Rose leaf diseases severely reduce yield and product quality, and traditional disease monitoring relies on manual visual inspection by experts, which is inefficient for large-scale cultivation. However, deploying accurate and lightweight detectors in field environments remains challenging due to two main obstacles. First, models trained under controlled laboratory conditions suffer performance degradation due to domain shift when deployed in complex field environments. Second, the computational capacity of hardware deployable in the field is often limited. To address these problems, this study proposes a practical knowledge distillation approach based on transferable feature representations from a pre-trained teacher model, rather than on complex distillation architecture. A high-capacity YOLOv12-L teacher, pre-trained on laboratory images, guided the training of a compact YOLOv12-N student using field images. The distilled YOLOv12-N student model achieved an mAP@50 of 81.1% on field test set, representing a 3.5% improvement over the baseline YOLOv12-N model, while maintaining a highly efficient architecture of only 2.56 million parameters and 6.3 GFLOPs. Several ablation studies confirm the core contribution of this work, namely that the performance gains in lightweight detection stem primarily from the transfer of the teacher model’s feature representations, rather than from modifications to the distillation algorithm or student model’s architecture, thus clarifying the importance of high quality feature transfer in cross-domain agricultural vision tasks. This approach provides a generalizable and efficient solution for real-time rose leaf disease detection in precision agriculture.

With the deepening of the smart re-baking factory and the accelerating digital transformation of the agricultural industry, tobacco re-baking, as the core link of raw materials for the cigarette process, is facing the urgent need to move from the traditional manual way to the intelligent automated system in terms of cigarette product quality traceability and information management, since the traditional way of relying on manual auditing has been difficult to meet the modern tobacco industry's high standards of efficiency, accuracy and traceability. In this paper, a novel multimodal learning framework, MLDP (Multimodal Learning for Document Processing), is proposed to address the bottlenecks in efficiency, accuracy, and robustness of traditional cigarette box label information extraction methods. The framework integrates the advantages of text modal and image modal, and integrates various DeBERTa variants such as BiLSTM-DeBERTa, Multi-Sample Dropout-DeBERTa, Distil-DeBERTa, etc., and achieves a balance between the model diversity and computational efficiency through the Optuna-driven weighted integration strategy. A balance between model diversity and computational efficiency is achieved; for image modality processing, PaddleOCR is used for character recognition, and a text correction mechanism based on Levenshtein distance is introduced to reduce the digit recognition error. In order to enhance the cross-modal synergy, MLDP designs a dynamic weighted fusion modal decision-making mechanism, and constructs a modal error correction matrix to optimize the information extraction results, so as to improve the robustness and accuracy in complex scenarios. The experiments are conducted on the multimodal dataset of cigarette box labels provided by the re-baking factory, and the results show that the MLDP framework improves the average F1 score by 4.34% compared to the text-only model (MLDP-T) and 5.73% compared to the image-only model (MLDP-V), and achieves a 1.18%~1.55% improvement in recognizing the non-textual elements in the DocBank complex document task, which verifies its strong generalization capability. Furthermore, comparative experiments with state-of-the-art models including LayoutLMv3, UDOP, and Donut demonstrate the superiority of MLDP, achieving an average F1-score of 96.51% and outperforming these strong baselines by 1.3% to 3.7%. The results validate the effectiveness of the dynamic multimodal fusion strategy, especially in handling noisy documents and recognizing numeric fields. In addition, this paper also constructed and open-sourced an industrial-grade cigarette box label multimodal dataset containing 9,719 graphic-aligned samples, which fills the gap of high-quality benchmark data in this field.

With the advancement of smart re-baking factories and the accelerating digital transformation of agriculture, tobacco re-baking is facing an urgent need to shift from manual inspection to intelligent automated systems for quality traceability and information management. However, traditional cigarette box label information extraction methods still suffer from limited efficiency, insufficient accuracy, and poor robustness under real industrial conditions. To address these challenges, we propose MLDP (Multimodal Learning for Document Processing), a multimodal framework tailored for robust information extraction from tobacco box labels.MLDP integrates textual and visual information in a unified framework. For the text modality, multiple DeBERTa-based variants, including BiLSTM-DeBERTa, Multi-Sample Dropout-DeBERTa, Distil-DeBERTa, Two-Stage Dropout-DeBERTa, and LongMax-Dropout-DeBERTa, are combined through an Optuna-driven weighted ensemble strategy to balance model diversity and computational efficiency. For the image modality, PaddleOCR is adopted for text detection and recognition, and a Levenshtein distance-based correction mechanism is introduced to reduce OCR errors, especially in numeric fields. To further enhance cross-modal complementarity, MLDP employs a dynamic weighted fusion strategy together with a decision matrix for modality-level error correction, improving extraction robustness under noisy and complex industrial conditions.Experiments on a real-world multimodal tobacco box label dataset show that MLDP achieves a Micro-F1 of 96.51%, outperforming the text-only baseline (MLDP-T) and image-only baseline (MLDP-V) by 4.34% and 5.73%, respectively. Compared with classical document understanding baselines, including LayoutLMv3, UDOP, and Donut, and recent advanced document parsing or multimodal baselines such as MinerU2.5, PaddleOCR-VL, and Qwen2.5-VL, MLDP achieves the best overall performance and provides a favorable accuracy-efficiency trade-off.In addition, on the DocBank benchmark, MLDP yields 1.18%-1.55% gains on non-textual elements, demonstrating promising generalization ability beyond the target industrial scenario. Nevertheless, the current framework still depends on OCR quality and has been validated mainly on tobacco box labels, so its robustness under more diverse industrial document distributions requires further study. Furthermore, we construct and open-source an industrial tobacco box label multimodal dataset containing 9,719 graphically aligned samples, providing a useful benchmark resource for this task.