Artificial intelligence-integrated drones used for detection of live wild boars, wild boar carcasses and remnants in the context of African swine fever control

Introduction. Effective measures for African swine fever outbreak prevention and early detection are required in view of global spread of African swine fever, fatal viral hemorrhagic disease of domestic pigs and wild boars. Wild boar population managing and search for the wild boars died of African swine fever and being the virus source are considered priority measures for the disease control in wildlife.

Objective. Generalization of currently available knowledge about advanced technologies for the use of unmanned aerial vehicles (drones) in combination with artificial intelligence-based methods in the wild.

Materials and methods. Analytical research methods including search in the following databases were used: PubMed, Springer, Wiley Online Library, Google Scholar, CrossRef, Russian Science Citation Index (RSCI), еLIBRARY, CyberLeninka.

Results. Potential of using unmanned aerial vehicles (drones) and artificial intelligence (neural network) for detection of wild boars and their remnants in the context of combating African swine fever is described in the review. The role of wild boars in the disease spread and the need for wild boar population regulation are discussed in detail. Also, the importance of timely wild boar carcass removal and use of modern technologies for wild boar population recording and its density estimation are underlined. Data on the use of drones equipped with various technical devices for study of large animal populations in the wild are analyzed, advantages and peculiarities of unmanned aerial vehicle use are indicated. Experience gained in using neural networks-based techniques for automatic processing of animal images acquired from drones is also summarized.

Conclusion. Artificial intelligence-integrated unmanned aerial vehicles appear to be a key tool for managing wild boar populations and the rapid detection of African swine fever dead wild boars that allows improvement of overall effectiveness of the measures taken against this disease.

1. EFSA, Boklund A. E., Ståhl K., Miranda Chueca M. Á., Podgórski T., Vergne T., et al. Risk and protective factors for ASF in domestic pigs and wild boar in the EU, and mitigation measures for managing the disease in wild boar. EFSA Journal. 2024; 22 (12):e9095. https://doi.org/10.2903/j.efsa.2024.9095

2. Pejsak Z., Truszczyński M., Niemczuk K., Kozak E., Markowska-Daniel I. Epidemiology of African swine fever in Poland since the detection of the first case. Polish Journal of Veterinary Sciences. 2014; 17 (4): 665–672. https://doi.org/10.2478/pjvs-2014-0097

3. Oļševskis E., Guberti V., Seržants M., Westergaard J., Gallardo C., Rodze I., Depner K. African swine fever virus introduction into the EU in 2014: Experience of Latvia. Research in Veterinary Science. 2016; 105: 28–30. https://doi.org/10.1016/j.rvsc.2016.01.006

4. Nurmoja I., Schulz K., Staubach C., Sauter-Louis C., Depner K., Conraths F. J., Viltrop A. Development of African swine fever epidemic among wild boar in Estonia – two different areas in the epidemiological focus. Scientific Reports. 2017; 7:12562. https://doi.org/10.1038/s41598-017-12952-w

5. Sauter-Louis C., Forth J. H., Probst C., Staubach C., Hlinak A., Rudovsky A., et al. Joining the club: First detection of African swine fever in wild boar in Germany. Transboundary and Emerging Diseases. 2021; 68 (4): 1744–1752. https://doi.org/10.1111/tbed.13890

6. Sauter-Louis C., Conraths F. J., Probst C., Blohm U., Schulz K., Sehl J., et al. African swine fever in wild boar in Europe – A review. Viruses. 2021; 13 (9):1717. https://doi.org/10.3390/v13091717

7. Chenais E., Ståhl K., Guberti V., Depner K. Identification of wild boar-habitat epidemiologic cycle in African swine fever epizootic. Emerging Infectious Diseases. 2018; 24 (4): 810–812. https://doi.org/10.3201/eid2404.172127

8. Probst C., Globig A., Knoll B., Conraths F. J., Depner K. Behaviour of free ranging wild boar towards their dead fellows: Potential implications for the transmission of African swine fever. Royal Society Open Science. 2017; 4 (5):170054. https://doi.org/10.1098/rsos.170054

9. Fischer M., Hühr J., Blome S., Conraths F. J., Probst C. Stability of African swine fever virus in carcasses of domestic pigs and wild boar experimentally infected with the ASFV “Estonia 2014” isolate. Viruses. 2020; 12 (10):1118. https://doi.org/10.3390/v12101118

10. EFSA Panel on Animal Health and Welfare. Scientific opinion on African swine fever. EFSA Journal. 2015; 13 (7):4163. https://doi.org/10.2903/j.efsa.2015.4163

11. Gonzalez L. F., Montes G. A., Puig E., Johnson S., Mengersen K., Gaston K. J. Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors. 2016; 16 (1):97. https://doi.org/10.3390/s16010097

12. Schad L., Fischer J. Opportunities and risks in the use of drones for studying animal behavior. Methods in Ecology and Evolution. 2023; 14 (8): 1864–1872. https://doi.org/10.1111/2041-210x.13922

13. Petso T., Jamisola R. S. Jr. Wildlife conservation using drones and artificial intelligence in Africa. Science Robotics. 2023; 8 (85):eadm7008. https://doi.org/10.1126/scirobotics.adm7008

14. Hodgson J. C., Baylis S. M., Mott R., Herrod A., Clarke R. H. Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports. 2016; 6:22574. https://doi.org/10.1038/srep22574

15. Marchowski D. Drones, automatic counting tools, and artificial neural networks in wildlife population censusing. Ecology and Evolution. 2021; 11 (22): 16214–16227. https://doi.org/10.1002/ece3.8302

16. Demmer C. R., Demmer S., McIntyre T. Drones as a tool to study and monitor endangered grey crowned cranes (Balearica regulorum): Behavioural responses and recommended guidelines. Ecology and Evolution. 2024; 4 (2):e10990. https://doi.org/10.1002/ece3.10990

17. Lenzi J., Barnas A. F., ElSaid A. A., Desell T., Rockwell R. F., Ellis-Felege S. N. Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys. Scientific Reports. 2023; 13:947. https://doi.org/10.1038/s41598-023-28240-9

18. Brickson L., Zhang L., Vollrath F., Douglas-Hamilton I., Titus A. J. Elephants and algorithms: a review of the current and future role of AI in elephant monitoring. Journal of the Royal Society. 2023; 20 (208):20230367. https://doi.org/10.1098/rsif.2023.0367

19. Witczuk J., Pagacz S., Zmarz A., Cypel M. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests – preliminary results. International Journal of Remote Sensing. 2018; 39 (15–16): 5504–5521. http://doi.org/10.1080/01431161.2017.1390621

20. Linchant J., Lhoest S., Quevauvillers S., Lejeune P., Vermeulen C., Semeki Ngabinzeke J., et al. UAS imagery reveals new survey opportunities for counting hippos. PLoS ONE. 2018; 13 (11):e0206413. https://doi.org/10.1371/journal.pone.0206413

21. Semel B. P., Karpanty S. M., Vololonirina F. F., Rakotonanahary A. N. Eyes in the sky: Assessing the feasibility of low-cost, ready-to-use unmanned aerial vehicles to monitor primate populations directly. Folia Primatologica. 2019; 91 (1): 69–82. https://doi.org/10.1159/000496971

22. Baldwin R. W., Beaver J. T., Messinger M., Muday J., Windsor M., Larsen G. D., et al. Camera trap methods and drone thermal surveillance provide reliable, comparable density estimates of large, free-ranging ungulates. Animals. 2023; 13 (11):1884. https://doi.org/10.3390/ani13111884

23. Krishnan B. S., Jones L. R., Elmore J. A., Samiappan S., Evans K. O., Pfeiffer M. B., et al. Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys. Scientific Reports. 2023; 13:10385. https://doi.org/10.1038/s41598-023-37295-7

24. Corcoran E., Denman S., Hamilton G. Evaluating new technology for biodiversity monitoring: Are drone surveys biased? Ecology and Evolution. 2021; 11 (11): 6649–6656. https://doi.org/10.1002/ece3.7518

25. Hvala A., Rogers R. M., Alazab M., Campbell H. A. Supplementing aerial drone surveys with biotelemetry data validates wildlife detection probabilities. Frontiers in Conservation Science. 2023; 4:1203736. https://doi.org/10.3389/fcosc.2023.1203736

26. Tack J. Wild boar (Sus scrofa) populations in Europe: A scientific review of population trends and implications for management. Brussels: European Landowners’ Organization; 2018. 56 p. https://wildbeimwild.com/wp-content/uploads/2023/08/12-Tack-J-Wild-Boar-Population-Trends-inEurope-2018.pdf

27. Carpio A. J., Apollonio M., Acevedo P. Wild ungulate overabundance in Europe: contexts, causes, monitoring and management recommendations. Mammal Review. 2021; 51 (1): 95–108. https://doi.org/10.1111/mam.12221

28. Chenais E., Depner K., Guberti V., Dietze K., Viltrop A., Ståhl K. Epidemiological considerations on African swine fever in Europe 2014–2018. Porcine Health Management. 2019; 5:6. https://doi.org/10.1186/s40813018-0109-2

29. Podgórski T., Borowik T., Łyjak M., Woźniakowski G. Spatial epidemiology of African swine fever: Host, landscape and anthropogenic drivers of disease occurrence in wild boar. Preventive Veterinary Medicine. 2020; 177:104691. https://doi.org/10.1016/j.prevetmed.2019.104691

30. Śmietanka K., Woźniakowski G., Kozak E., Niemczuk K., Frączyk M., Bocian Ł., et al. African swine fever epidemic, Poland, 2014–2015. Emerging Infectious Diseases. 2016; 22 (7): 1201–1207. https://doi.org/10.3201/eid2207.151708

31. Boklund A., Dhollander S., Chesnoiu Vasile T., Abrahantes J. C., Bøtner A., Gogin A., et al. Risk factors for African swine fever incursion in Romanian domestic farms during 2019. Scientific Reports. 2020; 10:10215. https://doi.org/10.1038/s41598-020-66381-3

32. Johann F., Handschuh M., Linderoth P., Dormann C. F., Arnold J. Adaptation of wild boar (Sus scrofa) activity in a human-dominated landscape. BMC Ecology. 2020; 20:4. https://doi.org/10.1186/s12898-019-0271-7

33. Cukor J., Faltusová M., Vacek Z., Linda R., Skoták V., Václavek P., et al. Wild boar carcasses in the center of boar activity: crucial risks of ASF transmission. Frontiers in Veterinary Science. 2024; 11:1497361. https://doi.org/10.3389/fvets.2024.1497361

34. Morelle K., Jezek M., Licoppe A., Podgorski T. Deathbed choice by ASF‐infected wild boar can help find carcasses. Transboundary and Emerging Diseases. 2019; 66 (5): 1821–1826. https://doi.org/10.1111/tbed.13267

35. Cukor J., Linda R., Václavek P., Šatrán P., Mahlerová K., Vacek Z., et al. Wild boar deathbed choice in relation to ASF: Are there any differences between positive and negative carcasses? Preventive Veterinary Medicine. 2020; 177:104943. https://doi.org/10.1016/j.prevetmed.2020.104943

36. Rogoll L., Schulz K., Staubach C., Oļševskis E., Seržants M., Lamberga K., et al. Identification of predilection sites for wild boar carcass search based on spatial analysis of Latvian ASF surveillance data. Scientific Reports. 2024; 14:382. https://doi.org/10.1038/s41598-023-50477-7

37. Allepuz А., Hovari M., Masiulis M., Ciaravino G., Beltrán-Alcrudo D. Targeting the search of African swine fever-infected wild boar carcasses: A tool for early detection. Transboundary and Emerging Diseases. 2022; 69 (5): e1682–e1692. https://doi.org/10.1111/tbed.14504

38. Coelho I. M. P., Paiva M. T., da Costa A. J. A., Nicolino R. R. African swine fever: spread and seasonal patterns worldwide. Preventive Veterinary Medicine. 2025; 235:106401. https://doi.org/10.1016/j.prevetmed.2024.106401

39. Zakharova O. I., Blokhin A. A., Burova O. A., Yashin I. V., Korennoy F. I. Risk factors for African swine fever spread in wild boar in the Russian Federation. Veterinary Science Today. 2024; 13 (1): 64–72. https://doi.org/10.29326/2304-196X-2024-13-1-64-72

40. Probst C., Gethmann J., Amler S., Globig A., Knoll B., Conraths F. J. The potential role of scavengers in spreading African swine fever among wild boar. Scientific Reports. 2019; 9:11450. https://doi.org/10.1038/s41598019-47623-5

41. Nuanualsuwan S., Songkasupa T., Boonpornprasert P., Suwankitwat N., Lohlamoh W., Nuengjamnong C. Persistence of African swine fever virus on porous and non-porous fomites at environmental temperatures. Porcine Health Management. 2022; 8:34. https://doi.org/10.1186/s40813022-00277-8

42. Tummeleht L., Häkkä S. S. S., Jürison M., Vilem A., Nurmoja I., Viltrop A. Wild boar (Sus scrofa) carcasses as an attraction for scavengers and a potential source for soil contamination with the African swine fever virus. Frontiers in Veterinary Science. 2024; 11:1305643. https://doi.org/10.3389/fvets.2024.1305643

43. Probst C., Gethmann J., Amendt J., Lutz L., Teifke J. P., Conraths F. J. Estimating the postmortem interval of wild boar carcasses. Veterinary Sciences. 2020; 7 (1):6. https://doi.org/10.3390/vetsci7010006

44. Pepin K. M., Golnar A. J., Abdo Z., Podgórski T. Ecological drivers of African swine fever virus persistence in wild boar populations: insight for control. Ecology and Evolution. 2020; 10 (6): 2846–2859. https://doi.org/10.1002/ece3.6100

45. Davies K., Goatley L. C., Guinat C., Netherton C. L., Gubbins S., Dixon L. K., Reis A. L. Survival of African swine fever virus in excretions from pigs experimentally infected with the Georgia 2007/1 isolate. Transboundary and Emerging Diseases. 2017; 64 (2): 425–431. https://doi.org/10.1111/tbed.12381

46. Prodelalova J., Kavanova L., Salat J., Moutelikova R., Kobzova S., Krasna M., et al. Experimental evidence of the long-term survival of infective African swine fever virus strain Ba71V in soil under different conditions. Pathogens. 2022; 11 (6):648. https://doi.org/10.3390/pathogens11060648

47. Carlson J., Fischer M., Zani L., Eschbaumer M., Fuchs W., Mettenleiter T., et al. Stability of African swine fever virus in soil and options to mitigate the potential transmission risk. Pathogens. 2020; 9 (11):977. https://doi.org/10.3390/pathogens9110977

48. Mazur-Panasiuk N., Woźniakowski G. Natural inactivation of African swine fever virus in tissues: Influence of temperature and environmental conditions on virus survival. Veterinary Microbiology. 2020; 242:108609. https://doi.org/10.1016/j.vetmic.2020.108609

49. Blokhin A. A., Burova O. A., Toropova N. N., Zakharova O. I., Iashin I. V., Korennoy F. I. Monitoring ASF in wildlife: virus survival in wild boar carcasses and disinfection methods (review). Veterinariya. 2022; (3): 14–21. https:// doi.org/10.30896/0042-4846.2022.25.3.14-21 (in Russ.)

50. Merta D., Mocała P., Pomykacz M., Frąckowiak W. Autumn-winter diet and fat reserves of wild boars (Sus scrofa) inhabiting forest and forest-farmland environment in south-western Poland. Folia Zoologica. 2014; 63 (2): 95–102. https://doi.org/10.25225/fozo.v63.i2.a7.2014

51. Cukor J., Linda R., Václavek P., Mahlerová K., Šatrán P., Havránek F. Confirmed cannibalism in wild boar and its possible role in African swine fever transmission. Transboundary and Emerging Diseases. 2020; 67 (3): 1068–1073. https://doi.org/10.1111/tbed.13468

52. Sánchez-Cordón P. J., Lean F. Z. X., Batten C., Steinbach F., Neimanis A., Le Potier M. F., et al. Comparative evaluation of disease dynamics in wild boar and domestic pigs experimentally inoculated intranasally with the European highly virulent African swine fever virus genotype II strain “Armenia 2007”. Vet­erinary Research. 2024; 55:89. https://doi.org/10.1186/s13567-024-01343-5

53. Rietz J., Ischebeck S., Conraths F. J., Probst C., Zedrosser A., Fiderer C., et al. Scavenger-induced scattering of wild boar carcasses over large distances and its implications for disease management. Journal of Environmental Management. 2024; 365:121554. https://doi.org/10.1016/j.jenvman.2024.121554

54. Hyun C.-U., Park M., Lee W. Y. Remotely piloted aircraft system (RPAS)-based wildlife detection: a review and case studies in maritime Antarctica. Animals. 2020; 10 (12):2387. https://doi.org/10.3390/ani10122387

55. Prosekov A. Yu. Characteristics and key limitations of traditional methods for accounting hunting animals and digital technologies for solving the existing problems (review). Agricultural Science Euro­North­East. 2020; 21 (4): 341–354. https://doi.org/10.30766/2072-9081.2020.21.4.341-354 (in Russ.)

56. Prosekov A., Kuznetsov A., Rada A., Ivanova S. Methods for monitoring large terrestrial animals in the wild. Forests. 2020; 11 (8):808. https://doi.org/10.3390/f11080808

57. Tubis A. A., Poturaj H., Dereń K., Żurek A. Risks of drone use in light of literature studies. Sensors. 2024; 24 (4):1205. https://doi.org/10.3390/s24041205

58. Ivanova S., Prosekov A. Hunting resource management by population size control by remote sensing using an unmanned aerial vehicle. Nature Environment and Pollution Technology. 2024; 23 (1): 391–399. https://doi.org/10.46488/NEPT.2024.v23i01.033

59. Lee M. J., Voss S. C., Franklin D., Dadour I. R. Preliminary investigation of aircraft mounted thermal imaging to locate decomposing remains via the heat produced by larval aggregations. Forensic Science International. 2018; 289: 175–185. https://doi.org/10.1016/j.forsciint.2018.05.028

60. Butters O., Krosch M. N., Roberts M., MacGregor D. Application of forward-looking infrared (FLIR) imaging from an unmanned aerial platform in the search for decomposing remains. Journal of Forensic Sciences. 2021; 66 (1): 347–355. https://doi.org/10.1111/1556-4029.14581

61. Peksa J., Mamchur D. A review on the state of the art in copter drones and flight control systems. Sensors. 2024; 24 (11):3349. https://doi.org/10.3390/s24113349

62. Askari M., Benciolini M., Phan H. V., Stewart W., Ijspeert A. J., Floreano D. Crash-perching on vertical poles with a hugging-wing robot. Communications Engineering. 2024; 3:98. https://doi.org/10.1038/s44172024-00241-0

63. Mechan F., Bartonicek Z., Malone D., Lees R. S. Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector- borne diseases. Malaria Journal. 2023; 22:23. https://doi.org/10.1186/s12936-022-04414-0

64. Longmore S. N., Collins R. P., Pfeifer S., Fox S. E., Mulero-Pázmány M., Bezombes F., et al. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. International Journal of Remote Sensing. 2017; 38 (8–10): 2623–2638. https://doi.org/10.1080/01431161.2017.1280639

65. Prosekov A., Vesnina A., Atuchin V., Kuznetsov A. Robust algorithms for drone-assisted monitoring of big animals in harsh conditions of Siberian winter forests: recovery of European elk (Alces alces) in Salair mountains. Animals. 2022; 12 (12):1483. https://doi.org/10.3390/ani12121483

66. Zhou M., Elmore J. A., Samiappan S., Evans K. O., Pfeiffer M. B., Blackwell B. F., Iglay R. B. Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks. Sensors. 2021; 21 (17):5697. https://doi.org/10.3390/s21175697

67. Samiappan S., Krishnan B. S., Dehart D., Jones L. R., Elmore J. A., Evans K. O., Iglay R. B. Aerial wildlife image repository for animal monitoring with drones in the age of artificial intelligence. Database. 2024; 2024:baae070. https://doi.org/10.1093/database/baae070