基于少样本学习的酿酒葡萄品种鉴定方法  

Few-shot learning for identifying wine grape varieties with limited data

作　　者：全吾梦 施钦晁 范一言 王起帆 苏宝峰[1,2] QUAN Wumeng;SHI Qinchao;FAN Yiyan;WANG Qifan;SU Baofeng(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]农业农村部农业物联网重点实验室,杨凌712100

出　　处：《农业工程学报》2025年第4期211-219,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中国宁夏回族自治区重点研发计划项目(2024BBF02006)。

摘　　要：酿酒葡萄品种的精确鉴定是实现葡萄园智慧化管理的关键环节。针对品种鉴定过程中,标注数据需求量大、成本高昂且对新品种适应性不足的问题,该研究提出了一种基于少样本学习的两阶段品种鉴定方法。首先,为了减少复杂背景对少样本学习模型的干扰,构建了Deeplabv3+语义分割模型,实现了前景叶片的精细提取;其次,采用基于度量的元学习方法,使用基于MobileNetV2网络结构并融合注意力机制设计的Mobile-CS作为主干网络,实现了在少样本条件下品种的准确鉴定,并在新的品种鉴定任务中快速适应。试验结果表明,Deeplabv3+模型在叶片分割上实现了97.52%的平均交并比;少样本学习模型在5-way 5-shot任务上达到了80.06%的平均准确率,优于经典卷积神经网络结构和经典少样本学习的方法。该研究的两阶段品种鉴定方法具有较高的识别准确率和较强的泛化能力,能够为农业领域的智能识别技术提供新的解决方案。Wine grape has been one of the most significant economic crops.The precise identification of wine grape varieties can greatly contribute to effective vineyard management and the quality of the wine industry.It is still lacking in the adaptability to new varieties in the variety identification,particularly for the labeled data and the substantial costs.In this study,a two-phase variety identification was proposed using few-sample learning,including the extraction phase and meta-learning phase.Firstly,the impact of complex backgrounds was mitigated by the few-sample learning model.A Deeplabv3+semantic segmentation model was then developed on the segmented image after post-processing.Specifically,image cropping was performed to enlarge the pixel area of the leaves in the image.The precise extraction of the foreground leaves was provided on the high-quality image inputs for the subsequent model.Secondly,metric meta-learning was utilized to evaluate the similarity among the samples in the support dataset and the samples in the query to recognize variety.Mobile-CS architecture was also employed as the backbone network during meta-learning.MobileNetV2 network structure was then enhanced to lighten the original network.A bottleneck structure was also removed to integrate the CBAM attention mechanism.Precise identification of varieties was realized under sample-limited conditions,and then rapid adaptation in new variety identification tasks.An image dataset of wine grape leaves was constructed using 30 varieties,with a total of 5908 raw images in fields.The experimental results demonstrate that the Deeplabv3+model achieved an average intersection ratio of 97.52%and a pixel accuracy of 98.98%for precise leaf segmentation.In limited data samples,the two-stage model achieved an average accuracy of 62.27%on the 5-way 1-shot task and 80.06%on the 5-way 5-shot task,indicating superior performance,compared with the rest few-sample learning.Furthermore,the backbone network performed the best with a smaller number of parameters,co

关 键 词：图像识别 计算机视觉 深度学习 酿酒葡萄 品种鉴定 少样本学习 元学习 

分 类 号：S24[农业科学—农业电气化与自动化]