An AI-empowered energy-efficient portable NIRS solution for precision agriculture: A pilot study on a citrus fruit

• Autorzy: Cisotto Giulia , Tegegn Dagmawi Delelegn , Zancanaro Alberto , Reguzzoni Ivan , Lotti Edoardo , Manzoni Sara L. , Zoppis Italo F.

Identyfikatory

DOI

10.15439/2024F5111

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

Smart agriculture has seen impressive progresses in monitoring the quality of the crop and early detecting the onset of pathogens. However, this is typically achieved through smart, expensive, and energy-demanding robots and autonomous systems. We propose an AI-empowered portable low-cost short-wave near-infrared spectroscopy (sw-NIRS) solution that allows non-destructive measurements from plants and vegetables. In this pilot study, we specifically targeted an orange fruit and showed that it is possible to classify its different parts through sw-NIRS in the range 1350-2150 nm by using AI models, exceeding 98\% accuracy. Also, we explored the minimum amount of energy needed to reach such high classification performance. In the future, we aim to extend this investigation to other targets (e.g., bean plants), to develop AI architectures to more accurately model the physiological conditions of the target, and to create a network of sw-NIRS sensors to simultaneously monitor a large-scale crop.

Słowa kluczowe

EN

infrared spectroscopy; machine learning; AI; chemometrics; energy efficient; green technology; smart agrifood; precision agriculture

PL

spektroskopia w bliskiej podczerwieni; uczenie maszynowe; sztuczna inteligencja; chemometria; energooszczędność; zielona technologia; rolnictwo inteligentne; rolnictwo precyzyjne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 313--–318
• Opis fizyczny: Bibliogr. 22 poz., il., tab., wykr.

Twórcy

autor

Cisotto Giulia

• University of Milano-Bicocca, Department of Informatics, Systems and Communication, Viale Sarca, 336, 20126 Milan, Italy

• https://orcid.org/0000-0002-9554-9367

autor

Tegegn Dagmawi Delelegn

• SeleTech Engineering Srl Via Volturno, 37, 20861 Brugherio (Monza Brianza), Italy

• https://orcid.org/0000-0002-5031-7589

autor

Zancanaro Alberto

• Department of Information Engineering, Via Gradenigo, 6, 35136 Padova, Italy

• https://orcid.org/0000-0002-5276-7030

autor

Reguzzoni Ivan

• SeleTech Engineering Srl Via Volturno, 37, 20861 Brugherio (Monza Brianza), Italy

autor

Lotti Edoardo

• SeleTech Engineering Srl Via Volturno, 37, 20861 Brugherio (Monza Brianza), Italy

autor

Manzoni Sara L.

• University of Milano-Bicocca, Department of Informatics, Systems and Communication, Viale Sarca, 336, 20126 Milan, Italy

• https://orcid.org/0000-0002-6406-536X

autor

Zoppis Italo F.

• University of Milano-Bicocca, Department of Informatics, Systems and Communication, Viale Sarca, 336, 20126 Milan, Italy

• https://orcid.org/0000-0001-7312-7123

Bibliografia

• 1. D. D. Tegegn, “Process of analyzing organic materials, based on processing of near-nfrared spectra through advanced methods (PhD Thesis),” 2023.
• 2. P. Rodríguez, J. Villamizar, L. Londoño, T. Tran, and F. Davrieux, “Quantification of dry matter content in hass avocado by near-infrared spectroscopy (NIRS) scanning different fruit zones,” Plants, vol. 12, no. 17, p. 3135, 2023.
• 3. K. Ncama, L. S. Magwaza, A. Mditshwa, and S. Z. Tesfay, “Application of visible to near-infrared spectroscopy for non-destructive assessment of quality parameters of fruit,” Infrared Spectroscopy-Principles, Advances, and Applications, 2018.
• 4. A. M. Cavaco, D. Passos, R. M. Pires, M. D. Antunes, and R. Guerra, “Nondestructive assessment of citrus fruit quality and ripening by visible-near infrared reflectance spectroscopy,” Citrus-Research, Development and Biotechnology, p. 95970, 2021.
• 5. A. Zancanaro, G. Cisotto, D. D. Tegegn, S. L. Manzoni, I. Reguzzoni, E. Lotti, and I. Zoppis, “Variational autoencoder for early stress detection in smart agriculture: A pilot study,” in 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 2022, pp. 126–130.
• 6. J. Müller-Maatsch and S. M. van Ruth, “Handheld devices for food authentication and their applications: A review,” Foods, vol. 10, no. 12, p. 2901, 2021.
• 7. K. R. Borba, P. C. Spricigo, D. P. Aykas, M. C. Mitsuyuki, L. A. Colnago, and M. D. Ferreira, “Non-invasive quantification of vitamin C, citric acid, and sugar in Valência oranges using infrared spectroscopies,” Journal of Food Science and Technology, vol. 58, pp. 731–738, 2021.
• 8. J. A. Cayuela and C. Weiland, “Intact orange quality prediction with two portable NIR spectrometers,” Postharvest Biology and Technology, vol. 58, no. 2, pp. 113–120, 2010.
• 9. “Spectral Evolution NaturaSpec portable spectroradiometer,” https://spectralevolution.com/products/hardware/field-portable-spectroradiometers-for-remote-sensing/naturaspec-portable-spectroradiometer/, accessed: 2023-09-25.
• 10. J. Martins, R. Guerra, R. Pires, M. Antunes, T. Panagopoulos, A. Brázio, A. Afonso, L. Silva, M. Lucas, and A. Cavaco, “SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS-NIR spectroscopy,” Computers and Electronics in Agriculture, vol. 197, p. 106945, 2022.
• 11. Seletech Engineering Srl, “AI-empowered monitoring systems,” https://lnx.seletech.com/index.php/it/prodotti-e-progetti/intelligenza-artificiale/, accessed: 2023-09-25.
• 12. W. Suphamitmongkol, G. Nie, R. Liu, S. Kasemsumran, and Y. Shi, “An alternative approach for the classification of orange varieties based on near infrared spectroscopy,” Computers and electronics in agriculture, vol. 91, pp. 87–93, 2013.
• 13. J. A. Prananto, B. Minasny, and T. Weaver, “Near infrared (NIR) spectroscopy as a rapid and cost-effective method for nutrient analysis of plant leaf tissues,” Advances in agronomy, vol. 164, pp. 1–49, 2020.
• 14. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, pp. 1–27, 2011.
• 15. F. Cabitza and A. Campagner, “The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical ai studies,” p. 104510, 2021.
• 16. K. Choudhary, D. Wines, K. Li, K. F. Garrity, V. Gupta, A. H. Romero, J. T. Krogel, K. Saritas, A. Fuhr, P. Ganesh et al., “Jarvis-leaderboard: a large scale benchmark of materials design methods,” npj Computational Materials, vol. 10, no. 1, p. 93, 2024.
• 17. G. Cisotto, “Python code associated with this publication at colab,” https://colab.research.google.com/drive/14xU8u1Ao3smfnc_epTcIP9AS8etUjG8V?usp=sharing, last access: 2024-07-18.
• 18. G. Cisotto, D. D. Tegegn, I. Reguzzoni, and E. Lotti, “NIRS dataset associated with this publication,” https://github.com/CisottoGiulia/PON22-AI-NIRS-AgriFood.
• 19. A. M. Cavaco, R. Pires, M. D. Antunes, T. Panagopoulos, A. Brázio, A. M. Afonso, L. Silva, M. R. Lucas, B. Cadeiras, S. P. Cruz et al., “Validation of short wave near infrared calibration models for the quality and ripening of Newhall orange on tree across years and orchards,” Postharvest Biology and Technology, vol. 141, pp. 86–97, 2018.
• 20. A. Zancanaro, G. Cisotto, and L. Badia, “Modeling value of information in remote sensing from correlated sources,” Computer Communications, vol. 203, pp. 289–297, 2023.
• 21. P. Bertellini, G. D’Addese, G. Franchini, S. Parisi, C. Scribano, D. Zanirato, and M. Bertogna, “Binary classification of agricultural crops using sentinel satellite data and machine learning techniques,” in 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS). IEEE, 2023, pp. 859–864.
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Uwagi

1. This work was partially supported by the MUR under the grant “Dipartimenti di Eccellenza 2023-2027" of the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy. AZ is also supported by PON 2014-2020 action IV.4 and GC by action IV.6 funded by the MUR.

2. Thematic Sessions: Regular Papers

3. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).