Developing a machine learning prediction model for honey production

Abstract

Türkiye, with its rich flora diversity, holds a significant share in global honey production. However, honey bee populations, essential for agricultural ecosystems, face multifaceted threats such as climate change, habitat degradation, diseases, parasites, and exposure to pesticides. Alongside the increasing global food demand driven by population growth, there is a pressing need for a substantial increase in honey production. In this context, advances in machine learning algorithms offer tools to predict future food needs and production levels. The objective of this work is to develop a predictive model using machine learning techniques to predict Türkiye's honey output in the next years. To achieve this goal, a range of machine learning algorithms including K-Nearest Neighbor, Random Forest, Linear Regression, and Gaussian Naive Bayes were employed. Following investigations, Linear Regression emerged as the most effective method for predicting honey production levels (R2= 0.97).

Keywords

• Honey bee
• Honey
• Production estimate
• Artificial intelligence

References

1. Abay Z, Bezabeh A, Gela, A, Tassew A (2023) Evaluating the Impact of Commonly Used Pesticides on Honeybees (Apis mellifera) in North Gonder of Amhara Region, Ethiopia. Journal of Toxicology 2023: 2634158. doi: 10.1155/2023/2634158.
2. Ahmed MU, Hussain I (2022) Prediction of wheat production using machine learning algorithms in northern areas of pakistan. Telecommunications Policy 46: 102370. https://doi.org/10.1016/j.telpol.2022.102370.
3. Alqarni AS, Iqbal J, Raweh HS, Hassan A, Owayss AA (2021) Beekeeping in the Desert: Foraging activities of honey bee during major honeyflow in a hot-arid ecosystem. Applied Science 11: 9756. doi: 10.3390/app11209756.
4. Atanasov AZ, Georgiev SG, Vulkov LG (2023) Parameter estimation analysis in a model of honey production. Axioms 12: 214. doi: 10.3390/axioms12020214.
5. Braga AR, Freitas BM, Gomes DG, Bezerra AD, Cazier JA (2021) Forecasting sudden drops of temperature in pre-overwintering honeybee colonies. Biosystems Engineering 209: 315-321. doi: 10.1016/j.biosystemseng.2021.07.009.
6. Breiman L (2001) Random forests. Machine Learning 45: 5-32. doi: 10.1023/A:1010933404324.
7. Brown MJ, Dicks LV, Paxton RJ, Baldock KC, Barron AB, Chauzat MP, Freitas BM, Goulson D, Jepsen S, Kremen C, Li J, Neumann P, Pattemore DE, Potts SG, Schweiger O, Seymour CL, Stout JC (2016) A horizon scan of future threats and opportunities for pollinators and pollination. PeerJ 4: e2249. doi: 10.7717/peerj.2249.
8. Burucu V, Gülse Bal HS (2017) Türkiye’de arıcılığın mevcut durumu ve bal üretim öngörüsü. Tarım Ekonomisi Araştırmaları Dergisi 3: 28-37.

9. Calovi M, Grozinger CM, Miller DA, Goslee SC (2021) Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States. Scientific Repeports 11: 1553. doi: 10.1038/s41598-021-81051-8.
10. Campbell T, Dixon KW, Dods K, Fearns P, Handcock R (2020) Machine learning regression model for predicting honey harvests. Agriculture 10: 118. doi: 10.3390/agriculture10040118.
11. Clarke D, Robert D (2018) Predictive modelling of honey bee foraging activity using local weather conditions. Apidologie 49: 386-396. doi: 10.1007/s13592-018-0565-3.
12. Coşkun A (2019) Türkiye’de bal sektörünün mevcut durum değerlendirilmesi ve tüketici eğilimleri. Yüksek Lisans Tezi, Namık Kemal University, Tekirdağ.
13. Çukur T, Çukur F (2021) ARIMA modeli ile Türkiye bal üretim öngörüsü. Tarım Ekonomisi Araştırmaları Dergisi 7(1): 31-39.
14. Fatima N, Mujtaba G, Akhunzada A, Ali K, Shaikh ZH, Rabia B, Shahani JA (2022) Prediction of Pakistani honey authenticity through machine learning. IEEE Access 10: 87508-87521.
15. Ferreira PA, Boscolo D, Carvalheiro LG, Biesmeijer JC, Rocha PL, Viana BF (2015) Responses of bees to habitat loss in fragmented landscapes of Brazilian Atlantic Rainforest. Landscape Ecology 30: 2067-2078.
16. Gültepe Y (2019) Makine öğrenmesi algoritmaları ile hava kirliliği tahmini üzerine karşılaştırmalı bir değerlendirme. Avrupa Bilim ve Teknoloji Dergisi 16: 8-15. doi: 10.31590/ejosat.530347.
17. Güngör E, Ayhan A (2016) Bartın yöresi orman kaynaklarının bal üretim potansiyeli ve ekonomik değeri. Turkish Journal of Forestry 17: 108-116. doi: 10.18182/tjf.89126.
18. Hai A, Bharath G, Patah MFA, Daud WMAW, Rambabu K, Show P, Banat F (2023) Machine learning models for the prediction of total yield and specific surface area of biochar derived from agricultural biomass by pyrolysis. Environmental Technology & Innovation 30: 103071. doi: 10.1016/j.eti.2023.103071.
19. John GH, Langley P (2013) Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964. doi: 10.48550/arXiv.1302.4964.
20. Karaağaç S, Bulut İ (2023) Antalya’daki bal ormanlarının dağılımı ve sürdürülebilir yönetimi (2010-2022). Akdeniz Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 13: 56-73.
21. Klein AM, Vaissiere BE, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T (2007) Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society B: Biological Sciences 274: 303-313. doi: 10.1098/rspb.2006.3721.
22. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23: 89-109. doi: 10.1016/s0933-3657(01)00077-x.
23. Maulud D, Abdulazeez AM (2020) A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends 1: 140-147. doi: 10.38094/jastt1457.
24. Naseri Z, Saner G, Adanacıoğlu H (2016) The Future Trends of Honey Supply and Demand in Turkey (OR-70). In: 5th International Mugla Beekeeping and Pine Honey Congress, Healthy Bees-Healthy Life. Mugla, Türkiye, pp. 200-201.
25. Niazian M, Niedbala G (2020) Machine learning for plant breeding and biotechnology. Agriculture 10: 436. doi: 10.3390/agriculture10100436.
26. Olate-Olave VR, Verde M, Vallejos L, Perez Raymonda L, Cortese MC, Doorn M (2021) Bee health and productivity in apis mellifera, a consequence of multiple factors. Veterinary Science 8: 76. doi: 10.3390/vetsci8050076.
27. Oroian M, Ropciuc S, Paduret S, Sanduleac ET (2017) Authentication of Romanian honeys based on physicochemical properties, texture and chemometric. Journal of Food Science and Technology 54: 4240-4250. doi: 10.1007/s13197-017-2893-0.
28. Pătruică S, Pet I, Simiz E (2021) Beekeeping in the context of climate change. Scientific Papers. Series D. Animal Science 64: 2393-2260.
29. Potts SG, Imperatriz-Fonseca V, Ngo HT, Aizen MA, Biesmeijer JC, Breeze TD, Dicks LV, Garibaldi LA, Hill R, Settele J, Vanbergen AJ (2016) Safeguarding pollinators and their values to human well-being. Nature 540: 220-229. doi: 10.1038/nature20588.
30. Prešern J, Smodiš Škerl MI (2019) Parameters influencing queen body mass and their importance as determined by machine learning in honey bees (Apis mellifera carnica). Apidologie 50: 745-757. doi: 10.1007/s13592-019-00683-y.
31. Rahman LF, Marufuzzaman M, Alam L, Bari MA, Sumaila UR, Sidek LM (2021) Developing an ensembled machine learning prediction model for marine fish and aquaculture production. Sustainability 13: 9124. doi: 10.3390/su13169124.
32. Şengül Z, Yücel B, Saner G, Takma Ç (2023) Investigating the Impact of Climate Parameters on Honey Yield under Migratory Beekeeping Conditions through Decision Tree Analysis: The Case of İzmir Province. ANADOLU Ege Tarımsal Araştırma Enstitüsü Dergisi 33: 268-280. doi: 10.18615/anadolu.1394787.
33. Shoemaker KT, Heffelfinger LJ, Jackson NJ, Blum ME, Wasley T, Stewart KM (2018) A machine-learning approach for extending classical wildlife resource selection analyses. Ecology and Evolution 8: 3556-3569. doi: 10.1002/ece3.3936.
34. Switanek M, Brodschneider R, Crailsheim K, Truhetz H (2015) Impacts of austrian climate variability on honey bee mortality In: EGU General Assembly Conference. Vienna, 17, 9575.
35. TÜİK (2023) Nüfus projeksiyonları. https://data.tuik.gov.tr/Bulten/Index?p=Nufus-Projeksiyonlari-2018-2080-30567. Accessed 22 March, 2024.
36. Van Dijk M, Morley T, Rau ML, Saghai Y (2021) A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Natur Food 2: 494-501. doi: 10.1038/s43016-021-00322-9.
37. Varol E, Yücel B (2019) The effects of environmental problems on honey bees in view of sustainable life. Mellifera 19: 23-32.
38. Veiner M, Morimoto J, Leadbeater E, Manfredini F (2022) Machine learning models identify gene predictors of waggle dance behaviour in honeybees. Molecular Ecology Resources 22: 2248-2261. doi: 10.1111/1755-0998.13611.