ADeepWeeD:一个用于杂草分类的自适应深度学习框架

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Md Geaur Rahman , Md Anisur Rahman , Mohammad Zavid Parvez , Md Anwarul Kaium Patwary , Tofael Ahamed , David A. Fleming-Muñoz , Saad Aloteibi , Mohammad Ali Moni PhD
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引用次数: 0

摘要

有效的农田杂草管理对于实现最佳作物产量和保障全球粮食安全至关重要。每年,全世界的农民都要投入大量的时间、资金和资源来应对因杂草肆虐造成的产量损失,损失金额约为756亿美元。深度学习(DL)方法最近被应用于农业实践,特别是在杂草检测和分类方面。现有的基于dl的杂草分类技术,包括VGG16和ResNet50,首先通过在包含杂草种类的训练数据集上实现该算法来构建模型,然后使用该模型对训练过程中获得的杂草种类进行识别。鉴于农田的动态特性,我们认为现有的方法可能由于两个关键问题而表现出不理想的性能:(i)最初所有训练杂草物种的不可用性,因为这些物种随着时间的推移而出现,导致训练数据集逐渐扩大;(ii)用于模型开发的系统的内存和计算能力有限,这阻碍了所有杂草物种在较长时间内的保留。为了解决这些问题,本文引入了一种新的基于dl的框架,称为ADeepWeeD,用于杂草分类,促进自适应(即增量)学习,以便通过跟踪历史信息来处理新的杂草物种。通过将ADeepWeeD与四种非增量方法和两种增量方法在三个公开的大型数据集上的性能进行比较,使用F1-Score和分类精度两个标准对其进行评估。我们的实验结果表明,ADeepWeeD优于本研究中使用的现有技术。我们相信我们开发的模型可以用于开发杂草识别自动化系统。建议的方法的代码可以在GitHub上获得:https://github.com/grahman20/ADeepWeed。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ADeepWeeD: An adaptive deep learning framework for weed species classification
Efficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep Learning (DL) methodologies have been recently implemented to revolutionise agricultural practices, particularly in weed detection and classification. Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. Given the dynamic nature of crop fields, we argue that existing methods may exhibit suboptimal performance due to two key issues: (i) the unavailability of all training weed species initially, as these species emerge over time, resulting in a progressively expanding training dataset, and (ii) the constrained memory and computational capacity of the system utilised for model development, which hinders the retention of all weed species that manifest over an extended duration. To address the issues, this paper introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping track of historical information. ADeepWeeD is evaluated using two criteria, namely F1-Score and classification accuracy, by comparing its performances against four non-incremental and two incremental state-of-the-art methods on three publicly available large datasets. Our experimental results demonstrate that ADeepWeeD outperforms existing techniques used in this study. We believe that our developed model could be used to develop an automation system for weed identification. The code of the proposed method is available on GitHub: https://github.com/grahman20/ADeepWeed.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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