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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Blackcurrant Plantations Monitoring Using Drones

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DOI: http://dx.doi.org/10.15439/2025F8569

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 479–488 ()

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Abstract. The work is dedicated to the study of drone use in horticulture, focusing on an example of blackcurrant cultivation. The research aims to use drones to monitor vegetation in plantations and to maintain the technological environment of plants, using traditional agrotechnical field care methods. The concept offers mapping and instance segmentation followed by multi-label classification operations, taking into account the specifics of blackcurrant plantations. The mapping operation creates blackcurrant plantation maps from images taken by drones at low altitudes. This ensures the acquisition of highquality maps of large areas with the help of simple image photography cameras. Instance segmentation is intended for extracting singular leaf instances from mapped images, which are analyzed using classification methods to detect blackcurrant diseases, pest spread, nutrient and moisture deficiencies, and other plant vegetation-related parameters. Classification employs machine learning techniques and is specific to the cultivation of a particular plants -- blackcurrants. The proposed technology, with appropriate adjustments, can also be applied to the vegetation monitoring of other horticultural plants.

References

  1. A. Botta, P. Cavallone, L. Baglieri, G. Colucci, L. Tagliavini, and G. Quaglia, “A review of robots, perception, and tasks in precision agriculture”, Applied Mechanics 3, 830–854 (2022).
  2. A. Uzhinskiy, “Advanced technologies and artificial intelligence in agriculture”, AppliedMath 3, 799–813 (2023).
  3. J. D. B. Gil, P. Reidsma, K. Giller, L. Todman, A. Whitmore, and M. van Ittersum, “Sustainable development goal 2: improved targets and indicators for agriculture and food security”, Ambio 48, 685–698 (2019).
  4. P. De Castro, P. P. Miglietta, Y. Vecchio, et al., “The common agricultural policy 2021-2027: a new history for european agriculture”, Rivista Di Economia Agraria 75, 5–12 (2020).
  5. J. Xu, B. Gu, and G. Tian, “Review of agricultural iot technology”, Artificial Intelligence in Agriculture 6, 10–22 (2022).
  6. A. Rejeb, A. Abdollahi, K. Rejeb, and H. Treiblmaier, “Drones in agriculture: a review and bibliometric analysis”, Computers and Electronics in Agriculture 198, 107017 (2022).
  7. M. O. Ojo and A. Zahid, “Deep learning in controlled environment agriculture: a review of recent advancements, challenges and prospects”, Sensors 22, https://dx.doi.org/10.3390/s22207965 (2022).
  8. T. Wang, B. Chen, Z. Zhang, H. Li, and M. Zhang, “Applications of machine vision in agricultural robot navigation: a review”, Computers and Electronics in Agriculture 198, 107085 (2022).
  9. R. C. d. Oliveira and R. D. d. S. e. Silva, “Artificial intelligence in agriculture: benefits, challenges, and trends”, Applied Sciences 13, https://dx.doi.org/10.3390/app13137405 (2023).
  10. M. A. Dayıoğlu and U. Turker, “Digital transformation for sustainable future - agriculture 4.0: a review”, Journal of Agricultural Sciences 27, 373–399 (2021).
  11. R. Abbasi, P. Martinez, and R. Ahmad, “The digitization of agricultural industry – a systematic literature review on agriculture 4.0”, Smart Agricultural Technology 2, 100042 (2022).
  12. J. Huuskonen and T. Oksanen, “Soil sampling with drones and augmented reality in precision agriculture”, Computers and Electronics in Agriculture 154, 25–35 (2018).
  13. J. Zhou, Y. Xu, X. Gu, T. Chen, Q. Sun, S. Zhang, and Y. Pan, “High-precision mapping of soil organic matter based on uav imagery using machine learning algorithms”, Drones 7, https://dx.doi.org/10.3390/drones7050290 (2023).
  14. L. Bertalan, I. Holb, A. Pataki, G. Négyesi, G. Szabó, A. Kupásné Szalóki, and S. Szabó, “Uav-based multispectral and thermal cameras to predict soil water content – a machine learning approach”, Computers and Electronics in Agriculture 200, 107262 (2022).
  15. “Assessment of plant density for barley and wheat using uav multispectral imagery for high-throughput field phenotyping”, Computers and Electronics in Agriculture 189, 106380 (2021).
  16. J. C. O. Koh, M. Hayden, H. Daetwyler, and S. Kant, “Estimation of crop plant density at early mixed growth stages using uav imagery”, Plant Methods 15, 64 (2019).
  17. P. Ong, K. S. Teo, and C. K. Sia, “Uav-based weed detection in chinese cabbage using deep learning”, Smart Agricultural Technology 4, 100181 (2023).
  18. E. C. Tetila, B. B. Machado, G. Astolfi, N. A. de Souza Belete, W. P. Amorim, A. R. Roel, and H. Pistori, “Detection and classification of soybean pests using deep learning with uav images”, Computers and Electronics in Agriculture 179, 105836 (2020).
  19. N. A. Mohidem, N. N. Che’Ya, A. S. Juraimi, W. F. Fazlil Ilahi, M. H. Mohd Roslim, N. Sulaiman, M. Saberioon, and N. Mohd Noor, “How can unmanned aerial vehicles be used for detecting weeds in agricultural fields?”, Agriculture 11, https://dx.doi.org/10.3390/agriculture11101004 (2021).
  20. C. Kumar, P. Mubvumba, Y. Huang, J. Dhillon, and K. Reddy, “Multi-stage corn yield prediction using highresolution uav multispectral data and machine learning models”, Agronomy 13, https://dx.doi.org/10.3390/agronomy13051277 (2023).
  21. L. Zeng, G. Peng, R. Meng, J. Man, W. Li, B. Xu, Z. Lv, and R. Sun, “Wheat yield prediction based on unmanned aerial vehicles-collected red–green–blue imagery”, Remote Sensing 13, https://dx.doi.org/10.3390/rs13152937 (2021).
  22. T. B. Shahi, C.-Y. Xu, A. Neupane, D. B. Fleischfresser, D. J. O’Connor, G. C. Wright, and W. Guo, “Peanut yield prediction with uav multispectral imagery using a cooperative machine learning approach”, Electronic Research Archive 31, 3343–3361 (2023).
  23. P. Chen, F. Ouyang, Y. Zhang, and Y. Lan, “Preliminary evaluation of spraying quality of multi-unmanned aerial vehicle (uav) close formation spraying”, Agriculture 12, 10.3390/agriculture12081149 (2022).
  24. C. Song, L. Liu, G. Wang, J. Han, T. Zhang, and Y. Lan, “Particle deposition distribution of multi-rotor uav-based fertilizer spreader under different height and speed parameters”, Drones 7, https://dx.doi.org/10.3390/drones7070425 (2023).
  25. D. Su, W. Yao, F. Yu, Y. Liu, Z. Zheng, Y. Wang, T. Xu, and C. Chen, “Single-neuron pid uav variable fertilizer application control system based on a weighted coefficient learning correction”, Agriculture 12, https://dx.doi.org/10.3390/agriculture12071019 (2022).
  26. K. Anand and R. Goutam, “An autonomous uav for pesticide spraying”, Int. J. Trend Sci. Res. Dev 3, 986–990 (2019).
  27. S. Ivić, A. Andrejčuk, and S. Družeta, “Autonomous control for multi-agent non-uniform spraying”, Applied Soft Computing 80, 742–760 (2019).
  28. J. Sinha, “Aerial robot for smart farming and enhancing farmers’ net benefit”, in (ICAR, 2020).
  29. M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, and R. Kaliaperumal, “Smart farming: internet of things (iot)-based sustainable agriculture”, Agriculture 12, 10.3390/agriculture12101745 (2022).
  30. A. Madushanki, M. Halgamuge, W. Wirasagoda, and A. Syed, “Adoption of the internet of things (iot) in agriculture and smart farming towards urban greening: a review”, English, International Journal of Advanced Computer Science and Applications 10, 11–28 (2019).
  31. G. Bilotta, E. Genovese, R. Citroni, F. Cotroneo, G. M. Meduri, and V. Barrile, “Integration of an innovative atmospheric forecasting simulator and remote sensing data into a geographical information system in the frame of agriculture 4.0 concept”, AgriEngineering 5, 1280–1301 (2023).
  32. K. M. Hosny, W. M. El-Hady, F. M. Samy, E. Vrochidou, and G. A. Papakostas, “Multi-class classification