Accurate and timely recognition of pests and diseases is often required in precision agriculture. However, conventional deep learning models rely on large-scale annotated data for effective training, leading to weak generalization. The challenges also remain, including the extreme scarcity of high-quality labeled samples, serious symptom variations over regions and crop varieties, strong interference from complex field backgrounds, and highly imbalanced sample distributions among different disease categories. In this study, a high-performance, parameter-efficient, and robust vision-language model was developed for stable and accurate plant disease identification under extremely limited training samples. Real-world deployment was also realized in field environments, particularly for the wide application of intelligent disease diagnosis. A Dual-prompt Hierarchical Interactive vision-language model (DPHI) was constructed using the Contrastive Language-Image Pre-training (CLIP) architecture. Three components were utilized to strengthen deep cross-modal fusion for feature identification. (1) A Synergistic Prompt Interaction (SPI) module was constructed to extract learnable visual and textual prompts hierarchically at each layer of the visual and text encoders. A class-mask-guided bidirectional cross-attention mechanism was applied to realize fine-grained semantic alignment between visual and textual features, while filtering out irrelevant background noise and suppressing inter-class feature confusion. (2) A Dynamic Prototype Constraint (DPC) mechanism was established to maintain stable and consistent category representations, in which the Exponential Moving Average (EMA) strategy was used to dynamically update class visual prototypes. The disturbance was reduced from small-batch sampling bias, noisy data, and outlier samples. (3) A multi-task collaborative contrastive loss function was designed to optimize the shared visual-text feature space, which combined cross-entropy loss, relative distance loss, and textual dispersion loss to simultaneously strengthen intra-class compactness and inter-class separability. Comprehensive experiments were implemented under the 16-shot few-shot setting on three representative public datasets: PlantVillage, PlantDoc, and Sugarcane Leaf Disease Dataset, which covered controlled laboratory environments, complex real-field conditions, and single-crop fine-grained disease classification. The results showed that the DPHI model achieved the highest classification accuracy and F1-score on all three datasets, outperforming state-of-the-art prompt learning, including Context Optimization (CoOp) and Multi-modal Prompt Learning (MaPLe), as well as classic convolutional neural networks and Transformer-based recognition models. An apple disease dataset was collected from Yaozhou District, Tongchuan City, Shaanxi Province, China. The DPHI obtained 92.11% macro-average accuracy and 92.20% macro-average F1-score, indicating the outstanding recognition precision even in classes with extremely scarce samples, such as brown spot disease. The pre-trained CLIP backbone network was frozen to train only a small number of learnable prompt parameters. Llow computational cost and memory usage were attained in both training and inference stages, which greatly improved training efficiency with the low hardware requirements for practical deployment. The DPHI model significantly improved the few-shot learning performance, fine-grained discrimination, and cross-scenario generalization for agricultural disease recognition. The findings can also provide a reliable, parameter-efficient, and low-cost technical solution for intelligent disease diagnosis in real agricultural applications, such as rare cash crops, newly emerging epidemic diseases, and cross-regional popularization. The application cost of intelligent disease recognition can be effectively reduced to ensure the practical feasibility of model deployment in complex field environments.

Apple leaf diseases (ALD) identification can be characterized by “significant intra-class variation and subtle inter-class differences”. In this study, an innovative model was presented to integrate transformer with prototype self-supervised (FTPSS) learning. This improved model aimed to significantly elevate the precision of ALD recognition, thereby enhancing disease management strategies in orchards. The ResNet50 was utilized as the backbone network in the FTPSS model. This robust architecture was employed to extract multi-level feature maps from ALD images, in order to capture the intricate details for accurate disease identification. An encoder design was also integrated a simplified self-attention (SSA) mechanism with spatial attention guided deformable convolution (SAG-DC). The simplified self-attention and deformable convolution transformer (SSADC-TF) was used to facilitate the effective interaction and fusion of multi-level feature maps. The extracted features were then processed. The sensitivity of model was enhanced for the irregular lesion areas within ALD images. SSADC-TF was significantly distinguished among different disease manifestations. A prototype self-supervised (PSS) learning module was introduced to further verify the performance of model. Two self-supervised loss functions: "Orthogonality" and “Clustering” were selected in the module. In the “Orthogonality” loss, the feature representations of different ALD classes were orthogonal to each other. A clear separation among classes was promoted to enhance the identification of the model. Meanwhile, the “Clustering” loss was used to tighten the intra-class compactness, thus ensuring that the variations within the same class was suitable for the robustness of the model. Extensive experiments were conducted on both standard and real-world image datasets, indicating the remarkable effectiveness of FTPSS model. The FTPSS model was achieved in a recognition accuracy of 98.61% on the standard image set, indicating a significant improvement of 5.15 percentage points over the baseline model. Similarly, the FTPSS model was obtained an accuracy of 98.73% on the real-world image set, indicating an enhancement of 4.49 percentage points, compared with the baseline. These results underscored the robust performance of FTPSS model to identify ALD, even in the presence of significant intra-class variation and subtle inter-class differences. The FTPSS model was attributed to the innovative integration of Transformer with Prototype Self-Supervised learning. There were the powerful feature extraction of ResNet50. SSADC-TF was also enhanced feature interaction and fusion. The complex details in ALD images were captured to achieve in a 2.40 percentage point improvement. Furthermore, the PSS learning module was introduced to mitigate the semantic gap, where the model was generalized well to new, unseen ALD cases. The accuracy of ALD image recognition increased by 2.69 percentage points. In conclusion, the FTPSS model shared a significant advancement in ALD recognition, with the potential to revolutionize disease management strategies in orchards. The precise, timely information can be expected to apply into the automatic process of disease detection ALD, thereby preserving the health and productivity of the orchards. This finding can greatly contribute to the field of precision agriculture using advanced deep learning techniques.