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The Application of Imaging Methods and Machine Learning in the Agroindustry Sector at Production Activity

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2346))

  • 63 Accesses

Abstract

The agricultural production sector is of great interest to national economies, their GDP and the population's food chain. During digitalization and modernization, imaging techniques and machine learning are important driving factors.

In this research, a narrative critical literature review was conducted on the most used imaging modalities and their use with machine learning methods, which are examined along with their applications in the agricultural industry in production activities, as well as their limitations and existing challenges. This research is intended to support a further development of a technological framework for intelligent precision agroindustry production.

It was found that the most used imaging methods are hyperspectral and multispectral imaging, infrared thermal imaging, magnetic resonance imaging, X-ray imaging, scanning electron microscopy and ultraviolet imaging. The majority of employments of machine learning along with imaging was using supervised learning algorithms, there were a few applications using unsupervised and reinforcement learning algorithms.

From the results and analysis, it can be concluded that the use of imaging techniques enables an increase in quality and profit maximization of agro-industrial products and their characterization data, which can be better analyzed with the help of machine learning methods and lead to a more sustainable and innovative agro-industrial productive sector.

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References

  1. Silva, E., Grácio, C.: The agro-food sector in Portugal: evolution, challenges, and opportunities. Port. Econ. J. 19(2), 89–115 (2020)

    Article  MATH  Google Scholar 

  2. Garske, B., Bau, A., Ekardt, F.: Digitalization and AI in European agriculture: a strategy for achieving climate and biodiversity targets? Sustainability 13(9), 4652 (2021)

    Article  MATH  Google Scholar 

  3. Zhang, C., Kovacs, J.M.: The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13(6), 693–712 (2012)

    Article  MATH  Google Scholar 

  4. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18(8), 2674 (2018)

    Article  Google Scholar 

  5. Shirsath, P.B., Singh, A.K., Pathak, H.: Characterizing regional vulnerability to climate change using the IPCC framework. Clim. Change 143(1–2), 73–87 (2017)

    Google Scholar 

  6. Mahlein, A.K., Oerke, E.C., Steiner, U., Dehne, H.W.: Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 133(1), 197–209 (2012)

    Article  Google Scholar 

  7. Nasirahmadi, A., Edwards, S.A., Sturm, B.: Automated image processing for behavioural monitoring of pigs: a review. Comput. Electron. Agric. 141, 302–317 (2017)

    MATH  Google Scholar 

  8. Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C.: Internet of Things in agriculture, recent advances and future challenges. Biosys. Eng. 164, 31–48 (2017)

    Article  MATH  Google Scholar 

  9. Gebbers, R., Adamchuk, V.I.: Precision agriculture and food security. Science 327(5967), 828–831 (2010)

    Article  MATH  Google Scholar 

  10. Amigo, J.M.: Hyperspectral and multispectral imaging: setting the scene. In: Data Handling in Science and Technology, vol. 32, pp. 3–16. Elsevier, Amsterdam (2019)

    Google Scholar 

  11. Mahlein, A.K.: Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016)

    Article  Google Scholar 

  12. Mulla, D.J.: Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosys. Eng. 114(4), 358–371 (2013)

    Article  MATH  Google Scholar 

  13. Yaqoob, M., Sharma, S., Aggarwal, P.: Imaging techniques in agro-industry and their applications, a review. J. Food Meas. Character. 15, 2329–2343 (2021)

    Article  MATH  Google Scholar 

  14. Sridhar, S., Gupta, R., Louis, G.: Reviewing the trend in image processing techniques used in the agriculture industry. In: Proceedings of 4th International Conference on Recent Trends in Environmental Science and Engineering, vol. 163, pp. 1–9 (2020)

    Google Scholar 

  15. Choudhury, M., et al.: Infrared imaging a new non-invasive machine learning technology for animal husbandry. Imaging Sci. J. 68(4), 240–249 (2020)

    Article  MATH  Google Scholar 

  16. Nääs, I.A., Garcia, R.G., Caldara, F.R.: Infrared thermal image for assessing animal health and welfare. J. Animal Behav. Biometeorol. 2(3), 66–72 (2020)

    Article  MATH  Google Scholar 

  17. Zheng, S., Zhou, C., Jiang, X., Huang, J., Xu, D.: Progress on infrared imaging technology in animal production: a review. Sensors 22(3), 705 (2022)

    Article  MATH  Google Scholar 

  18. Renu, R., Chidanand, D.V.: Internal quality classification of agricultural produce using non-destructive image processing technologies (soft X-ray). Int. J. Latest Trends Eng. Technol. 2(4), 535–543 (2013)

    MATH  Google Scholar 

  19. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)

    Google Scholar 

  20. Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2013)

    Google Scholar 

  21. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)

    Google Scholar 

  22. Nosratabadi, S., et al.: Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics 8(10), 1799 (2020)

    Article  MATH  Google Scholar 

  23. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    Google Scholar 

  24. Gupta, A., Chaurasiya, V.K.: Reinforcement learning based energy management in wireless body area network: a survey. In: 2019 IEEE Conference on Information and Communication Technology, pp. 1–6 (2019)

    Google Scholar 

  25. Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)

    Article  MATH  Google Scholar 

  26. Aghighi, H., Azadbakht, M., Yunus, P., Abdullah, S., Halin, A.A.: A review of hyperspectral imaging for food quality and safety. Appl. Biotechnol. Food Sci. 9(3), 123–139 (2018)

    Google Scholar 

  27. Calderón, R., Navas-Cortes, J.A., Lucena, C., Zarco-Tejada, P.J.: High-throughput UAV-based remote sensing for plant stress detection: rice case study. Eur. J. Agron. 45, 50–67 (2013)

    Google Scholar 

  28. Xie, C., Yang, C.: A review of hyperspectral imaging for plant phenotyping. Comput. Electron. Agric. 175, 105331 (2020)

    MATH  Google Scholar 

  29. Adao, T., et al.: Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing 9(11), 1110 (2017)

    Article  MATH  Google Scholar 

  30. Behmann, J., Steinrücken, J., Plümer, L.: Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote. Sens. 93, 98–111 (2014)

    Article  MATH  Google Scholar 

  31. Tsai, C.J., Chou, T.Y.: Challenges and opportunities in deploying artificial intelligence (AI) applications in real-world agriculture. Sustainability 12(17), 6927 (2020)

    MATH  Google Scholar 

  32. Guan, L., Niu, L., Zhang, Y., Dong, W., Wei, Y.: Applications of thermal imaging in agriculture and food industry: a review. Crit. Rev. Food Sci. Nutr. 59(15), 2439–2456 (2019)

    MATH  Google Scholar 

  33. Jones, H.G., Sirault, X.R.R.: Thermal and hyperspectral remote sensing in precision agriculture: crop stress and yield analysis. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, vol. 9239, p. 92390L (2014)

    Google Scholar 

  34. Manuwa, S.I., Odey, S.O.: Thermal imaging techniques in agricultural and biological research. J. Agric. Biol. Eng. 5(2), 12–24 (2012)

    Google Scholar 

  35. Berni, J.A.J., Zarco-Tejada, P.J., Suárez, L., Fereres, E.: Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47(3), 722–738 (2009)

    Article  Google Scholar 

  36. Oomen, R.A., Van Der Kolk, J.H., Van Der Zijden, A.M.: Comparison of low-field MRI and radiography for the detection of bovine lung consolidation. Vet. Radiol. Ultrasound 60(3), 281–290 (2019)

    Google Scholar 

  37. van Dusschoten, D., et al.: Quantitative 3D analysis of plant roots growing in soil using magnetic resonance imaging. Plant Physiol. 170(3), 1176–1188 (2016)

    Article  MATH  Google Scholar 

  38. Breyer, D., Jaeger, D., Gehl, C., Mair, P.: Magnetic resonance imaging in small animal veterinary practice: an update. Vet. J. 228, 70–77 (2017)

    Google Scholar 

  39. Peng, Y., Li, M.: X-ray computed tomography for agricultural and food research applications: a review. Comput. Electron. Agric. 138, 145–157 (2017)

    MATH  Google Scholar 

  40. Hong, S.J., et al.: Nondestructive prediction of pepper seed viability using single and fusion information of hyperspectral and X-ray images. Sens. Actuators, A 350, 114151 (2023)

    Article  MATH  Google Scholar 

  41. Berckmans, D.: Smart farming with sensors and actuators, the rock around which the next agricultural revolution will revolve. J. Agric. Eng. 48(3), 106–114 (2017)

    MATH  Google Scholar 

  42. Goldstein, J., et al.: Scanning Electron Microscopy and X-ray Microanalysis. Springer, New York (2017)

    MATH  Google Scholar 

  43. Goodhew, P.J., Humphreys, J.: Electron Microscopy and Analysis. CRC Press, Boca Raton (2014)

    Google Scholar 

  44. Ravichandran, R., Sundarrajan, S., Venugopal, J.R., Mukherjee, S., Ramakrishna, S.: Applications of SEM-EDX in pharmaceutical formulations: a review. Mater. Sci. Eng., C 31(4), 1855–1865 (2011)

    Google Scholar 

  45. Bhattacharya, A., Ghosh, S. K.: Artificial Intelligence in Microscopy and Imaging. CRC Press, Boca Raton (2012)

    Google Scholar 

  46. Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., Peterson, K.: Unmanned aerial system advances health mapping in Sub-Saharan Africa. Sci. Rep. 7(1), 42713 (2017)

    Google Scholar 

  47. Jacovides, C.P., Fountas, S., Wulfsohn, D., Blackmore, B.S.: A review of applications of unmanned aerial vehicle systems for agricultural management. Span. J. Agric. Res. 15(2), e1108 (2017)

    MATH  Google Scholar 

  48. Patrício, D.I., Rieder, R.: Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153, 69–81 (2018)

    Article  MATH  Google Scholar 

  49. Hamuda, E., Mc Ginley, B., Glavin, M., Jones, E.: Automatic crop detection under field conditions using the modified adaptive boosting (AdaBoost.MH) algorithm. Comput. Electron. Agric. 124, 234–242 (2016)

    Google Scholar 

  50. Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  51. Ma, L., Zheng, G., Li, P., Shi, Y.: Application of unsupervised learning algorithms to agricultural remote sensing image classification. Remote Sens. 11(6), 668 (2019)

    MATH  Google Scholar 

  52. Whelan, B., Taylor, J.: Precision Agriculture for Grain Production Systems. CSIRO Publishing (2013)

    Google Scholar 

  53. Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S.: Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 21(2), 110–124 (2016)

    Article  MATH  Google Scholar 

  54. Jin, X., Kumar, L., Li, Z.: A review of data mining-based financial fraud detection research. Procedia Comput. Sci. 91, 586–593 (2018)

    MATH  Google Scholar 

  55. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  MATH  Google Scholar 

  56. Pantazi, X.E., Moshou, D., Tamouridou, A.A., Paraskevas, M.: Automated leaf disease detection in different crop species through image features analysis and One-Class Support Vector Machines. Comput. Electron. Agric. 122, 41–48 (2016)

    Google Scholar 

  57. Subeesh, A., Mehta, C.R.: Automation and digitization of agriculture using artificial intelligence and internet of things. Artif. Intell. Agric. 5, 278–291 (2021)

    MATH  Google Scholar 

  58. Tu, S., Deng, Z.: An automatic data labeling method for deep learning in large-scale video surveillance applications. Multimed. Tools Appl. 76, 10399–10419 (2017)

    MATH  Google Scholar 

  59. Reyes, J., Venturini, S.: High-performance computing in agriculture: machine learning techniques and applications. Appl. Sci. 8(7), 1055 (2018)

    MATH  Google Scholar 

  60. Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 67–76 (2018)

    Article  MATH  Google Scholar 

  61. Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: A brief survey of deep reinforcement learning. arXiv e-prints, arXiv-1708 (2017)

    Google Scholar 

  62. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  MATH  Google Scholar 

  63. Zhang, X., Friedland, C.J., Zhang, X., El-Ghazawi, T.: Applying reinforcement learning to irrigation decision-making in precision agriculture. IFAC-PapersOnLine 51(17), 161–166 (2018)

    MATH  Google Scholar 

  64. Duckett, T., Pearson, S., Blackmore, S., Grieve, B.: Agricultural robotics: the future of robotic agriculture. arXiv e-prints, arXiv-1806 (2018)

    Google Scholar 

  65. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013)

    Article  MATH  Google Scholar 

  66. Norton, T., Chen, C., Larsen, M.L.V., Berckmans, D.: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer. Animal 13(12), 3009–3017 (2019)

    Article  Google Scholar 

  67. Zhang, D., Zhou, G.: Estimation of soil moisture from optical and thermal remote sensing: a review. Sensors 16(8), 1308 (2016)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. ISLA Santarém, Santarém, Portugal

    Ricardo Vardasca, Antonio Pratas, Marco Tereso & Fernando Bento

  2. LAETA, INEGI, Universidade do Porto, Porto, Portugal

    Ricardo Vardasca

  3. VISTA Lab, ALGORITMI, Universidade de Évora, Évora, Portugal

    Marco Tereso

  4. ISTAR, ISCTE-IUL, Lisboa, Portugal

    Fernando Bento

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  1. Ricardo Vardasca
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  2. Antonio Pratas
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  3. Marco Tereso
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  4. Fernando Bento
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Correspondence to Ricardo Vardasca .

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Editors and Affiliations

  1. Universidad Estatal Península de Santa Elena, Santa Elena, Ecuador

    Teresa Guarda

  2. University of Minho, Guimarães, Portugal

    Filipe Portela

  3. Universidad Andrés Bello, Santiago, Chile

    Gustavo Gatica

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Vardasca, R., Pratas, A., Tereso, M., Bento, F. (2025). The Application of Imaging Methods and Machine Learning in the Agroindustry Sector at Production Activity. In: Guarda, T., Portela, F., Gatica, G. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024. Communications in Computer and Information Science, vol 2346. Springer, Cham. https://doi.org/10.1007/978-3-031-83210-9_24

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  • DOI: https://doi.org/10.1007/978-3-031-83210-9_24

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