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October 8, 2025Open Access

Plant Disease Detection Using Federated Learning and Cloud Infrastructure for Scalability and Data Privacy

PCPaulo V. CaminhaUniversidade Federal do ABC HOHelder OliveiraUniversidade de São Paulo

Key Points

Achieving 99.71% detection accuracy via federated learning emphasizes its potential in agriculture.
Federated learning enables secure collaboration for training models without sharing sensitive data.
Cloud computing supports federated learning by addressing data processing and model scalability challenges.
This approach promotes knowledge sharing while preserving data confidentiality, benefiting agricultural productivity.

Abstract

Agriculture faces significant challenges from crop diseases, making early and accurate detection critical. Federated Learning (FL), an advancement in artificial intelligence (AI) and machine learning (ML), presents a promising solution by enabling collaborative model training on decentralized data without the need to share sensitive information. This article examines the application of FL in detecting plant diseases through image analysis, highlighting the role of cloud computing in addressing challenges related to data processing, storage, and model scalability. By leveraging decentralized data stored and processed in the cloud, FL develops robust models that not only improve detection accuracy but also generalize effectively to new data, promoting knowledge sharing while ensuring data privacy. The integration of cloud infrastructure enables FL to scale, providing resilience and productivity gains in agricultural practices. The results show that the proposed approach achieves a 99.71% accuracy using the VGG16 model after Federated Learning aggregation, while preserving data confidentiality, enhancing agricultural resilience, and benefiting from the scalability and flexibility offered by cloud computing.

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