Comparative Evaluation of Hyperspectral, Thermal, and Near-Infrared Imaging Systems for Non-Destructive Prediction of Fruit and Vegetable Quality: A Review

1. Abdullah, N., Nawi, N.M., Ding, P., Kassim, M.S.M., Lazim, S.S.R.M.: Preliminary study to predict moisture content of jackfruit skin using near infrared spectroscopy. Food Res. 3(2), 128–132 (2019)10.26656/fr.2017.3(2).135 ↗

2. Ali, M.M., Hashim, N., Shahamshah, M.I.: Durian (Durio zibethinus) ripeness detection using thermal imaging with multivariate analysis. Postharvest Biol. Technol. 176, 111517 (2021). https://doi.org/10.1016/j.postharvbio.2021.11151710.1016/j.postharvbio.2021.111517 ↗

3. Anjali Jena, A., Bamola, A., Mishra, S., Jain, I., Pathak, N., Akhtar, S.: State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions. Food Prod. Process. Nutr. 6(1), 56 (2024). https://doi.org/10.1186/s43014-023-00205-510.1186/s43014-023-00205-5 ↗

4. Arendse, E., Fawole, O.A., Magwaza, L.S., Opara, U.L.: Non-destructive characterization and volume estimation of pomegranate fruit external and internal morphological fractions using X-ray computed tomography. J. Food Eng. 186, 42–49 (2016). https://doi.org/10.1016/j.jfoodeng.2016.04.01110.1016/j.jfoodeng.2016.04.011 ↗

5. Arendse, E., Fawole, O.A., Magwaza, L.S., Nieuwoudt, H.H., Opara, U.L.: Development of calibration models for the evaluation of pomegranate aril quality by Fourier-transform near infrared spectroscopy combined with chemometrics. Biosyst. Eng. 159, 22–32 (2017). https://doi.org/10.1016/j.biosystemseng.2017.04.00410.1016/j.biosystemseng.2017.04.004 ↗

6. Arendse, E., Fawole, O.A., Magwaza, L.S., Opara, U.L.: Non-destructive prediction of internal and external quality attributes of fruit with thick rind: a review. J. Food Eng. 217, 11–23 (2018). https://doi.org/10.1016/j.jfoodeng.2017.08.00910.1016/j.jfoodeng.2017.08.009 ↗

7. Babu, P.S., Sudheer, K.P., Sarathjith, M.C., Mathew, S.M., Gopinath, G.: Quality evaluation of tender jackfruit using near-infrared reflectance spectroscopy. J. Appl. Hortic. 22(3), 171–175 (2020). https://doi.org/10.37855/jah.2020.v22i03.3110.37855/jah.2020.v22i03.31 ↗

8. Bahrami, M.E., Honarvar, M., Ansari, K., Jamshidi, B.: Measurement of quality parameters of sugar beet juices using near-infrared spectroscopy and chemometrics. J. Food Eng. 271, 109775 (2020). https://doi.org/10.1016/j.jfoodeng.2019.10977510.1016/j.jfoodeng.2019.109775 ↗

9. Blanco, M., Villarroya, I.: NIR spectroscopy: a rapid-response analytical tool. TrAC Trends Anal. Chem. 21, 240–250 (2002). https://doi.org/10.1016/S0165-9936(02)00404-110.1016/S0165-9936(02)00404-1 ↗

10. Bobelyn, E., Serban, A., Nicu, M., Lammertyn, J., Nicolaï, B.M., Saeys, W.: Postharvest quality of apple predicted by NIR spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol. Technol. 55, 133–143 (2010). https://doi.org/10.1016/j.postharvbio.2009.09.00610.1016/j.postharvbio.2009.09.006 ↗

11. Booche, F., Kupfer, G., Dockter, K., Kubauch, W.: Shape of the red as vitality indicator for plants. Int. J. Remote Sens. 11, 1741–1753 (1990). https://doi.org/10.1080/0143116900895512710.1080/01431169008955127 ↗

12. Caporaso, N., Whitworth, M.B., Fisk, I.D.: Near-infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl. Spectrosc. Rev. 53(8), 667–687 (2018). https://doi.org/10.1080/05704928.2018.142521410.1080/05704928.2018.1425214 ↗

13. Caporaso, N., Whitworth, M.B., Fisk, I.D.: Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging. Food Chem. 344, 128663 (2021). https://doi.org/10.1016/j.foodchem.2020.12866310.1016/j.foodchem.2020.128663 ↗

14. Chen, P., Nattuvetty, V.R.: Light transmittance through a region of an intact fruit. Trans. ASAE 23(2), 519–522 (1980). https://doi.org/10.13031/2013.3461510.13031/2013.34615 ↗

15. Chowdhury, F.A., Raman, M.A., Mian, A.J.: Distribution of free sugars and fatty acids in jackfruit (Artocarpus heterophyllus). Food Chem. 60(1), 25–28 (1997). https://doi.org/10.1016/S0308-8146(96)00294-410.1016/S0308-8146(96)00294-4 ↗

16. Clark, A.F.: Spectroscopy of rocks and minerals and principles of spectroscopy. In: Rencz, A.N. (ed.) Manual of remote sensing: remote sensing for the earth sciences, pp. 3–58. John Wiley & Sons, New York (2004). https://doi.org/10.3749/9780921294771.ch0210.3749/9780921294771.ch02 ↗

17. Clark, C.J., McGlone, V.A., De Silva, H.N., Manning, M.A.: Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest. Postharvest Biol. Technol. 32, 147–158 (2004). https://doi.org/10.1016/j.postharvbio.2003.11.00410.1016/j.postharvbio.2003.11.004 ↗

18. Conway, J.M., Norris, K.H., Bodwell, C.E.: A new approach for the estimation of body composition: infrared interactance. Am. J. Clin. Nutr. 40, 1123–1130 (1984). https://doi.org/10.1093/ajcn/40.6.112310.1093/ajcn/40.6.1123 ↗

19. de Oliveira, G.A., Bureau, S., Renard, C.M.G.C., Pereira-Netto, A.B., de Castilhos, F.: Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 143, 223–230 (2014). https://doi.org/10.1016/j.foodchem.2013.07.12210.1016/j.foodchem.2013.07.122 ↗

20. ElMasry, G., Kamruzzman, M., Sun, D.W., Allen, P.: Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci. Nutr. 52(11), 999–1023 (2012). https://doi.org/10.1080/10408398.2010.54349510.1080/10408398.2010.543495 ↗

21. Elsherbiny, O., El-Hendawy, S., Elsayed, S., Elwakeel, A.E., Alebidi, A., Yue, X., Galal, H.: Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators. Sci. Rep. 15(1), 12345 (2025). https://doi.org/10.1038/s41598-024-81694-310.1038/s41598-024-81694-3 ↗

22. Fabre, S., Lesaignoux, A., Olioso, A., Briottet, X.: Influence of water content on spectral reflectance of leaves in the 3–15 µm domain. IEEE Geosci. Remote Sens. Lett. 8, 143–147 (2011). https://doi.org/10.1109/LGRS.2010.205351810.1109/LGRS.2010.2053518 ↗

23. Gerber, F., Marion, R., Olioso, A., Jacquemoud, S., da Luz, B., Fabre, S.: Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4μm to 5.7μm with the PROSPECT-VISIR model. Remote Sens. Environ. 115, 404–414 (2011). https://doi.org/10.1016/j.rse.2010.09.01110.1016/j.rse.2010.09.011 ↗

24. Ghooshkhaneh, N.G., Golzarian, M.R., Mollazade, K.: VIS-NIR spectroscopy for detection of citrus core rot caused by Alternaria alternata. Food Control 144, 109320 (2023). https://doi.org/10.1016/j.foodcont.2022.10932010.1016/j.foodcont.2022.109320 ↗

25. Gómez, A.H., He, Y., Pereira, A.G.: Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using VIS-NIR spectroscopy techniques. J. Food Eng. 77, 313–319 (2006). https://doi.org/10.1016/j.jfoodeng.2005.06.03610.1016/j.jfoodeng.2005.06.036 ↗

26. Gowen, A.A., O’Donnell, C., Cullen, P., Downey, G., Frias, J.: Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590–598 (2007). https://doi.org/10.1016/j.tifs.2007.06.00110.1016/j.tifs.2007.06.001 ↗

27. Grabska, J., Beć, K.B., Ueno, N., Huck, C.W.: Analyzing the quality parameters of apples by spectroscopy from VIS/NIR to NIR region: a comprehensive review. Foods 12(10), 1946 (2023). https://doi.org/10.3390/foods1210194610.3390/foods12101946 ↗

28. Guo, Z., Ni, Y., Gao, H., Ding, G., Zeng, Y.: A dual-branch selective attention capsule network for classifying kiwifruit soft rot with hyperspectral images. Sci. Rep. 14(1), 10664 (2024). https://doi.org/10.1038/s41598-024-61425-410.1038/s41598-024-61425-4 ↗

29. He, W., Huang, W., Popovic, T., Zhu, Z., Zhang, X.: Improving mango cold-damage and bruise detection using thermal imaging and flexible spectral sensing. Food Control 172, 111163 (2025). https://doi.org/10.1016/j.foodcont.2025.11116310.1016/j.foodcont.2025.111163 ↗

30. Hespeler, S.C., Nemati, H., Dehghan-Niri, E.: Non-destructive thermal imaging for object detection via advanced deep learning for robotic inspection and harvesting of chili peppers. Artif. Intell. Agric. 5, 102–117 (2021). https://doi.org/10.1016/j.aiia.2021.05.00310.1016/j.aiia.2021.05.003 ↗

31. Hong, Z., Lu, H.: A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.). Comput. Electron. Agric. 83, 47–51 (2012). https://doi.org/10.1016/j.compag.2012.01.01210.1016/j.compag.2012.01.012 ↗

32. Huang, Y., Zheng, Y., Liu, P., Xie, L., Ying, Y.: Enhanced prediction of soluble solids content and vitamin C content in citrus using visible and near-infrared spectroscopy combined with one-dimensional convolutional neural network. J. Food Compos. Anal. 139, 107131 (2025). https://doi.org/10.1016/j.jfca.2024.10713110.1016/j.jfca.2024.107131 ↗

33. Hussain, M.A., Rahman, M.S.: Thermal conductivity prediction of fruits and vegetables using neural networks. Int. J. Food Prop. 2(2), 121–137 (1999). https://doi.org/10.1080/1094291990952459610.1080/10942919909524596 ↗

34. Ibrahim, A., Alghannam, A., Eissa, A., Firtha, F., Kaszab, T., Kovacs, Z., Helyes, L.: Preliminary study for inspecting moisture content, dry matter content and firmness parameters of two date cultivars using an NIR hyperspectral imaging system. Front. Bioeng. Biotechnol. 9, 720630 (2021). https://doi.org/10.3389/fbioe.2021.72063010.3389/fbioe.2021.720630 ↗

35. Inoue, Y.: Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production. Plant Prod. Sci. 6(1), 3–16 (2003). https://doi.org/10.1626/pps.6.310.1626/pps.6.3 ↗

36. Ishimwe, R., Abutaleb, K., Ahmed, F.: Applications of thermal imaging in agriculture: a review. Adv. Remote Sens. 3, 128–140 (2014). https://doi.org/10.4236/ars.2014.3301110.4236/ars.2014.33011 ↗

37. Jiang, L., Xu, B., Husnain, N., Wang, Q.: Overview of agricultural machinery automation technology for sustainable agriculture. Agronomy 15(6), 1471 (2025). https://doi.org/10.3390/agronomy1506147110.3390/agronomy15061471 ↗

38. Kays, S.J.: Metabolic processes in harvested products. In: Postharvest physiology of perishable plant products, pp. 75–142. Springer, US (1991). https://doi.org/10.1017/s001447970001980310.1017/s0014479700019803 ↗

39. Khodabakhshian, R., Emadi, B., Khojastehpour, M., Golzarian, M.R.: Determining quality and maturity of pomegranates using multispectral imaging. J. Saudi Soc. Agric. Sci. 16(4), 322–331 (2017). https://doi.org/10.1016/j.jssas.2015.10.00410.1016/j.jssas.2015.10.004 ↗

40. Kim, M.J., Yu, W.H., Song, D.J., Chun, S.W., Kim, M.S., Lee, A., Mo, C.: Prediction of soluble-solid content in citrus fruit using visible–near-infrared hyperspectral imaging based on effective-wavelength selection algorithm. Sensors 24(5), 1512 (2024). https://doi.org/10.3390/s2405151210.3390/s24051512 ↗

41. Krivoshiev, G.P., Chalucova, R.P., Moukarev, M.I.: A possibility for elimination of the interference from the peel in nondestructive determination of the internal quality of fruit and vegetables by VIS/NIR spectroscopy. LWT Food Sci. Technol. 33, 344–353 (2000). https://doi.org/10.1006/fstl.2000.066910.1006/fstl.2000.0669 ↗

42. Kumar, L., Rietkerk, M., Van Langevelde, F., Van De Koppel, J., Van Andel, J., De Ridder, N., Stroosnijder, L., Skidmore, A.K., Prins, H.H.T.: Relationship between vegetation growth rates at the onset of the wet season and soil type in the Sahel of Burkina Faso: implications for resource utilisation at large scales. Ecol. Model. 149, 143–152 (2002). https://doi.org/10.1016/S0304-3800(01)00520-810.1016/S0304-3800(01)00520-8 ↗

43. Lan, W., Jailais, B., Renard, C.M.G.C., Leca, A., Chen, S., Bourvellec, C.L., Bureau, S.: A method using near-infrared hyperspectral imaging to highlight the internal quality of apple fruit slices. Postharvest Biol. Technol. 175, 111497 (2021). https://doi.org/10.1016/j.postharvbio.2021.11149710.1016/j.postharvbio.2021.111497 ↗

44. Leemans, V., Destain, M.F.: A real-time grading method of apples based on features extracted from defects. J. Food Eng. 61(1), 83–89 (2004). https://doi.org/10.1016/S0260-8774(03)00189-410.1016/S0260-8774(03)00189-4 ↗

45. Li, B., Hou, B., Zhang, D., Zhou, Y., Zhao, M., Hong, R., Huang, Y.: Pear’s characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging. Optik 127(5), 2624–2630 (2016). https://doi.org/10.1016/j.ijleo.2015.11.19310.1016/j.ijleo.2015.11.193 ↗

46. Li, B., Yin, H., Liu, Y.D., Zhang, F., Yang, A.K., Su, C.T., Ou-yang, A.G.: Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method. J. Anal. Sci. Technol. 13(1), 24 (2022). https://doi.org/10.1186/s40543-022-00334-510.1186/s40543-022-00334-5 ↗

47. Li, J., Rao, X., Guo, J., Ying, Y.: Hyperspectral reflectance imaging for detecting citrus canker based on dual-band ratio image classification method. In: Proceedings of SPIE – The International Society for Optical Engineering, 7656, 76562C (2010). https://doi.org/10.1117/12.86706510.1117/12.867065 ↗

48. Li, J., Rao, X., Ying, Y.: Detection of common defects on oranges using hyperspectral reflectance imaging. Comput. Electron. Agric. 78, 38–48 (2011). https://doi.org/10.1016/j.compag.2011.05.01010.1016/j.compag.2011.05.010 ↗

49. Li, J., Tian, Q., Bu, Y., Wang, J., Chi, Q., Guo, W.: Calibrating the prediction model of soluble solids content and firmness in kiwifruit across years based on NIR spectroscopy using model transfer and transfer learning. Spectrochim. Acta A Mol. Biomol. Spectrosc. (2025). https://doi.org/10.1016/j.saa.2025.12612210.1016/j.saa.2025.126122 ↗

50. Liu, M., Zhang, L., Guo, E.: Hyperspectral laser-induced fluorescence imaging for nondestructive assessing soluble solids content of orange. In: Computer and Computing Technologies in Agriculture (CCTA 2007), pp. 51–59. Springer, US (2008). https://doi.org/10.1007/978-0-387-77251-6_710.1007/978-0-387-77251-6_7 ↗

51. Liu, Q., Gu, X., Li, X., Jacob, F., Hanocq, J., Friedl, M.: Study on thermal infrared emission directionality over crop canopies with TIR camera imagery. Sci. China E Technol. Sci. 43, 95–103 (2000). https://doi.org/10.1146/annurev.py.33.090195.00242110.1146/annurev.py.33.090195.002421 ↗

52. Liu, Y., Sun, X., Ouyong, A.: Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT Food Sci. Technol. 43, 602–607 (2010). https://doi.org/10.1016/j.lwt.2009.10.00810.1016/j.lwt.2009.10.008 ↗

53. Liu, S., Zhao, X., Zhu, Q., Huang, M., Guo, X.: A feature-enhanced approach based on joint domain alignment and multi-order derivative spectral reconstruction for predicting apple firmness using VIS-NIR spectroscopy. Food Chem. 476, 143457 (2025). https://doi.org/10.1016/j.foodchem.2025.14345710.1016/j.foodchem.2025.143457 ↗

54. Long, R.L., Walsh, K.B.: Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy. Aust. J. Agric. Res. 57, 403–410 (2006). https://doi.org/10.1071/AR0528510.1071/AR05285 ↗

55. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., Garcia-Navarrete, O.L., Blasco, J.: Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol. 5, 1121–1142 (2012). https://doi.org/10.1007/s11947-011-0725-110.1007/s11947-011-0725-1 ↗

56. Lu, Y., Lu, R., Zhang, Z.: Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imaging. Postharvest Biol. Technol. 180, 111624 (2021). https://doi.org/10.1016/j.postharvbio.2021.11162410.1016/j.postharvbio.2021.111624 ↗

57. Luo, Y., Dong, J., Shi, X., Wang, W., Li, Z., Sun, J.: Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy. Int. J. Food Eng. 17(5), 365–375 (2021)10.1515/ijfe-2020-0198 ↗

58. Ma, T., Xia, Y., Inagaki, T., Tsuchikawa, S.: Non-destructive and fast method of mapping the distribution of the soluble solids content and pH in kiwifruit using rotation near-infrared hyperspectral imaging approach. Postharvest Biol. Technol. 174, 111440 (2021). https://doi.org/10.1016/j.postharvbio.2020.11144010.1016/j.postharvbio.2020.111440 ↗

59. Magwaza, L.S., Opara, U.L., Nieuwoudt, H., Cronje, P.J., Saeys, W., Nicolaï, B.: NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food Bioprocess Technol. 5(2), 425–444 (2012). https://doi.org/10.1007/s11947-011-0697-110.1007/s11947-011-0697-1 ↗

60. Magwaza, L.S., Opara, U.L., Terry, L.A., Landahl, S., Cronje, P.J.R., Nieuwoudt, H.H., Hanssens, A., Saeys, W., Nicolaï, B.M.: Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of ‘Valencia’ oranges. J. Food Compos. Anal. 31, 144–154 (2013). https://doi.org/10.1016/j.jfca.2013.05.00710.1016/j.jfca.2013.05.007 ↗

61. Magwaza, L.S., Ford, H.D., Cronje, P.J., Opara, U.L., Landahl, S., Tatam, R.P., Terry, L.A.: Application of optical coherence tomography to non-destructively characterise rind breakdown disorder of ‘Nules Clementine’ mandarins. Postharvest Biol. Technol. 84, 16–21 (2013). https://doi.org/10.1016/j.postharvbio.2013.03.01910.1016/j.postharvbio.2013.03.019 ↗

62. Magwaza, L.S., Opara, U.L., Cronje, P.J., Landahl, S., Nieuwoudt, H.H., Mouazen, A.M., Terry, L.A.: Assessment of rind quality of ‘Nules Clementine’ mandarin fruit during postharvest storage: 2. robust VIS/NIRS PLS models for prediction of physico-chemical attributes. Sci. Hortic. 165, 421–432 (2014). https://doi.org/10.1016/j.scienta.2013.09.05010.1016/j.scienta.2013.09.050 ↗

63. Makino, Y., Isami, A., Suhara, T., Oshita, S., Kawagoe, Y., Tsukada, M., Kuroki, S.: Non-destructive analysis of internal and external qualities of mango fruits during storage by hyperspectral imaging. Acta Hortic. 1011, 443–449 (2013). https://doi.org/10.17660/ActaHortic.2013.1011.5610.17660/ActaHortic.2013.1011.56 ↗

64. Maniwara, P., Nakano, K., Boonyakiat, D., Ohashi, S., Hiroi, M., Tohyama, T.: The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. J. Food Eng. 143, 33–43 (2014). https://doi.org/10.1016/j.jfoodeng.2014.06.02810.1016/j.jfoodeng.2014.06.028 ↗

65. Manickavasagan, A., Jayas, D.S.: Infrared thermal imaging for agricultural and food applications. Stewart Postharvest Rev. 3(5), 1–8 (2007). https://doi.org/10.2212/spr.2007.5.410.2212/spr.2007.5.4 ↗

66. McClure, W.F., Hana, M., Sugiyama, J.: Quantitative NIR neural networks. NIR News 2(5), 8–9 (1991). https://doi.org/10.1255/nirn.8110.1255/nirn.81 ↗

67. McGlone, V.A., Fraser, D.G., Jordan, R.B., Kunnemeyer, R.: Internal quality assessment of mandarin fruit by VIS-NIR spectroscopy. J. Near Infrared Spectrosc. 11, 323–332 (2003)10.1255/jnirs.383 ↗

68. Mendoza, F., Lu, R., Ariana, D., Cen, H., Bailey, B.: Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 62, 149–160 (2011). https://doi.org/10.1016/j.postharvbio.2011.05.00910.1016/j.postharvbio.2011.05.009 ↗

69. Mohd Ali, M., Hashim, N., Abd Aziz, S., Lasekan, O.: Utilisation of deep learning with multimodal data fusion for determination of pineapple quality using thermal imaging. Agronomy 13(2), 401 (2023). https://doi.org/10.3390/agronomy1302040110.3390/agronomy13020401 ↗

70. Møller, S.M., Travers, S., Bertram, H.C., Bertelsen, M.G.: Prediction of postharvest dry matter, soluble solids content, firmness and acidity in apples (cv. Elshof) using NMR and NIR spectroscopy: a comparative study. Eur. Food Res. Technol. 237(6), 1021–1024 (2013). https://doi.org/10.1007/s00217-013-2087-610.1007/s00217-013-2087-6 ↗

71. Mukama, M., Ambaw, A., Opara, U.L.: Thermophysical properties of fruit—a review with reference to postharvest handling. J. Food Meas. Charact. 14(5), 2917–2937 (2020). https://doi.org/10.1007/s11694-020-00536-810.1007/s11694-020-00536-8 ↗

72. Naik, S.: Thermal imaging with fuzzy classifier for maturity and size based non-destructive mango (Mangifera indica L.) grading. In: Proceedings of the International Conference on Emerging Trends & Innovation in ICT (ICEI 2017), pp. 15–20 (2017). https://doi.org/10.1109/ETIICT.2017.797700310.1109/ETIICT.2017.7977003 ↗

73. Nandi, C.S., Tudu, B., Koley, C.: An automated machine vision based system for fruit sorting and grading. In: Proceedings of the 6th International Conference on Sensing Technology (ICST 2012), pp. 195–200 (2012). https://doi.org/10.1109/ICSensT.2012.646166910.1109/ICSensT.2012.6461669 ↗

74. Nandi, C.S., Tudu, B., Koley, C.: Machine vision based techniques for automatic mango fruit sorting and grading based on maturity level and size. In: Mason, A., Mukhopadhyay, S., Jayasundera, K., Bhattacharyya, N. (eds.) Sensing Technology: Current Status and Future Trends II, Smart Sensors, Measurement and Instrumentation, vol 8. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02315-1_210.1007/978-3-319-02315-1_2 ↗

75. Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I.: Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007). https://doi.org/10.1016/j.postharvbio.2007.06.02410.1016/j.postharvbio.2007.06.024 ↗

76. Nicolaï, B.M., Theron, K.I., Lammertyn, J.: Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 85, 243–252 (2007). https://doi.org/10.1016/j.chemolab.2006.07.00110.1016/j.chemolab.2006.07.001 ↗

77. Nicolaï, B.M., Bulens, I., de Baerdemaeker, J., de Ketelaere, B., Hertog, M.L.A.T.M., Verboven, P., Lammertyn, J., et al.: Non-destructive evaluation: detection of external and internal attributes frequently associated with quality and damage. In: Florkowski, W.J. (ed.) Postharvest Handling: A Systems Approach, pp. 421–442. Academic Press, Amsterdam (2009). https://doi.org/10.1016/B978-0-12-822845-6.00014-210.1016/B978-0-12-822845-6.00014-2 ↗

78. Olisah, C.C., Trewhella, B., Li, B., Smith, M.L., Winstone, B., Whitfield, E.C., Duncalfe, H.: Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment. Eng. Appl. Artif. Intell. 132, 107945 (2024). https://doi.org/10.1016/j.engappai.2024.10794510.1016/j.engappai.2024.107945 ↗

79. Onsawai, P., Phetpan, K., Khurnpoon, L., Sirisomboon, P.: Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit. Measurement 174, 108684 (2021). https://doi.org/10.1016/j.measurement.2020.10868410.1016/j.measurement.2020.108684 ↗

80. Pan, L., Zhang, Q., Zhang, W., Sun, Y., Hu, P., Tu, K.: Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chem. 192, 134–141 (2016). https://doi.org/10.1016/j.foodchem.2015.06.10610.1016/j.foodchem.2015.06.106 ↗

81. Pandey, R., Gamit, N., Naik, S.: Non-destructive quality grading of mango (Mangifera indica L.) based on CIELab colour model and size. In: 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 1246–1251 (2014). https://doi.org/10.1109/ICACCCT.2014.701929810.1109/ICACCCT.2014.7019298 ↗

82. Pandey, R., Gamit, N., Naik, S.: A novel non-destructive grading method for mango (Mangifera indica L.) using fuzzy expert system. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1087–1094. IEEE (2014). https://doi.org/10.1109/ICACCI.2014.696836610.1109/ICACCI.2014.6968366 ↗

83. Pathmanaban, P., Gnanavel, B.K., Anandan, S.S., Sathiyamurthy, S.: Advancing post-harvest fruit handling through AI-based thermal imaging: applications, challenges, and future trends. Discover Food 3(1), 27 (2023). https://doi.org/10.1007/s44187-023-00068-210.1007/s44187-023-00068-2 ↗

84. Peiris, K.H.S., Dull, G.G., Leffler, R.G., Kays, S.J.: Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers: implications for the development and use of NIR spectrometric techniques. HortScience 34(1), 114–118 (1999)10.21273/HORTSCI.34.1.114 ↗

85. Prakash, A.: Thermal remote sensing: concepts, issues and applications. Int. Arch. Photogramm. Remote Sens. 33, 239–243 (2000)

86. Pritty, S.B., Sudheer, K.P.: Standardization and near infrared reflectance spectroscopy-based quality evaluation of thermally processed tender jackfruit (Artocarpus heterophyllus L.). Doctoral dissertation, Department of Post Harvesting and Agricultural Engineering (2020)

87. Pullanagari, R.R., Li, M.: Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. J. Food Eng. 289, 110177 (2021). https://doi.org/10.1016/j.jfoodeng.2020.11017710.1016/j.jfoodeng.2020.110177 ↗

88. Qin, J., Burks, F.T., Kim, M.S., Chao, K.L., Ritenour, M.A.: Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sens. Instrum. Food Qual. Safety 2, 178–187 (2008). https://doi.org/10.1007/s11694-008-9043-310.1007/s11694-008-9043-3 ↗

89. Qin, J., Chao, K., Kim, M.S., Lu, R., Burks, T.F.: Hyperspectral and multispectral imaging for evaluating food safety and quality. J. Food Eng. 118(2), 157–171 (2013). https://doi.org/10.1016/j.jfoodeng.2013.04.00110.1016/j.jfoodeng.2013.04.001 ↗

90. Rahman, A., Park, E., Bae, H., Cho, B.K.: Hyperspectral imaging technique to evaluate the firmness and the sweetness index of tomatoes. Korean J. Agric. Sci. 45(4), 823–837 (2018). https://doi.org/10.7744/kjoas.2018007510.7744/kjoas.20180075 ↗

91. Rajkumar, P., Wang, N., ElMasry, G., Raghavan, G.S.V., Gariepy, Y.: Studies on banana fruit quality and maturity stages using hyperspectral imaging. J. Food Eng. 108(1), 194–200 (2012). https://doi.org/10.1016/j.jfoodeng.2011.00210.1016/j.jfoodeng.2011.002 ↗

92. Ribeiro da Luz, B.: Attenuated total reflectance spectroscopy of plant leaves: a tool for ecological and botanical studies. New Phytol. 173, 305–318 (2006). https://doi.org/10.1111/j.1469-8137.2006.01823.x10.1111/j.1469-8137.2006.01823.x ↗

93. Sablani, S.S., Rahman, M.S.: Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity. Food Res. Int. 36(6), 617–623 (2003). https://doi.org/10.1016/S0963-9969(03)00012-710.1016/S0963-9969(03)00012-7 ↗

94. Saechua, W., Sharma, S., Nakawajana, N., Leepaitoon, K., Chunsri, R., Posom, J., Roeksukrungrueang, C., Siritechavong, T., Phanomsophon, T., Sirisomboon, P., Lapcharoensuk, R., Pornchaloempong, P.: Integrating VIS-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids of durian pulp. Postharvest Biol. Technol. 181, 111640 (2021). https://doi.org/10.1016/j.postharvbio.2021.11164010.1016/j.postharvbio.2021.111640 ↗

95. Saeys, W., Mouazen, A.M., Ramon, H.: Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst. Eng. 91(4), 393–402 (2005). https://doi.org/10.1016/j.biosystemseng.2005.05.00110.1016/j.biosystemseng.2005.05.001 ↗

96. Sankaran, S., Maja, J.M., Buchanon, S., Ehsani, R.: Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques. Sensors (Basel) 13(2), 2117–2130 (2013). https://doi.org/10.3390/s13020211710.3390/s130202117 ↗

97. Shah, S.S.A., Zeb, A., Qureshi, W.S., Malik, A.U., Tiwana, M., Walsh, K., Alanazi, E.: Mango maturity classification instead of maturity index estimation: a new approach towards handheld NIR spectroscopy. Infrared Phys. Technol. 115, 103639 (2021). https://doi.org/10.1016/j.infrared.2021.10363910.1016/j.infrared.2021.103639 ↗

98. Shao, Y., Bao, Y., He, Y.: Visible/near-infrared spectra for linear and nonlinear calibrations: a case to predict soluble solids contents and pH value in peach. Food Bioprocess Technol. 4, 1376–1383 (2011). https://doi.org/10.1007/s11947-009-0227-610.1007/s11947-009-0227-6 ↗

99. Shaw, G., Manolakis, D.: Signal processing for hyperspectral image exploitation. IEEE Signal Process. Mag. 19(1), 12–16 (2002). https://doi.org/10.1109/79.97471510.1109/79.974715 ↗

100. Shukla, A., Kot, R.: An overview of hyperspectral remote sensing and its applications in various disciplines. IRA-Int. J. Appl. Sci. 5(2), 85–90 (2016)