The week's pick
Random Articles
Reseach Article
Low-Cost Smartphone-based Plant Disease Diagnosis for Zimbabwean Farmers using Transfer Learning and Crowdsourced Image Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 32 |
| Year of Publication: 2025 |
| Authors: Panashe Brian Mhembere |
10.5120/ijca2025925588
|
Panashe Brian Mhembere . Low-Cost Smartphone-based Plant Disease Diagnosis for Zimbabwean Farmers using Transfer Learning and Crowdsourced Image Data. International Journal of Computer Applications. 187, 32 ( Aug 2025), 56-64. DOI=10.5120/ijca2025925588
Abstract
Timely and accurate identification of crop diseases is vital for improving food security and farmer livelihoods, particularly in low-resource agricultural settings. This study presents a low-cost, smartphone-compatible plant disease diagnosis system designed specifically for Zimbabwean farmers. The system integrates transfer learning with the MobileNetV2 architecture and leverages a hybrid dataset composed of curated PlantVillage images and 400 crowdsourced leaf images collected from smallholder farmers in Zimbabwe. Following preprocessing and augmentation, the data was used to train a lightweight convolutional neural network via a two-stage transfer learning approach. The model achieved a test accuracy of 91.0%, with strong precision, recall, and F1-scores across six classes. A web-based prototype was developed using Streamlit and deployed via Ngrok, allowing real-time disease prediction through browser-based image uploads, simulating field use on mobile devices. Compared with previous studies, this work demonstrates competitive accuracy while emphasizing practical deployability and contextual relevance. The inclusion of locally sourced images significantly improved real-world performance. The approach empowers Zimbabwean farmers with rapid, accurate, and actionable plant disease diagnosis, supporting sustainable agriculture and food security.
References
- Gai, Y., Wang, H. 2024. Plant disease: A growing threat to global food security. Agronomy.
- Food and Agriculture Organization of the United Nations. 2023. Zimbabwe: Agricultural economic overview.
- Wang, N., Sundin, G. W., Fuente, L. D. L., Cubero, J., Tatineni, S., Brewer, M. T., Zeng, Q., Bock, C. H., Cunniffe, N. J., Wang, C., Candresse, T., Chappell, T., Coleman, J. J., Munkvold, G. 2024. Key challenges in plant pathology in the next decade. Phytopathology.
- Asani, E. O., Osadeyi, Y. P., Adegun, A. A., Viriri, S., Ayoola, J. A., Kolawole, E. A. 2023. mPD-APP: A mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world. Front. Artif. Intell.
- Olodun, M. 2023. Zimbabwe’s mobile boom: How the country achieved a 97% penetration rate. Zimbabwe Advocate.
- Postal and Telecommunications Regulatory Authority of Zimbabwe. 2023. Postal and telecommunications sector performance report.
- Isinkaye, F., Erute, E. D. 2022. A smartphone-based plant disease detection and treatment recommendation system using machine learning techniques. Trans. Eng. Comput. Sci.
- Sambana, B., Nnadi, H. S., Wajid, M. A., Fidelia, N. O., Camacho-Zuñiga, C., Ajuzie, H. D., Onyema, E. M. 2025. An efficient plant disease detection using transfer learning approach. Sci. Rep.
- Mohanty, S. P., Hughes, D. P., Salathé, M. 2016. Using deep learning for image-based plant disease detection. Front. Plant Sci.
- Tan, M., Le, Q. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning.
- Wiesner-Hanks, T., Wu, H., Stewart, E., DeChant, C., Kaczmar, N., Lipson, H., Gore, M. A., Nelson, R. J. 2019. Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Front. Plant Sci.
- Ogunbiyi, T. E., Mustapha, A. M., Eturhobore, E. J., Achas, M. J., Sessi, T. A. 2024. Development of a web-based tomato plant disease detection and diagnosis system using transfer learning techniques. Ann. Civil Environ. Eng.
- Pravalika, H. V., Jayavardhini, P., Gowda, P. S., Tanuja, J., Mamatha, A. 2024. AgriAI: An integrated web platform for plant disease detection, prevention strategies, virtual assistance, and feedback. Int. J. Res. Publ. Rev.
- Too, E. C., Yujian, L., Njuki, S., Yingchun, L. 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric.
- Brahimi, M., Boukhalfa, K., Moussaoui, A. 2017. Deep learning for tomato diseases: Classification and symptoms visualization. Appl. Artif. Intell.
- Xu, M., Yoon, S., Jeong, Y., and Park, D. S. 2022. Transfer learning for versatile plant disease recognition with limited data. Frontiers in Plant Science. 13, 1010981. DOI: https://doi.org/10.3389/fpls.2022.1010981.
- Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D. P. 2017. Deep learning for image-based cassava disease detection. Front. Plant Sci.
- Okimba, P. E., Ejiofor, V. E. 2023. Review of challenges in image acquisition on plant-based disease detection in Africa. Global Sci. J.
- Howard, A. G., et al. 2019. Searching for MobileNetV3. arXiv preprint.
- Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y. 2022. Lightweight deep learning model for on-device plant disease recognition. IEEE Access.
- Ferentinos, K. P. 2018. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric.
- Hughes, D. P., Salathé, M. 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv.
Index Terms
Keywords