Zhou Huachen, Li Weixia, Li Pei, Xu Yifei, Zhang Lin, Zhou Xingyu, Zhao Zihan, Li Enqi, Lv Chunli
China Agricultural University, Beijing 100083, China.
Beijing Foreign Studies University, Beijing 100089, China.
Plants (Basel). 2025 Jan 23;14(3):339. doi: 10.3390/plants14030339.
The rapid advancement in smart agriculture has introduced significant challenges, including data scarcity, complex and diverse disease features, and substantial background interference in agricultural scenarios. To address these challenges, a disease detection method based on few-shot learning and diffusion generative models is proposed. By integrating the high-quality feature generation capabilities of diffusion models with the feature extraction advantages of few-shot learning, an end-to-end framework for disease detection has been constructed. The experimental results demonstrate that the proposed method achieves outstanding performance in disease detection tasks. Across comprehensive experiments, the model achieved scores of 0.94, 0.92, 0.93, and 0.92 in precision, recall, accuracy, and mean average precision (mAP@75), respectively, significantly outperforming other comparative models. Furthermore, the incorporation of attention mechanisms effectively enhanced the quality of disease feature representations and improved the model's ability to capture fine-grained features.
智能农业的快速发展带来了重大挑战，包括数据稀缺、疾病特征复杂多样以及农业场景中的大量背景干扰。为应对这些挑战，提出了一种基于少样本学习和扩散生成模型的疾病检测方法。通过将扩散模型的高质量特征生成能力与少样本学习的特征提取优势相结合，构建了一个用于疾病检测的端到端框架。实验结果表明，该方法在疾病检测任务中取得了优异的性能。在综合实验中，该模型在精确率、召回率、准确率和平均精度均值（mAP@75）方面分别达到了0.94、0.92、0.93和0.92的分数，显著优于其他对比模型。此外，注意力机制的引入有效地提高了疾病特征表示的质量，并提升了模型捕捉细粒度特征的能力。
