摘要
水稻叶片病害智能识别在现代农业生产中具有重要意义。针对传统Vision Transformer网络缺乏归纳偏置,难以有效捕捉图像局部细节特征的问题,提出了一种改进的Vision Transformer模型。该模型通过引入内在归纳偏置,增强了对多尺度上下文以及局部与全局依赖关系的建模能力,同时降低了对大规模数据集的需求。此外,Vision Transformer中的多层感知器模块被Kolmogorov-Arnold网络结构取代,从而提升了模型对复杂特征的提取能力和可解释性。实验结果表明,所提模型在水稻叶片病害识别任务中取得了优异的性能,识别准确率达到了98.62%,较原始ViT模型提升了6.2%,显著提高了对水稻叶片病害的识别性能。
Intelligent recognition of rice leaf diseases is of great significance in modern agricultural production.Focused on the issue that the traditional Vision Transformer network lacks inductive bias and is difficult to effectively capture the local detail features of the image,an improved Vision Transformer model was proposed.This model′s the ability to model multi-scale context as well as local and global dependencies was enhanced by introducing intrinsic inductive bias,while reduced the need for large-scale datasets.In addition,the multi-layer perceptron module in the Vision Transformer was replaced by the Kolmogorov-Arnold networks structure,thereby improving the model′s ability to extract complex features and interpretability.Experimental results show that the proposed model achieved excellent performance in the rice leaf disease recognition task,with an accuracy of 98.62%,which was 6.2%higher than the original ViT model,effectively improving the recognition performance of rice leaf diseases.
作者
朱周华
周怡纳
侯智杰
田成源
Zhu Zhouhua;Zhou Yina;Hou Zhijie;Tian Chengyuan(College of Communication and Information Technology,Xi′an University of Science and Technology,Xi′an 710061,China)
出处
《电子测量技术》
北大核心
2025年第10期153-160,共8页
Electronic Measurement Technology
基金
国家自然科学基金联合资助项目(U19B2015)资助。
