1. Abdulridha, J., Ampatzidis, Y., Kakarla, S.C., & Roberts, P. (2020). Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agriculture, 21 (5), 955-978. [DOI:10.1007/s11119-019-09703-4]

2. Abdulridha, J., Batuman, O., & Ampatzidis, Y. (2019). UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sensing, 11 (11), 1373. [DOI:10.3390/rs11111373]

3. Alisaac, E., Behmann, J., Rathgeb, A., Karlovsky, P., Dehne, H.-W., & Mahlein, A.-K. (2019). Assessment of Fusarium infection and mycotoxin contamination of wheat kernels and flour using hyperspectral imaging. Toxins, 11 (10), 556. [DOI:10.3390/toxins11100556]

4. Arjoune, Y., Sugunaraj, N., Peri, S., Nair, S.V., Skurdal, A., Ranganathan, P., & Johnson, B. (2022). Soybean cyst nematode detection and management: a review. Plant Methods, 18 (1), 1-39. [DOI:10.1186/s13007-022-00933-8]

5. Ashourloo, D., Aghighi, H., Matkan, A.A., Mobasheri, M.R., & Rad, A.M. (2016). An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement. IEEE Journal of selected topics in applied earth observations and remote sensing, 9 (9), 4344-4351. [DOI:10.1109/JSTARS.2016.2575360]

6. Astani, M., Hasheminejad, M., & Vaghefi, M. (2022). A diverse ensemble classifier for tomato disease recognition. Computers Electronics in Agriculture, 198107054. [DOI:10.1016/j.compag.2022.107054]

7. Atherton, D., Choudhary, R., & Watson, D. Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants prior to visual disease symptoms. In: 2017 ASABE Annual International Meeting, 2017. American Society of Agricultural and Biological Engineers, p 1. http://dx.doi.org/10.13031/aim.201701010 [DOI:10.13031/aim.201701010]

8. Bendel, N., Backhaus, A., Kicherer, A., Köckerling, J., Maixner, M., Jarausch, B., Biancu, S., Klück, H.-C., Seiffert, U., Voegele, R.T., & Töpfer, R. (2020). Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging. Remote Sensing, 12 (24), 4151. [DOI:10.3390/rs12244151]

9. Bohnenkamp, D., Kuska, M., Mahlein, A.K., & Behmann, J. (2019). Hyperspectral signal decomposition and symptom detection of wheat rust disease at the leaf scale using pure fungal spore spectra as reference. Plant Pathology, 68 (6), 1188-1195. [DOI:10.1111/ppa.13020]

10. Brdar, S., Panić, M., Hogeveen-van Echtelt, E., Mensink, M., Grbović, Ž., Woltering, E., & Chauhan, A. (2021). Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections. Scientific Reports, 11 (1), 1-17. [DOI:10.1038/s41598-021-02302-2]

11. Cheshkova, A. (2022). A review of hyperspectral image analysis techniques for plant disease detection and identif ication. Vavilov Journal of Genetics and Breeding, 26 (2), 202-213. [DOI:10.18699/VJGB-22-25]

12. Gašparović, M. (2020) Urban growth pattern detection and analysis. In: Urban Ecology. Elsevier, pp 35-48. [DOI:10.1016/B978-0-12-820730-7.00003-3]

13. Guo, A., Huang, W., Dong, Y., Ye, H., Ma, H., Liu, B., Wu, W., Ren, Y., Ruan, C., & Geng, Y. (2021). Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sensing, 13 (1), 123. [DOI:10.3390/rs13010123]

14. Guo, L., Zhang, H., Shi, T., Chen, Y., Jiang, Q., & Linderman, M. (2019). Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma, 33732-41. [DOI:10.1016/j.geoderma.2018.09.003]

15. Ha, J.G., Moon, H., Kwak, J.T., Hassan, S.I., Dang, M., Lee, O.N., & Park, H.Y. (2017). Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Applied Remote Sensing, 11 (4), 042621-042621. [DOI:10.1117/1.JRS.11.042621]

16. Halicek, M., Fabelo, H., Ortega, S., Callico, G.M., & Fei, B. (2019). In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers, 11 (6), 756. [DOI:10.3390/cancers11060756]

17. Hruska, R., Mitchell, J., Anderson, M., & Glenn, N.F. (2012). Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle. Remote Sensing, 4 (9), 2736-2752. [DOI:10.3390/rs4092736]

18. Hu, Y., Chang, J., Li, Y., Zhang, W., Lai, X., & Mu, Q. (2023). High Zoom Ratio Foveated Snapshot Hyperspectral Imaging for Fruit Pest Monitoring. Journal of Spectroscopy, 2023. [DOI:10.1155/2023/2286867]

19. Huang, Y., Feng, G., Tewolde, H., & Shankle, M.W. (2023). Remote Sensing from Different Sources for Crop Growth Monitoring in the Area of the Lower Northern Mississippi. Challenges, 14 (1), 12. [DOI:10.3390/challe14010012]

20. Izzo, R.R., Lakso, A.N., Marcellus, E.D., Bauch, T.D., Raqueño, N.G., & van Aardt, J. An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes Wine Country of Upstate New York. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 2019. SPIE, pp 276-293. [DOI:10.1117/12.2518762]

21. Jang, G., Kim, J., Yu, J.-K., Kim, H.-J., Kim, Y., Kim, D.-W., Kim, K.-H., Lee, C.W., & Chung, Y.S. (2020). Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application. Remote Sensing, 12 (6), 998. [DOI:10.3390/rs12060998]

22. Jarmer, T. (2013). Spectroscopy and hyperspectral imagery for monitoring summer barley. International journal of remote sensing, 34 (17), 6067-6078. [DOI:10.1080/01431161.2013.793871]

23. Jiang, X., Luo, S., Ye, Q., Li, X., & Jiao, W. (2022). Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning. Agriculture, 12 (8), 1188. [DOI:10.3390/agriculture12081188]

24. Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., & Cao, W. (2021). Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote Sensing, 13 (18), 3612. [DOI:10.3390/rs13183612]

25. Kuska, M.T., Heim, R.H., Geedicke, I., Gold, K.M., Brugger, A., & Paulus, S. (2022). Digital plant pathology: a foundation and guide to modern agriculture. Journal of Plant Diseases and Protection, 129 (3), 457-468. [DOI:10.1007/s41348-022-00600-z]

26. Leucker, M., Mahlein, A.-K., Steiner, U., & Oerke, E.-C. (2016). Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging. Phytopathology, 106 (2), 177-184. [DOI:10.1094/PHYTO-04-15-0100-R]

27. Li, K.-Y., Sampaio de Lima, R., Burnside, N.G., Vahtmäe, E., Kutser, T., Sepp, K., Cabral Pinheiro, V.H., Yang, M.-D., Vain, A., & Sepp, K. (2022). Toward automated machine learning-based hyperspectral image analysis in crop yield and biomass estimation. Remote Sensing, 14 (5), 1114. [DOI:10.3390/rs14051114]

28. Li, X., Chen, K., & He, Y. (2020). In situ and non-destructive detection of the lipid concentration of Scenedesmus obliquus using hyperspectral imaging technique. Algal Research, 45101680. [DOI:10.1016/j.algal.2019.101680]

29. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18 (8), 2674. [DOI:10.3390/s18082674]

30. Liu, B., Li, R., Li, H., You, G., Yan, S., & Tong, Q. (2019). Crop/Weed Discrimination Using a Field Imaging Spectrometer System. Sensors, 19 (23), 5154. [DOI:10.3390/s19235154]

31. Lu, B., Dao, P.D., Liu, J., He, Y., & Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12 (16), 2659. [DOI:10.3390/rs12162659]

32. Lu, Y., Young, S., Linder, E., Whipker, B., & Suchoff, D. (2022). Hyperspectral imaging with machine learning to differentiate cultivars, growth stages, flowers, and leaves of industrial hemp (Cannabis sativa L.). Frontiers in Plant Science, 123392. [DOI:10.3389/fpls.2021.810113]

33. Mahlein, A.-K., Alisaac, E., Al Masri, A., Behmann, J., Dehne, H.-W., & Oerke, E.-C. (2019). Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring fusarium head blight of wheat on spikelet scale. Sensors, 19 (10), 2281. [DOI:10.3390/s19102281]

34. Mahlein, A.-K., Kuska, M.T., Behmann, J., Polder, G., & Walter, A. (2018). Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annual review of phytopathology, 56535-558. [DOI:10.1146/annurev-phyto-080417-050100]

35. Mahlein, A.-K., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2010). Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture, 11 (4), 413-431. [DOI:10.1007/s11119-010-9180-7]

36. McCann, C., Repasky, K.S., Lawrence, R., & Powell, S. (2017). Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection. ISPRS Journal of Photogrammetry and Remote Sensing, 131121-133. [DOI:10.1016/j.isprsjprs.2017.07.015]

37. Miphokasap, P., & Wannasiri, W. (2018). Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability, 10 (4), 1266. [DOI:10.3390/su10041266]

38. Mishra, P., Asaari, M.S.M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017). Close range hyperspectral imaging of plants: A review. Biosystems Engineering, 16449-67. [DOI:10.1016/j.biosystemseng.2017.09.009]

39. Mishra, P., Lohumi, S., Khan, H.A., & Nordon, A .(2020). Close-range hyperspectral imaging of whole plants for digital phenotyping: Recent applications and illumination correction approaches. Computers and Electronics in Agriculture, 178105780. [DOI:10.1016/j.compag.2020.105780]

40. Nanni, M.R., Demattê, J.A.M., Rodrigues, M., Santos, G.L.A.A.d., Reis, A.S., Oliveira, K.M.d., Cezar, E., Furlanetto, R.H., Crusiol, L.G.T., & Sun, L. (2021). Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors. Remote Sensing, 13 (9), 1782. [DOI:10.3390/rs13091782]

41. Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., & Kwasniewski, M.T. (2021). Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors, 21 (3). [DOI:10.3390/s21030742]

42. O'rourke, S., & Holden, N. (2012). Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible-near infrared hyperspectral images. Soil Science Society of America Journal 76 (2), 586-596. [DOI:10.2136/sssaj2011.0053]

43. Pechanec, V., Mráz, A., Rozkošný, L., & Vyvlečka, P. (2021). Usage of airborne hyperspectral imaging data for identifying spatial variability of soil nitrogen content. ISPRS International Journal of Geo-Information, 10 (6), 355. [DOI:10.3390/ijgi10060355]

44. Polder, G., Blok, P.M., De Villiers, H.A., Van der Wolf, J.M., & Kamp, J. (2019). Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Frontiers in Plant Science, 10209. [DOI:10.3389/fpls.2019.00209]

45. Proshkin, Y.A., Smirnov, A.A., Semenova, N.A., Dorokhov, A.S., Burynin, D.A., Ivanitskikh, A.S., & Panchenko, V.A. (2021). Assessment of ultraviolet impact on main pigment content in purple basil (Ocimum basilicum L.) by the spectrometric method and hyperspectral images analysis. Applied Sciences, 11 (19), 8804. [DOI:10.3390/app11198804]

46. Scherrer, B., Sheppard, J., Jha, P., & Shaw, J.A. (2019). Hyperspectral imaging and neural networks to classify herbicide-resistant weeds. Journal of Applied Remote Sensing, 13(4), 044516. [DOI:10.1117/1.JRS.13.044516]

47. Shoshany, M., Goldshleger, N., & Chudnovsky, A. (2013). Monitoring of agricultural soil degradation by remote-sensing methods: A review. International Journal of Remote Sensing, 34 (17), 6152-6181. [DOI:10.1080/01431161.2013.793872]

48. Taghinezhad, E., Szumny, A., & Figiel, A. (2023). The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process. Molecules, 28 (7), 2930. [DOI:10.3390/molecules28072930]

49. Thomas, S., Behmann, J., Steier, A., Kraska, T., Muller, O., Rascher, U., & Mahlein, A.-K. (2018a). Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant methods, 14 (1), 1-12. [DOI:10.1186/s13007-018-0313-8]

50. Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., & Mahlein, A.-K. (2018b). Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection, 1255-20. [DOI:10.1007/s41348-017-0124-6]

51. Tian, S., Lu, Q., & Wei, L. (2022). Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery. Remote Sensing, 14 (14), 3292. [DOI:10.3390/rs14143292]

52. Türkoğlu, M., & Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Sciences, 27 (3), 1636-1651. [DOI:10.3906/elk-1809-181]

53. Vidal, M., & Amigo, J.M. (2012). Pre-processing of hyperspectral images. Essential steps before image analysis. Chemometrics and Intelligent Laboratory Systems, 117138-148. [DOI:10.1016/j.chemolab.2012.05.009]

54. Wan, L., Li, H., Li, C., Wang, A., Yang, Y., & Wang, P. (2022). Hyperspectral Sensing of Plant Diseases: Principle and Methods. Agronomy, 12 (6). [DOI:10.3390/agronomy12061451]

55. Wang, S., Guan, K., Zhang, C., Zhou, Q., Wang, S., Wu, X., Jiang, C., Peng, B., Mei, W., & Li, K. (2023). Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data. Remote Sensing of Environment. [DOI:10.1016/j.rse.2022.113366]

56. Wang, X., Zhang, Y., Ma, X., Xu, T., & Arce, G.R. (2018). Compressive spectral imaging system based on liquid crystal tunable filter. Optics express, 26 (19), 25226-25243. [DOI:10.1364/OE.26.025226]

57. Wen-jun, W., Zhi-wei, L., Can, W., De-cong, Z., & Hui-ling, D. (2019). Prediction of Available Potassium Content in Cinnamon Soil Using Hyperspectral Imaging Technology. Spectroscopy and Spectral Analysis 39 (5), 1579-1585. DOI: 10.3964/j.issn.1000-0593(2019)05-1579-07

58. Wu, D., & Sun, D.-W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review-Part I: Fundamentals. Innovative Food Science and Emerging Technologies, 191-14. [DOI:10.1016/j.ifset.2013.04.014]

59. Yin, F., Wu, M., Liu, L., Zhu, Y., Feng, J., Yin, D., Yin, C., & Yin, C. (2021). Predicting the abundance of copper in soil using reflectance spectroscopy and GF5 hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 102102420. [DOI:10.1016/j.jag.2021.102420]

60. Yuan, L., Yan, P., Han, W., Huang, Y., Wang, B., Zhang, J., Zhang, H., & Bao, Z. (2019). Detection of anthracnose in tea plants based on hyperspectral imaging. Computers and Electronics in Agriculture, 167105039. [DOI:10.1016/j.compag.2019.105039]

61. Zhao, H., Yang, C., Guo, W., Zhang, L., & Zhang, D. (2020). Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing, 12 (12), 1930. [DOI:10.3390/rs12121930]

62. Zhou, Y., Chen, J., Ma, J., Han, X., Chen, B., Li, G., Xiong, Z., & Huang, F. (2022). Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging. Scientific Reports, 12 (1), 21140. [DOI:10.1038/s41598-022-23326-2]

63. Zhu, H., Chu, B., Zhang, C., Liu, F., Jiang, L., & He, Y. (2017). Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Scientific Reports, 7 (1), 1-12. [DOI:10.1038/s41598-017-04501-2]

64. Žibrat, U., Gerič Stare, B., Knapič, M., Susič, N., Lapajne, J., & Širca, S. (2021). Detection of root-knot nematode Meloidogyne luci infestation of potato tubers using hyperspectral remote sensing and real-time PCR molecular methods. Remote Sensing, 13 (10), 1996. [DOI:10.3390/rs13101996]