. Li Z, Nie C, Wei C et al (2016) Comparison of four chemometric techniques for estimating leaf nitrogen concentrations in winter wheat (Triticum Aestivum) based on hyperspectral features. J Appl Spectrosc 83:240–247. https://doi.org/10.1007/s10812-016-0276-3

    Article  Google Scholar 

  75. Li X, Li R, Wang M et al (2018) Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables. In: Hyperspectral Imaging in Agriculture, Food and Environment. InTech

  76. Li B, Xu X, Zhang L et al (2020) Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS J Photogramm Remote Sens 162:161–172. https://doi.org/10.1016/j.isprsjprs.2020.02.013

    Article  Google Scholar 

  77. Li W, Zhou X, Yu K et al (2021) Spectroscopic estimation of N concentration in wheat organs for assessing N remobilization under different irrigation regimes. Front Plant Sci 12. https://doi.org/10.3389/fpls.2021.657578

  78. Lim J, Kim G, Mo C et al (2016) Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model. Talanta 151:183–191. https://doi.org/10.1016/j.talanta.2016.01.035

    Article  Google Scholar 

  79. Liu S, Liu X, Liu M et al (2017) Extraction of rice phenological differences under heavy metal stress using EVI time-series from HJ-1A/B data. Sensors 17:1243. https://doi.org/10.3390/s17061243

    Article  Google Scholar 

  80. Liu ZY, Qi JG, Wang NN et al (2018) Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network. Precis Agric 19:973–991. https://doi.org/10.1007/s11119-018-9567-4

    Article  Google Scholar 

  81. Liu C, Huang W, Yang G et al (2020) Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds. Infrared Physics and Technology 110:103462. https://doi.org/10.1016/j.infrared.2020.103462

    Article  Google Scholar 

  82. Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13:1–12. https://doi.org/10.1186/s13007-017-0233-z

    Article  Google Scholar 

  83. Lu J, Zhou M, Gao Y, Jiang H (2018) Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precis Agric 19:379–394. https://doi.org/10.1007/s11119-017-9524-7

    Article  Google Scholar 

  84. Lu J, Yang T, Su X et al (2020) Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves. Precis Agric 21:324–348. https://doi.org/10.1007/s11119-019-09670-w

    Article  Google Scholar 

  85. Lu J, Li W, Yu M et al (2021) Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precis Agric 22:51–74. https://doi.org/10.1007/s11119-020-09729-z

    Article  Google Scholar 

  86. Ma J, Sun D-W, Pu H et al (2019) Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications. Annu Rev Food Sci Technol 10:197–220. https://doi.org/10.1146/annurev-food-032818-121155

    Article  Google Scholar 

  87. Ma D, Maki H, Neeno S et al (2020) Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images. Biosyst Eng 200:40–54. https://doi.org/10.1016/j.biosystemseng.2020.09.002

    Article  Google Scholar 

  88. Ma C, Ren Z, Zhang Z et al (2021) Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology. Vib Spectrosc 114:103230. https://doi.org/10.1016/j.vibspec.2021.103230

    Article  Google Scholar 

  89. Mahajan GR, Pandey RN, Sahoo RN et al (2017) Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precis Agric 18:736–761. https://doi.org/10.1007/s11119-016-9485-2

    Article  Google Scholar 

  90. Mahajan GR, Pandey RN, Sahoo RN et al (2017) Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precision Agric 18:736–761. https://doi.org/10.1007/s11119-016-9485-2

    Article  Google Scholar 

  91. Mahlein A-K, Alisaac E, Al Masri A et al (2019) Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring fusarium head blight of wheat on spikelet scale. Sensors 19:2281. https://doi.org/10.3390/s19102281

    Article  Google Scholar 

  92. Manley M, Williams P, Nilsson D, Geladi P (2009) Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) Kernels. J Agric Food Chem 57:8761–8769. https://doi.org/10.1021/jf9018323

    Article  Google Scholar 

  93. Manolakis DG, Lockwood RB, Cooley TW (2016) Hyperspectral imaging remote sensing: physics, sensors, and algorithms. Cambridge University Press

  94. Menesatti P, Costa C, Aguzzi J (2010) Quality evaluation of fish by hyperspectral imaging. hyperspectral imaging for food quality analysis and control. Elsevier Inc., Amsterdam, pp 273–294

  95. Moghadam P, Ward D, Goan E et al (2017) Plant disease detection using hyperspectral imaging. In: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications. Institute of Electrical and Electronics Engineers Inc., pp 1–8

  96. Mozgeris G, Juodkienė V, Jonikavičius D et al (2018) Ultra-light aircraft-based hyperspectral and colour-infrared imaging to identify deciduous tree species in an urban environment. Remote Sens 10:1668. https://doi.org/10.3390/rs10101668

    Article  Google Scholar 

  97. Murphy RJ, Whelan B, Chlingaryan A, Sukkarieh S (2019) Quantifying leaf-scale variations in water absorption in lettuce from hyperspectral imagery: a laboratory study with implications for measuring leaf water content in the context of precision agriculture. Precis Agric 20:767–787. https://doi.org/10.1007/s11119-018-9610-5

    Article  Google Scholar 

  98. Naganathan GK, Grimes LM, Subbiah J et al (2008) Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Comput Electron Agric 64:225–233. https://doi.org/10.1016/j.compag.2008.05.020

    Article  Google Scholar 

  99. Nagasubramanian K, Jones S, Sarkar S et al (2018) Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems. Plant Methods 14:86. https://doi.org/10.1186/s13007-018-0349-9

    Article  Google Scholar 

  100. Nagasubramanian K, Jones S, Singh AK et al (2019) Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15:1–10. https://doi.org/10.1186/s13007-019-0479-8

    Article  Google Scholar 

  101. Naik BB, Naveen HR, Sreenivas G et al (2020) Identification of water and nitrogen stress indicative spectral bands using hyperspectral remote sensing in maize during post-monsoon season. J Indian Soc Remote Sens 48:1787–1795. https://doi.org/10.1007/s12524-020-01200-w

    Article  Google Scholar 

  102. Nakariyakul S, Casasent DP (2009) Fast feature selection algorithm for poultry skin tumor detection in hyperspectral data. J Food Eng 94:358–365. https://doi.org/10.1016/j.jfoodeng.2009.04.001

    Article  Google Scholar 

  103. Nandibewoor A, Hegadi R (2019) A novel SMLR-PSO model to estimate the chlorophyll content in the crops using hyperspectral satellite images. Cluster Comput 22:443–450. https://doi.org/10.1007/s10586-018-2243-7

    Article  Google Scholar 

  104. Nguyen Do Trong N, Tsuta M, Nicolaï BM et al (2011) Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging. J Food Eng 105:617–624. https://doi.org/10.1016/j.jfoodeng.2011.03.031

    Article  Google Scholar 

  105. Nguyen HDD, Pan V, Pham C et al (2020) Night-based hyperspectral imaging to study association of horticultural crop leaf reflectance and nutrient status. Comput Electron Agric 173:105458. https://doi.org/10.1016/j.compag.2020.105458

    Article  Google Scholar 

  106. Nogales-Bueno J, Baca-Bocanegra B, Rodríguez-Pulido FJ et al (2015) Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins. Food Chem 172:559–564. https://doi.org/10.1016/j.foodchem.2014.09.112

    Article  Google Scholar 

  107. Nogales-Bueno J, Rodríguez-Pulido FJ, Heredia FJ, Hernández-Hierro JM (2015) Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from la Rioja (Spain). Talanta 131:412–416. https://doi.org/10.1016/j.talanta.2014.07.086

    Article  Google Scholar 

  108. Nyalala I, Okinda C, Kunjie C, Korohou T, Nyalala L, Chao Q (2021) Weight and volume estimation of poultry and products based on computer vision systems: a review. Poult Sci 101072

  109. Onoyama H, Ryu C, Suguri M, Iida M (2018) Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis. Precis Agric 19:721–734. https://doi.org/10.1007/s11119-017-9552-3

    Article  Google Scholar 

  110. Paliwal J, Thakur S, Erkinbaev C (2018) Protein-starch interactions in cereal grains and pulses. Encyclopedia of Food Chemistry. Elsevier, Amsterdam, pp 446–452

  111. Pallottino F, Stazi SR, D’Annibale A et al (2018) Rapid assessment of As and other elements in naturally-contaminated calcareous soil through hyperspectral VIS-NIR analysis. Talanta 190:167–173. https://doi.org/10.1016/j.talanta.2018.07.082

    Article  Google Scholar 

  112. Pan L, Lu R, Zhu Q et al (2016) Predict compositions and mechanical properties of sugar beet using hyperspectral scattering. Food Bioprocess Technol 9:1177–1186. https://doi.org/10.1007/s11947-016-1710-5

    Article  Google Scholar 

  113. Pandey P, Ge Y, Stoerger V, Schnable JC (2017) High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Front Plant Sci 8:1348. https://doi.org/10.3389/fpls.2017.01348

    Article  Google Scholar 

  114. Pang L, Men S, Yan L, Xiao J (2020) Rapid vitality estimation and prediction of corn seeds based on spectra and images using deep learning and hyperspectral imaging techniques. IEEE Access 8:123026–123036. https://doi.org/10.1109/ACCESS.2020.3006495

    Article  Google Scholar 

  115. Parfitt J, Barthel M, MacNaughton S (2010) Food waste within food supply chains: Quantification and potential for change to 2050. Philos Trans R Soc B Biol Sci 365:3065–3081. https://doi.org/10.1098/rstb.2010.0126

    Article  Google Scholar 

  116. Park B, Windham WR, Lawrence KC, Smith DP (2007) Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosyst Eng 96:323–333. https://doi.org/10.1016/j.biosystemseng.2006.11.012

    Article  Google Scholar 

  117. Patel AK, Ghosh JK (2019) Soil fertility status assessment using hyperspectral remote sensing. Remote Sens Agric Ecosyst Hydrol XXI 11149:14. https://doi.org/10.1117/12.2533115

    Article  Google Scholar 

  118. Patel AK, Ghosh JK, Pande S, Sayyad SU (2020) Deep-learning-based approach for estimation of fractional abundance of nitrogen in soil from hyperspectral Data. IEEE J Sel Top Appl Earth Obs Remote Sens 13:6495–6511. https://doi.org/10.1109/JSTARS.2020.3039844

    Article  Google Scholar 

  119. Pearlman JS, Barry PS, Segal CC et al (2003) Hyperion, a space-based imaging spectrometer. IEEE Trans Geosci Remote Sens 41:1160–1173. https://doi.org/10.1109/TGRS.2003.815018

    Article  Google Scholar 

  120. Peng Y, Zhu X, Xiong J et al (2021) Estimation of nitrogen content on apple tree canopy through red-edge parameters from fractional-order differential operators using hyperspectral reflectance. J Indian Soc Remote Sens 49:377–392. https://doi.org/10.1007/s12524-020-01197-2

    Article  Google Scholar 

  121. Persistence Market Research (2016) Imaging technology for precision agriculture market. Available: https://www.persistencemarketresearch.com/market-research/imaging-technology-for-precision-agriculture-market.asp. Accessed 28 April 2021

  122. Pu H, Sun DW, Ma J et al (2014) Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging. J Food Eng 143:44–52. https://doi.org/10.1016/j.jfoodeng.2014.06.025

    Article  Google Scholar 

  123. Qiu G, Lü E, Lu H et al (2018) Single-kernel FT-NIR spectroscopy for detecting supersweet corn (Zea mays L. saccharata sturt) seed viability with multivariate data analysis. Sensors (Switzerland) 18. https://doi.org/10.3390/s18041010

  124. Rabanera JD, Guzman JD, Yaptenco KF (2021) Rapid and Non-destructive measurement of moisture content of peanut (Arachis hypogaea L.) kernel using a near-infrared hyperspectral imaging technique. J Food Meas Charact. https://doi.org/10.1007/s11694-021-00894-x

    Article  Google Scholar 

  125. Rady A, Ekramirad N, Adedeji AA et al (2017) Hyperspectral imaging for detection of codling moth infestation in GoldRush apples. Postharvest Biol Technol 129:37–44. https://doi.org/10.1016/j.postharvbio.2017.03.007

    Article  Google Scholar 

  126. Rahman A, Lee H, Kim MS, Cho BK (2018) Mapping the pungency of green pepper using hyperspectral imaging. Food Anal Methods 11:3042–3052. https://doi.org/10.1007/s12161-018-1275-1

    Article  Google Scholar 

  127. Priya TR, Manickavasagan A (2021) Characterising corn grain using infrared imaging and spectroscopic techniques: a review. J Food Meas Charact 1–16

  128. Ravikanth L, Chelladurai V, Jayas DS, White NDG (2016) Detection of broken kernels content in bulk wheat samples using near-infrared hyperspectral imaging. Agric Res 5:285–292. https://doi.org/10.1007/s40003-016-0227-5

    Article  Google Scholar 

  129. Reis AS, Rodrigues M, Alemparte Abrantes dos Santos GL et al (2021) Detection of soil organic matter using hyperspectral imaging sensor combined with multivariate regression modeling procedures. Remote Sens Appl Soc Environ 22:100492. https://doi.org/10.1016/j.rsase.2021.100492

    Article  Google Scholar 

  130. Riccioli C, Pérez-Marín D, Garrido-Varo A (2021) Optimizing spatial data reduction in hyperspectral imaging for the prediction of quality parameters in intact oranges. Postharvest Biol Technol 176:111504. https://doi.org/10.1016/j.postharvbio.2021.111504

    Article  Google Scholar 

  131. Rubio-Delgado J, Pérez CJ, Vega-Rodríguez MA (2021) Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture. Precision Agric 22:1–21. https://doi.org/10.1007/s11119-020-09727-1

    Article  Google Scholar 

  132. Schmid T, Rodriguez-Rastrero M, Escribano P et al (2016) Characterization of Soil Erosion Indicators Using Hyperspectral Data from a Mediterranean Rainfed Cultivated Region. IEEE J Sel Top Appl Earth Obs Remote Sens 9:845–860. https://doi.org/10.1109/JSTARS.2015.2462125. 

  133. Sendin K, Williams PJ, Manley M (2018) Near infrared hyperspectral imaging in quality and safety evaluation of cereals. Crit Rev Food Sci Nutr 58:575–590. https://doi.org/10.1080/10408398.2016.1205548

    Article  Google Scholar 

  134. Sendin K, Manley M, Baeten V et al (2019) Near infrared hyperspectral imaging for white maize classification according to grading regulations. Food Anal Methods 12:1612–1624. https://doi.org/10.1007/s12161-019-01464-0

    Article  Google Scholar 

  135. Serranti S, Trella A, Bonifazi G, Izquierdo CG (2018) Production of an innovative biowaste-derived fertilizer: Rapid monitoring of physical-chemical parameters by hyperspectral imaging. Waste Manag 75:141–148. https://doi.org/10.1016/j.wasman.2018.02.013

    Article  Google Scholar 

  136. Shen Q, Xia K, Zhang S et al (2019) Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area. Spectrochim Acta A Mol Biomol Spectrosc 222:117191. https://doi.org/10.1016/j.saa.2019.117191

    Article  Google Scholar 

  137. Shi T, Liu H, Chen Y et al (2016) Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. J Hazard Mater 308:243–252. https://doi.org/10.1016/j.jhazmat.2016.01.022

    Article  Google Scholar 

  138. Silva LCR, Folli GS, Santos LP et al (2020) Quantification of beef, pork, and chicken in ground meat using a portable NIR spectrometer. Vib Spectrosc 111. https://doi.org/10.1016/j.vibspec.2020.103158

  139. Singh H, Ansari H, Dhanak N, Kumar K (2017) HDML: Habit Detection with Machine Learning. ACM International Conference Proceeding Series 29–33. https://doi.org/10.1145/3154979.3154996

  140. Song X, Xu D, He L et al (2016) Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat. Precision Agric 17:721–736. https://doi.org/10.1007/s11119-016-9445-x

    Article  Google Scholar 

  141. Song Y-Q, Zhao X, Su H-Y et al (2018) Predicting spatial variations in soil nutrients with hyperspectral remote sensing at regional scale. Sensors 18:3086. https://doi.org/10.3390/s18093086

    Article  Google Scholar 

  142. Stuart McGonigle (2019) Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems. Sensors 19:3071. https://doi.org/10.3390/s19143071

    Article  Google Scholar 

  143. Suarez LA, Robson A, McPhee J et al (2020) Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data. Precis Agric 21:1304–1326. https://doi.org/10.1007/s11119-020-09722-6

    Article  Google Scholar 

  144. Sun Y, Liu Y, Yu H et al (2017) Non-destructive prediction of moisture content and freezable water content of purple-fleshed sweet potato slices during drying process using hyperspectral imaging technique. Food Anal Methods 10:1535–1546. https://doi.org/10.1007/s12161-016-0722-0

    Article  Google Scholar 

  145. Sun D, Cen H, Weng H et al (2019) Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality. Plant Methods 15:1–16. https://doi.org/10.1186/s13007-019-0432-x

    Article  Google Scholar 

  146. Susič N, Žibrat U, Širca S et al (2018) Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging. Sens Actuators B Chem 273:842–852. https://doi.org/10.1016/j.snb.2018.06.121

    Article  Google Scholar 

  147. Tahmasbian I, Morgan NK, Bai SH et al (2021) Comparison of hyperspectral imaging and near-infrared spectroscopy to determine nitrogen and carbon concentrations in wheat. Remote Sens 13. https://doi.org/10.3390/rs13061128

  148. Tan K, Wang X, Zhu J et al (2018) A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression. Int J Remote Sens 39:3029–3054. https://doi.org/10.1080/01431161.2018.1433893

    Article  Google Scholar 

  149. Tan K, Wang H, Chen L et al (2020) Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J Hazard Mater 382:120987. https://doi.org/10.1016/j.jhazmat.2019.120987

    Article  Google Scholar 

  150. Tian YC, Yao X, Yang J et al (2011) Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Res 120:299–310. https://doi.org/10.1016/j.fcr.2010.11.002

    Article  Google Scholar 

  151. Tian S, Wang S, Bai X et al (2020) Hyperspectral estimation model of soil Pb content and its applicability in different soil types. Acta Geochim 39:423–433. https://doi.org/10.1007/s11631-019-00388-0

    Article  Google Scholar 

  152. Tong X, Duan L, Liu T, Singh VP (2019) Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision. Precis Agric 20:477–495. https://doi.org/10.1007/s11119-018-9592-3

    Article  Google Scholar 

  153. Vanegas F, Bratanov D, Powell K et al (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18:260. https://doi.org/10.3390/s18010260

    Article  Google Scholar 

  154. Wang Y, Hu X, Hou Z et al (2018) Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging. J Sci Food Agric 98:4659–4664. https://doi.org/10.1002/jsfa.8996

    Article  Google Scholar 

  155. Wang L, Jin J, Song Z et al (2020) LeafSpec: An accurate and portable hyperspectral corn leaf imager. Comput Electron Agric 169:105209. https://doi.org/10.1016/j.compag.2019.105209

    Article  Google Scholar 

  156. Wang G, Wang Q, Su Z, Zhang J (2020) Predicting copper contamination in wheat canopy during the full growth period using hyperspectral data. Environ Sci Pollut Res 27:39029–39040. https://doi.org/10.1007/s11356-020-09973-w

    Article  Google Scholar 

  157. Wang J, Zhang C, Shi Y et al (2020) Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging. Plant Methods 16. https://doi.org/10.1186/s13007-020-00576-7

  158. Wang Z, Zhang Y, Fan S et al (2020) Determination of moisture content of single maize seed by using long-wave near-infrared hyperspectral imaging (LWNIR) Coupled with UVE-SPA combination variable selection method. IEEE Access 8:195229–195239. https://doi.org/10.1109/ACCESS.2020.3033582

    Article  Google Scholar 

  159. Wang Z, Fan S, Wu J et al (2021) Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. Spectrochim Acta A Mol Biomol Spectrosc 254:119666. https://doi.org/10.1016/j.saa.2021.119666

    Article  Google Scholar 

  160. Wang Z, Tian X, Fan S et al (2021) Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models. Infrared Phys Technol 112:103596. https://doi.org/10.1016/j.infrared.2020.103596

    Article  Google Scholar 

  161. Wakholi C, Kandpal LM, Lee H et al (2018) Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics. Sens Actuators B Chem 255:498–507. https://doi.org/10.1016/j.snb.2017.08.036

    Article  Google Scholar 

  162. Wei L, Wang Z, Huang C et al (2020) Transparency estimation of narrow rivers by UAV-borne hyperspectral remote sensing imagery. IEEE Access 8:168137–168153. https://doi.org/10.1109/ACCESS.2020.3023690

    Article  Google Scholar 

  163. Wen P, Shi Z, Li A et al (2020) Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters. Precision Agric 22:984–1005. https://doi.org/10.1007/s11119-020-09769-5

    Article  Google Scholar 

  164. Wu D, Sun DW (2013) Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta 116:266–276. https://doi.org/10.1016/j.talanta.2013.05.030

    Article  Google Scholar 

  165. Xie A, Sun DW, Xu Z, Zhu Z (2015) Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Talanta 139:208–215. https://doi.org/10.1016/j.talanta.2015.02.027

    Article  Google Scholar 

  166. Xing F, Yao H, Liu Y et al (2019) Recent developments and applications of hyperspectral imaging for rapid detection of mycotoxins and mycotoxigenic fungi in food products. Crit Rev Food Sci Nutr 59:173–180

    Article  Google Scholar 

  167. Yan Y, Yu W (2019) Early detection of rice blast (Pyricularia) at seedling stage based on near-infrared hyper-spectral image. In: ACM International Conference Proceeding Series. Association for Computing Machinery, New York, NY, USA, pp 64–68

  168. Yang S, Zhu QB, Huang M, Qin JW (2017) Hyperspectral image-based variety discrimination of maize seeds by using a multi-model strategy coupled with unsupervised joint skewness-based wavelength selection algorithm. Food Anal Methods 10:424–433. https://doi.org/10.1007/s12161-016-0597-0

    Article  Google Scholar 

  169. Yang W, Yang C, Hao Z et al (2019) Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network. IEEE Access 7:118239–118248. https://doi.org/10.1109/ACCESS.2019.2936892

    Article  Google Scholar 

  170. Yang W, Nigon T, Hao Z et al (2021) Estimation of corn yield based on hyperspectral imagery and convolutional neural network. Comput Electron Agric 184:106092. https://doi.org/10.1016/j.compag.2021.106092

    Article  Google Scholar 

  171. Ye X, Abe S, Zhang S (2020) Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precision Agric 21:198–225. https://doi.org/10.1007/s11119-019-09661-x

    Article  Google Scholar 

  172. Zaeem M, Nadeem M, Huong Pham T et al (2021) Development of a hyperspectral imaging technique using LA-ICP-MS to show the spatial distribution of elements in soil cores. Geoderma 385:114831. https://doi.org/10.1016/j.geoderma.2020.114831

    Article  Google Scholar 

  173. Zeng F, Lü E, Qiu G et al (2019) Single-kernel ft-NIR spectroscopy for detecting maturity of cucumber seeds using a multiclass hierarchical classification strategy. Appl Sci (Switzerland) 9. https://doi.org/10.3390/app9235058

  174. Zhang C, Zhao Y, Yan T et al (2020) Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning. Infrared Phys Technol 111:103550. https://doi.org/10.1016/j.infrared.2020.103550

    Article  Google Scholar 

  175. Zhang H, Zhang S, Chen Y et al (2020) Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features. J Food Compos Anal 92. https://doi.org/10.1016/j.jfca.2020.103567

  176. Zhang B, Huang W, Li J et al (2014) Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res Int 62:326–343

    Article  Google Scholar 

  177. Zhang C, Jiang H, Liu F, He Y (2017) Application of near-infrared hyperspectral imaging with variable selection methods to determine and visualize caffeine content of coffee beans. Food Bioprocess Technol 10:213–221. https://doi.org/10.1007/s11947-016-1809-8

    Article  Google Scholar 

  178. Zhang S, Shen Q, Nie C et al (2019) Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim Acta A Mol Biomol Spectrosc 211:393–400. https://doi.org/10.1016/j.saa.2018.12.032

    Article  Google Scholar 

  179. Zhang GS, Xu TY, Tian YW et al (2020) Assessment of rice leaf blast severity using hyperspectral imaging during late vegetative growth. Australas Plant Pathol 49:571–578. https://doi.org/10.1007/s13313-020-00736-2

    Article  Google Scholar 

  180. Zhang J, Dai L, Cheng F (2021) Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network. J Food Meas Charact 15:484–494. https://doi.org/10.1007/s11694-020-00646-3

    Article  Google Scholar 

  181. Zhang J, Dai L, Cheng F (2021) Identification of corn seeds with different freezing damage degree based on hyperspectral reflectance imaging and deep learning method. Food Anal Methods 14:389–400. https://doi.org/10.1007/s12161-020-01871-8

    Article  Google Scholar 

  182. Zheng H, Cheng T, Li D et al (2018) Evaluation of RGB, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sens 10:824. https://doi.org/10.3390/rs10060824

    Article  Google Scholar 

  183. Zovko M, Žibrat U, Knapič M et al (2019) Hyperspectral remote sensing of grapevine drought stress. Precis Agric 20:335–347. https://doi.org/10.1007/s11119-019-09640-2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics, Sambalpur University, Odisha, India

    Prabira Kumar Sethy

  2. Department of ET&T Engineering, Government Engineering College, Jagdalpur, CG, India

    Chanki Pandey & Yogesh Kumar Sahu

  3. Department of Computer Science and Engineering, VSSUT, Burla, Odisha, India

    Santi Kumari Behera

Authors
  1. Prabira Kumar Sethy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chanki Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yogesh Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santi Kumari Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prabira Kumar Sethy.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sethy, P.K., Pandey, C., Sahu, Y.K. et al. Hyperspectral imagery applications for precision agriculture - a systemic survey. Multimed Tools Appl 81, 3005–3038 (2022). https://doi.org/10.1007/s11042-021-11729-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11729-8

Keywords

search

Navigation