Home Plant Health Prediction Application With Cellphone Camera And AI Based On Federated Learning
Keywords:
Artificial Intelligence, Cell Phone Camera, Federated Learning, Image Classification, Mobile ApplicationAbstract
Accurately monitoring the health of house plants is becoming increasingly important as public interest in urban farming and ornamental plants increases. This research develops a plant health prediction application using cell phone cameras and artificial intelligence (AI) based on federated learning. This technology enables AI model training directly on the user's device without having to send image data to a central server, thus maintaining user privacy and network efficiency. The app uses plant image classification models to detect disease symptoms such as leaf spot, wilting, and discoloration. Data is collected from various users, and the model is trained in a decentralized manner through a federated averaging protocol. Test results show that the system is able to achieve prediction accuracy above 85% in detecting plant conditions, with low latency and minimal data consumption. This approach shows significant potential in the application of secure, distributed, and user-friendly AI for home plant monitoring
References
Abadi, M., et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI.
Bonawitz, K., et al. (2019). Towards Federated Learning at Scale: System Design. Proceedings of MLSys.
Chen, J., & Ran, X. (2019). Deep Learning With Edge Computing: A Review. Proceedings of the IEEE, 107(8), 1655–1674.
Das, A., et al. (2020). Federated Learning for Predictive Maintenance. ACM Transactions on Internet of Things, 1(1), 1–22.
Dean, J., et al. (2012). Large Scale Distributed Deep Networks. NIPS.
Deng, L., & Yu, D. (2014). Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387.
Flower, M., et al. (2020). Plant Disease Detection Using Mobile Image Processing and CNN. Journal of Agricultural Informatics, 11(2), 55–63.
Ghosh, A., et al. (2021). Privacy-Preserving Smart Agriculture Using Federated Learning. IEEE IoT Journal, 8(21), 15678–15689.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
He, K., et al. (2016). Deep Residual Learning for Image Recognition. CVPR.
Howard, A. G., et al. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint.
Jaiswal, A., et al. (2021). A Survey on Federated Learning for Healthcare: Vision, Challenges, and Future Directions. IEEE Access, 9, 62278–62300.
Khan, L., et al. (2021). Edge Computing and Federated Learning for Smart Agriculture. Sensors, 21(4), 1256.
Li, T., Sahu, A. K., et al. (2020). Federated Optimization in Heterogeneous Networks. Proceedings of MLSys.
Liu, Y., et al. (2020). AI in Agriculture: Applications, Challenges, and Opportunities. Computers and Electronics in Agriculture, 170, 105256.
McMahan, H. B., et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS.
Mohammadi, M., et al. (2018). Deep Learning for IoT Big Data and Streaming Analytics: A Survey. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960.
Nguyen, D. C., et al. (2021). Federated Learning Meets Blockchain: Opportunities and Challenges. IEEE Internet of Things Journal, 8(16), 12806–12825.
Ramcharan, A., et al. (2019). A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis. Frontiers in Plant Science, 10, 272.
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint.
Sheller, M. J., et al. (2020). Federated Learning in Medicine: Facilitating Multi-Institutional Collaborations Without Sharing Patient Data. Scientific Reports, 10, 12598.
Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR.
Sun, Y., et al. (2019). Plant Disease Identification Based on Mobile Edge Computing and Deep Learning. IEEE Access, 7, 160233–160240.
Yang, Q., et al. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
Zhang, Y., et al. (2020). AI-Based Plant Disease Diagnosis Using Image Recognition. Computers and Electronics in Agriculture, 172, 105336
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Copyright (c) 2025 Hardianto, Akhsan Daolu (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
