Crop diseases rank among the most serious threats to agricultural productivity and global food supply. In recent years, deep learning has gained traction as an effective means of automating plant disease recognition. However, conventional centralized training methods introduce significant challenges, including privacy vulnerabilities, high computational overhead, and limited suitability for resource-constrained farming environments. Federated Learning (FL) offers a decentralized alternative that supports cooperative model training without requiring local data to leave individual devices. This paper presents a comprehensive survey of recent research on FL-driven crop disease detection. Emphasis is placed on the use of lightweight convolutional models compatible with edge and mobile platforms, and on the contribution of Explainable Artificial Intelligence (XAI) to improving model transparency and user trust. The surveyed works are assessed for their strengths and limitations. Key technical challenges — including non-IID data distributions across participating clients, communication overhead during model aggregation, and the lack of real-world field validation — are analyzed in depth. The paper concludes by identifying open research problems and proposing directions toward scalable, privacy-preserving, and computationally lean disease detection systems.

This paper has provided a structured survey of recent developments in federated learning for crop disease detection, highlighting the growing need for privacy-preserving and resource-efficient approaches in smart agriculture. The reviewed body of work demonstrates clear technical advancement in the field; however, unresolved issues related to data heterogeneity, communication overhead, and model interpretability continue to limit practical applicability. Addressing these challenges through the thoughtful combination of federated learning, compact neural network architectures, and explainable AI holds the potential to produce systems that are both technically sound and practically usable in real agricultural settings. Such developments could contribute meaningfully to improving crop yields, minimizing unnecessary use of agrochemicals, and advancing the broader goal of intelligent, data-driven farming at a global scale.

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