Abstract
The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aiming to develop a scalable, efficient, and secure solution for crop disease detection tailored to the environmental and operational conditions of Minnesota farms. By maintaining sensitive farm data locally and enabling collaborative model updates, our proposed framework seeks to achieve high accuracy in crop disease classification without compromising data privacy. We outline a methodology involving data collection from Minnesota farms, application of local deep learning algorithms, transfer learning, and a central aggregation server for model refinement, aiming to achieve improved accuracy in disease detection, good generalization across agricultural scenarios, lower costs in communication and training time, and earlier identification and intervention against diseases in future implementations. We outline a methodology and anticipated outcomes, setting the stage for empirical validation in subsequent studies. This work comes in a context where more and more demand for data-driven interpretations in agriculture has to be weighed with concerns about privacy from farms that are hesitant to share their operational data. This will be important to provide a secure and efficient disease detection method that can finally revolutionize smart farming systems and solve local agricultural problems with data confidentiality. In doing so, this paper bridges the gap between advanced machine learning techniques and the practical, privacy-sensitive needs of farmers in Minnesota and beyond, leveraging the benefits of federated learning.
Abstract (translated)
农业部门正在通过集成先进科技，特别是在数据驱动决策方面进行转型。本文提出了一种用于智能农场的联邦学习框架，旨在为明尼苏达州农场量身定制一种可扩展、高效且安全的作物病害检测解决方案。通过在本地保留敏感的农场数据，并实现协作模型更新，我们提出的这一框架力求在不牺牲数据隐私的前提下，提高作物病害分类的准确性。 该方法涉及从明尼苏达州农场收集数据，应用本地深度学习算法，进行迁移学习以及利用中央聚合服务器来优化模型。其目标是提升疾病检测的准确率、增强跨农业场景的一般化能力、降低通信和训练时间的成本，并在未来的实施中实现早期病害识别与干预。 本文还概述了研究方法及预期成果，为后续实证验证奠定了基础。在此背景下，越来越多地需要对基于数据驱动的解释进行评估，同时考虑到那些不愿分享运营数据的农场对于隐私的担忧。这将有助于提供一种安全高效、能够最终革新智能农业系统的病害检测方式，并以数据保密性解决当地农业生产的问题。 通过这种方法，本文在高级机器学习技术与明尼苏达州乃至更广泛地区的农民的实际和隐私敏感需求之间建立了联系，充分利用了联邦学习的优势。
URL
https://arxiv.org/abs/2509.12363
