Few-shot cow identification via meta-learning

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Xingshi Xu, Yunfei Wang, Yuying Shang, Guangyuan Yang, Zhixin Hua, Zheng Wang, Huaibo Song
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引用次数: 0

Abstract

Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method.
通过元学习进行奶牛识别
牛的鉴定是精确畜牧业的先决条件。基于生物特征的方法在奶牛识别方面取得了重大进展。然而,大量的标签成本和频繁的识别任务变化仍然阻碍了模型的应用。在这项工作中,提出了一种称为“MFCI”的新方法,以实现在少量射击和任务变化条件下准确识别奶牛。具体来说,该方法包括两个部分:奶牛定位和奶牛识别。首先,采用改进的带有Ghost模块的YOLOv5n快速检测图像中的奶牛位置。然后,引入了模型不可知元学习(Model-Agnostic Meta-Learning, MAML)框架,以便在少量条件下准确识别,并快速适应奶牛个体的频繁变化。此外,采用了自动编码器,通过结合监督和无监督方法,使Base-Learner能够学习更多的广义特征。实验结果表明,所提出的奶牛定位模型的mAP值达到了99.5%。所提出的奶牛识别模型在20头奶牛中每头奶牛只有5个样本,准确率达到90.43%,优于其他最先进的方法。结果表明,该方法具有广泛的适用性和重要的应用价值。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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