Development of Deep Learning Based Application System for the Classification of Farm Related Ocular Disease in Benue State

Farm related ocular diseases constitute a major public health challenge among agricultural workers, particularly in developing regions where access to specialized eye care is limited. Ocular diseases such as cataract, glaucoma, and retinopathy are prevalent among farmers due to prolonged exposure to sunlight, dust, chemicals, and poor occupational safety practices. Despite the growing burden of these conditions, limited studies have explored the application of artificial intelligence–based diagnostic systems using localized data from Benue State, Nigeria. This study presents the development of a deep learning–based application system for the classification of farm related ocular diseases in Benue State. A total of 2,715 ocular images were collected from 85 subjects diagnosed with cataract, glaucoma, and retinopathy at Okida Eye Clinic, Otukpo. The dataset was augmented to improve class balance and diversity, and transfer learning was applied using a pre-trained AlexNet model with frozen convolutional layers. Model performance was evaluated using accuracy and loss metrics during training in a Python environment. The proposed model achieved a classification accuracy of 96.5% with loss values below 0.2, demonstrating strong learning capability and generalization. Comparative analysis with existing state-of-the-art models shows that the proposed approach performs competitively while benefiting from localized clinical data. The trained model was integrated into a software application and tested with real ocular images, yielding high confidence classification scores. The system is therefore recommended as a reliable decision-support tool for early detection and management of farm related ocular diseases in Benue State.

Ocular, AlexNet, Benue state, cataract, glaucoma, retinopathy,

1. Adelson J., Bourne R., Briant P., Flaxman S., Taylor H., &Jonas J., (2021) Causes of blindness and vision impairment in 2020 and trends over 30years, and prevalence of avoidable blindness in relation to VISION 2020: the right to sight: an analysis for the global burden of disease study. Lancet Glob Heal. 2021;9(2):e144–60 [Google Scholar] [Crossref]

2. Adeniyi M., Adewoye R., Popoola G., Aderukuola B., Ibrahim A., & Azees A., (2023) Awareness, prevalence and risk burden of cataract among adults in IDO/OSI local government area, a rural community of Ekiti state, southwest Nigeria. International Journal of Community Medicine and Public Health Sep;10(9):3077-3085 https://dx.doi.org/10.18203/2394-6040.ijcmph20232663 [Google Scholar] [Crossref]

3. Adepoju F., Monsudi K., &Adekoya B., (2014) Bilateral blindness from ocular injury: a 15 year review. Afr J Trauma. 2014;3(1):35–8 [Google Scholar] [Crossref]

4. Aghadoost D., (2014) Ocular trauma: an overview. Arch Trauma Res. 2014;3(2):e21639 [Google Scholar] [Crossref]

5. Alkhaldi, N.A.; Alabdulathim, R.E. Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images. Appl. Sci. 2024, 14, 7795. https://doi.org/ 10.3390/app14177795 [Google Scholar] [Crossref]

6. Alkhaldi, N.A.; Alabdulathim, R.E. Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images. Appl. Sci. 2024, 14, 7795. https://doi.org/ 10.3390/app14177795 [Google Scholar] [Crossref]

7. Babaqi.T, Jaradat. M, Yildirim. A, Al-Nimer. S, and Won. D, (2023), “Eye Disease Classification Using Deep Learning Techniques”, Proceedings of the IISE Annual Conference & Expo 2023 K. Babski-Reeves, B. Eksioglu, D. Hampton, eds, arXiv:2307.10501v1 [cs.CV]. [Google Scholar] [Crossref]

8. Bajwa G., (2018). An Approach to a Case of Ocular Trauma. 10.5005/jp/books/14137_11. [Google Scholar] [Crossref]

9. CHIDI, E. U., UDANOR, C. N., &ANOLIEFO, E. (2024). Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints. https://doi.org/10.20944/preprints202402.0583.v1 [Google Scholar] [Crossref]

10. Choksi N., Latorre A., Catalano S., Grivel A., Baldassari J., Pires J., Corvaro M., Silva Ogasawara M., Inforzato M., Habe P., Murata R., Stinchcombe S., Kolle S., Masinja W., Perjessy G., Daniel A., & Allen D., (2023) Retrospective Evaluation of the Eye Irritation Potential of Agrochemical Formulations. bioRxiv preprint doi: https://doi.org/10.1101/2023.06.23.543016 [Google Scholar] [Crossref]

11. Duke R, Lewallen S, &Courtright P., (2014) Estimated prevalence of monocular blindness and monocular severe visual impairment in children of Cross Rivers State, Nigeria. Niger J Ophthalmol. 2014;22(2):66. [Google Scholar] [Crossref]

12. Dung L., Hai P., Hoa L., Mai T., Hanh N., Than P., Tran V., Quyet N., Hai H., Ngoc D., Thu N., & Mai L., (2022) A case–control study of agricultural and behavioral factors associated with leptospirosis in Vietnam. BMC Infectious Diseases (2022) 22:583 https://doi.org/10.1186/s12879-022-07561-6 [Google Scholar] [Crossref]

13. Ebere Uzoka Chidi, E Anoliefo, C Udanor, AT Chijindu, LO Nwobodo (2025)” A Blind navigation guide model for obstacle avoidance using distance vision estimation based YOLO-V8n; Journal of the Nigerian Society of Physical Sciences, 2292-229; https://doi.org/10.46481/jnsps.2025.2292 [Google Scholar] [Crossref]

14. El-Shafei D., & Said R., (2023) Sun Safety: Knowledge and Behavior among Egyptian Farmers—a Multicomponent Intervention Study. Journal of Cancer Education (2023) 38:1042–1049 https://doi.org/10.1007/s13187-022-02230-3 [Google Scholar] [Crossref]

15. Emem A., Anietie E., Uwemedimbuk O., Akaninyene I., Emelwatitina, & Umoh A., (2021) Ocular Toxoplasmosis among Livestock Farmers and Raw Meat Handlers in Uyo, Nigeria. Ethiop J Health Sci. 2021;31 (2):257.doi: http://dx.doi.org/10.4314/ejhs.v31i2.8 [Google Scholar] [Crossref]

16. Ezinne N.,Ekemiri K., &Nwanali D., (2021) Occupational Ocular Injuries and Utilization of Eye Protective Devices among Sawmill Workers in the Ojo Local Government Area of Lagos State, Nigeria. Vision 2021, 5, 60. https://doi.org/10.3390/vision5040060 [Google Scholar] [Crossref]

17. Febriana S., Khalidah M., Huda F., Sutarni S., Mahayana I., Indrastuti N., Setyopranoto I., Waskito F., Prawiroranu S., Dwianingsih E., &Malueka R., (2023) Prevalence of pesticide related occupational diseases among Indonesian vegetable farmers – A collaborative work. Toxicology Reports 10 (2023) 571–579 https://doi.org/10.1016/j.toxrep.2023.04.016 [Google Scholar] [Crossref]

18. Feng, Z.; Xu, K.; Li, L.; Wang, Y. Hybrid Deep Learning Model for Cataract Diagnosis Assistance. Appl. Sci. 2024, 14, 11314. https://doi.org/10.3390/ app142311314 [Google Scholar] [Crossref]

19. Grassmann, F.; Mengelkamp, J.; Brandl, C.; Harsch, S.; Zimmermann, M.E.; Linkohr, B.; Peters, A.; Heid, I.M.; Palm, C.; Weber, B.H. Adeeplearning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 2018, 125, 1410–1420. [Google Scholar] [Crossref]

20. Guan H., Xue J., Ding Y., Zhang Y., Du K., & Yang J., (2023) Factors influencing cataract awareness and treatment attitudes among the middle-aged and older in western China’s rural areas. Front. Public Health 10:1045336. https://doi.org/10.3389/fpubh.2022.1045336 [Google Scholar] [Crossref]

21. Jac-Okereke C., Jac-Okereke A., Ezegwui I., & Umeh R., (2021) Current pattern of ocular trauma as seen in tertiary institutions in south-eastern Nigeria. BMC Ophthalmology (2021) 21:420 https://doi.org/10.1186/s12886-021-02162-4 [Google Scholar] [Crossref]

22. Jakob M., Santa D., Holte K., Sikkeland I., Hilt B., &Lundqvist P., (2021) Occupational Health and Safety in Agriculture – a brief report on organization, legislation and support in selected European countries. Ann Agric Environ Med. 2021; 28(4): 452–457. doi: 10.26444/aaem/140197 [Google Scholar] [Crossref]

23. Kekong P.E, Ajah I.A., Ebere U.C. (2019). Real-time drowsy driver monitoring and detection system using deep learning based behavioural approach. International Journal of Computer Sciences and Engineering 9 (1), 11-21 [Google Scholar] [Crossref]

24. Kulshrestha V.,&Mishra A., (2021)Occupational Eye Diseases and Injuries -A Cause for Concern. International Journal of MedicalScience and Clinical Invention8(5): 5414-5420, 2021DOI:10.18535/ijmsci/v8i05.08 [Google Scholar] [Crossref]

25. Kyari F., (2015) Challenges of agriculture-related eye injuries in Nigeria. Commu-nity Eye Heal J. 2015;28(91):52. [Google Scholar] [Crossref]

26. Mallick, S.; Saha, N.; Paul, J.; Ganguli, I.; Debnath, S.; Sil, J. An Efficient Deep Learning Framework for Glaucoma Diagnosis Using Convolution Mixed Transformer Network. In Proceedings of the TENCON 2023—2023 IEEE Region 10 Conference (TENCON), Chiang Mai, Thailand, 31 October–3 November 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 1111–1116. [Google Scholar] [Crossref]

27. Manassakorn, A.; Auethavekiat, S.; Sa-Ing, V.; Chansangpetch, S.; Ratanawongphaibul, K.; Uramphorn, N.; Tantisevi, V. GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture. IEEE Access 2022, 10, 95613–95622. [Google Scholar] [Crossref]

28. Mlowe G., Kavu;ikirwa O., Makundi I., Katakweba A., &Machangu R., (2023) Assessment of the Knowledge and Awareness of Leptospirosis among Households, Farmers, and Livestock Keepers in Unguja Island, Tanzania: A Cross-Sectional Study. https://doi.org/10.20944/preprints202304.0152.v1 [Google Scholar] [Crossref]

29. Modenese A., &Gobba F., (2019) Macular degeneration and occupational risk factors: a systematic review. International Archives of Occupational and Environmental Health (2019) 92:1–11 https://doi.org/10.1007/s00420-018-1355-y [Google Scholar] [Crossref]

30. Mohseni M, Blair K, Gurnani B,&Bragg B., (2023) Blunt Eye Trauma. [Updated 2023 Jun 11]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK470379/ [Google Scholar] [Crossref]

31. Mushtaq, G.; Siddiqui, F. Detection of diabetic retinopathy using deep learning methodology. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Tamil Nadu, India, 4–5 December 2020; IOP Publishing: London, UK, 2021; Volume 1070, p. 012049. [Google Scholar] [Crossref]

32. Musikawong S., Jampaklay A., Khamkhom N., Tadee R., Kerdmongkol A., Buckles L., Khachasin S., & Engblom A., (2021) Working and employment conditions in the agriculture sector in Thailand: A survey of migrants working on Thai sugarcane, rubber, oil palm and maize farms. International Labour Organization ISBN: 9789220344910 (print), 9789220344903 (web pdf) [Google Scholar] [Crossref]

33. Nawaz, M.; Nazir, T.; Javed, A.; Tariq, U.; Yong, H.-S.; Khan, M.A.; Cha, J. An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization. Sensors 2022, 22, 434. [Google Scholar] [Crossref]

34. Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., Ali, F., Kim, J., Kwon, H. & Hussain, A. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. Sensors. 21 (2021), https://www.mdpi.com/1424-8220/21/16/5283. [Google Scholar] [Crossref]

35. Oddone E., Taino G., Vita S., Schimd M., Frigerio F., & Imbriani M., (2019) Macular degeneration: peculiar sunlight exposure in an agricultural worker. Med Lav 2019; 110, 3: 241-245 DOI: 10.23749/mdl.v110i3.8125 [Google Scholar] [Crossref]

36. OnyekweluO.,Aribaba O., Musa K., Idowu O., Salami M.,&Odiaka Y., (2019) Ocular morbidity and utilisation of protective eyewear among carpenters in Mushin local government, Lagos, Nigeria. Niger Postgrad Med. J. 2019, 26, 199–204 [Google Scholar] [Crossref]

37. Prananda, A.R.; Frannita, E.L.; Hutami, A.H.T.; Maarif, M.R.; Fitriyani, N.L.; Syafrudin, M. Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment. Appl. Sci. 2023, 13, 37. https:// doi.org/10.3390/app13010037. [Google Scholar] [Crossref]

38. Shah M, Shah S, Agrawal R, &Patel K., (2018) Validation of a modifed Birmingham eye trauma terminology classifcation for mechanical eye injuries. Trauma. 2018;20(3):217–20. [Google Scholar] [Crossref]

39. Shukla, A.; Tiwari, S.; Jain, A. HybridFusionNet: Deep Learning for Multi-Stage Diabetic Retinopathy Detection. Technologies 2024, 12, 256. https://doi.org/10.3390/ technologies12120256 [Google Scholar] [Crossref]

40. Sochima V.E. Asogwa T.C., Lois O.N. Onuigbo C.M., Frank E.O., Ozor G.O., Ebere U.C. (2025)”; Comparing multi-control algorithms for complex nonlinear system: An embedded programmable logic control applications; DOI: http://doi.org/10.11591/ijpeds.v16.i1.pp212-224 [Google Scholar] [Crossref]

41. Sudhan, M.B.; Sinthuja, M.; Pravinth Raja, S.; Amutharaj, J.; Charlyn Pushpa Latha, G.; Sheeba Rachel, S.; Anitha, T.; Rajendran, T.; Waji, Y.A. Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. J. Healthc. Eng. 2022, 2022, 1601354. [Google Scholar] [Crossref]

42. Tashkandi, A. Eye Care: Predicting Eye Diseases Using Deep Learning Based on Retinal Images. Computation 2025, 13, 91. https://doi.org/10.3390/ computation13040091 [Google Scholar] [Crossref]

43. Thetkathuek A, Pornthip Y., Wanlop J., Patchana J., and PoonsakS.,(2017) Pesticide Exposure and Cholinesterase Levels in Migrant Farm Workers in Thailand. Journal of Agromedicine 22 (2): 118–30. [Google Scholar] [Crossref]

44. Weni, I.; Utomo, P.E.P.; Hutabarat, B.F.; Alfalah, M. Detection of cataract based on image features using convolutional neural networks. Indones. J. Comput. Cybern. Syst. 2021, 15, 75–86. [Google Scholar] [Crossref]

45. Yi, S.; Zhou, L.; Ma, L.; Shao, D. MTRA-CNN: A Multi-Scale Transfer Learning Framework for Glaucoma Classification in Retinal Fundus Images. IEEE Access 2023, 11, 142689–142701. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study