我们能否在事先不知道植物病害存在的情况下检测到植物病害?

IF 7.6 Q1 REMOTE SENSING
Rebecca Leygonie, Sylvain Lobry, Laurent Wendling
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引用次数: 0

摘要

有必要帮助农民做出决策,最大限度地提高作物产量。近年来出现了许多利用遥感图像深度学习检测植物病害的研究,植物病害可能由环境条件、遗传或病原体等多种因素引起。这一问题可视为异常检测任务。然而,这些方法往往受限于注释数据的可用性或异常现象存在的先验知识。在很多情况下,我们无法获得这些信息。在这项工作中,我们提出了一种无需事先了解植物异常现象的方法,从而克服了这些限制。为此,我们使用由正常和异常植物样本组成的数据集,在辅助预测任务中训练一个模型。我们提出的方法研究了从可解释性模型中检索到的热图分布。基于在辅助任务中训练的模型能够提取重要植物特征的假设,我们建议研究新观察结果的热图与正常数据集热图分布的密切程度。实验表明,我们的方法在 GrowliFlower 和 PlantDoc 数据集上的表现优于 f-AnoGAN 和 OCSVM 等参考方法,在 PlantVillage 数据集上的表现也很有竞争力,而且不需要事先了解异常情况的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can we detect plant diseases without prior knowledge of their existence?
There is a need to help farmers make decisions to maximize crop yields. Many studies have emerged in recent years using deep learning on remotely sensed images to detect plant diseases, which can be caused by multiple factors such as environmental conditions, genetics or pathogens. This problem can be considered as an anomaly detection task. However, these approaches are often limited by the availability of annotated data or prior knowledge of the existence of an anomaly. In many cases, it is not possible to obtain this information. In this work, we propose an approach that can detect plant anomalies without prior knowledge of their existence, thus overcoming these limitations. To this end, we train a model on an auxiliary prediction task using a dataset composed of samples of normal and abnormal plants. Our proposed method studies the distribution of heatmaps retrieved from an explainability model. Based on the assumptions that the model trained on the auxiliary task is able to extract important plant characteristics, we propose to study how closely the heatmap of a new observation follows the heatmap distribution of a normal dataset. Through the proposed a contrario approach, we derive a score indicating potential anomalies.
Experiments show that our approach outperforms reference approaches such as f-AnoGAN and OCSVM on the GrowliFlower and PlantDoc datasets and has competitive performances on the PlantVillage dataset, while not requiring the prior knowledge on the existence of anomalies.
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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