Tomato leaf diseases exhibit subtle inter-class differences and substantial intra-class variability, making accurate identification challenging for conventional deep learning models, especially under real-world conditions with diverse lighting, occlusion, and growth stages. Moreover, most existing approaches rely solely on visual features and lack the ability to incorporate semantic descriptions or expert knowledge, limiting their robustness and interpretability. To address these issues, we propose LPDiag, a multimodal prototype-attention diagnostic framework that integrates large language models (LLMs) for fine-grained recognition of tomato diseases. The framework first employs an LLM-driven semantic understanding module to encode symptom-aware textual embeddings from disease descriptions. These embeddings are then aligned with multi-scale visual features extracted by an enhanced Res2Net backbone, enabling cross-modal representation learning. A set of learnable prototype vectors, combined with a knowledge-enhanced attention mechanism, further strengthens the interaction between visual patterns and LLM prior knowledge, resulting in more discriminative and interpretable representations. Additionally, we develop an interactive diagnostic system that supports natural-language querying and image-based identification, facilitating practical deployment in heterogeneous agricultural environments. Extensive experiments on three widely used datasets demonstrate that LPDiag achieves a mean accuracy of 98.83%, outperforming state-of-the-art models while offering improved explanatory capability. The proposed framework offers a promising direction for integrating LLM-based semantic reasoning with visual perception to enhance intelligent and trustworthy plant disease diagnostics.

Deep learning has shown remarkable potential in smart agriculture applications, particularly for crop disease diagnosis. However, the distributed nature of agricultural data, along with environmental heterogeneity and varying disease, poses critical challenges for data privacy, model reliability, and multi-terminal collaboration. To address these issues, this paper proposes a novel Federated Learning Uncertainty-Aware Aggregation framework (FLUA) with Paillier Homomorphic Encryption (PHE) for tomato leaf disease classification. FLUA integrates a global aggregation encoder with a local Uncertainty Estimation (UE) module based on evidential deep learning, which quantifies the reliability of each client. An Uncertainty-Aware Weighting (UAW) mechanism dynamically adjusts aggregation weights according to client-specific uncertainty scores, enabling robust model learning across heterogeneous data sources. Meanwhile, PHE ensures privacy-preserving communication and secure parameter aggregation among all participating clients. Experimental results on three heterogeneous tomato leaf disease datasets under realistic non-IID conditions demonstrate that FLUA outperforms state-of-the-art benchmarks, including FedProx and Moon, in terms of both classification accuracy and model reliability. Notably, the proposed approach improves trustworthiness in multi-institution collaborative training, effectively mitigating data privacy risks and enhancing robustness under distribution shifts. These findings highlight FLUA’s potential as a generalizable and secure federated learning paradigm for agricultural AI applications.