基于迁移学习的可解释视觉变换器的干旱胁迫有效识别。

IF 3.8 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Aswini Kumar Patra, Ankit Varshney, Lingaraj Sahoo
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引用次数: 0

摘要

早期发现干旱胁迫对于在干旱影响不可逆转之前及时采取措施减少作物损失至关重要。通过非侵入性成像技术捕获干旱胁迫下细微的表型和生理变化,这些成像数据为机器学习方法识别干旱胁迫提供了宝贵的资源。在卷积神经网络得到广泛应用的同时,视觉变压器(ViTs)在捕获长期依赖关系和复杂的空间关系方面提供了一个有希望的替代方案,从而增强了对干旱胁迫微妙指标的检测。我们提出了一个可解释的深度学习管道,利用ViTs的力量,利用航空图像对马铃薯作物进行干旱胁迫检测。我们采用了两种不同的方法:一种是ViT和支持向量机(SVM)的协同组合,其中ViT从航空图像中提取复杂的空间特征,支持向量机将作物分类为受胁迫或健康;另一种是端到端方法,使用ViT中的专用分类层直接检测干旱胁迫。我们的主要发现通过可视化注意图来解释ViT模型的决策过程。这些地图突出了航空图像中的特定空间特征,ViT模型将其作为干旱胁迫特征。研究结果表明,所提出的方法不仅在干旱胁迫识别方面具有较高的准确性,而且还揭示了与干旱胁迫相关的多种微妙植物特征。这为干旱压力监测提供了一个可靠的、可解释的解决方案,使农民能够做出明智的决定,改善作物管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An explainable vision transformer with transfer learning based efficient drought stress identification.

Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasive imaging techniques and these imaging data serve as valuable resource for machine learning methods to identify drought stress. While convolutional neural networks are in wide use, vision transformers (ViTs) present a promising alternative in capturing long-range dependencies and intricate spatial relationships, thereby enhancing the detection of subtle indicators of drought stress. We propose an explainable deep learning pipeline that leverages the power of ViTs for drought stress detection in potato crops using aerial imagery. We applied two distinct approaches: a synergistic combination of ViT and support vector machine (SVM), where ViT extracts intricate spatial features from aerial images, and SVM classifies the crops as stressed or healthy and an end-to-end approach using a dedicated classification layer within ViT to directly detect drought stress. Our key findings explain the ViT model's decision-making process by visualizing attention maps. These maps highlight the specific spatial features within the aerial images that the ViT model focuses as the drought stress signature. Our findings demonstrate that the proposed methods not only achieve high accuracy in drought stress identification but also shedding light on the diverse subtle plant features associated with drought stress. This offers a robust and interpretable solution for drought stress monitoring for farmers to undertake informed decisions for improved crop management.

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来源期刊
Plant Molecular Biology
Plant Molecular Biology 生物-生化与分子生物学
自引率
2.00%
发文量
95
审稿时长
1.4 months
期刊介绍: Plant Molecular Biology is an international journal dedicated to rapid publication of original research articles in all areas of plant biology.The Editorial Board welcomes full-length manuscripts that address important biological problems of broad interest, including research in comparative genomics, functional genomics, proteomics, bioinformatics, computational biology, biochemical and regulatory networks, and biotechnology. Because space in the journal is limited, however, preference is given to publication of results that provide significant new insights into biological problems and that advance the understanding of structure, function, mechanisms, or regulation. Authors must ensure that results are of high quality and that manuscripts are written for a broad plant science audience.
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