A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images
A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images
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DOI：
10.3390/rs11131554
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Xin Zhang;Liangxiu Han;Yingying Dong;Yue Shi;Wenjiang Huang;Lianghao Han;P. González-Moreno;
中科院分区：
文献类型：
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作者：
Xin Zhang;Liangxiu Han;Yingying Dong;Yue Shi;Wenjiang Huang;Lianghao Han;P. González-Moreno;
冬小麦条锈病是一种分布广泛且严重的真菌病害，在全球范围内造成严重的产量损失。有效监测和准确检测小麦条锈病对确保小麦生产稳定可靠和粮食安全至关重要。现有的标准方法通常依赖于农学家或受过训练的调查员在小面积作物区对疾病症状的人工检查。这是昂贵的，耗时的，并且由于测量员的主观性而容易出错。安装有高光谱图像传感器的无人机（UAV）的最新进展具有以低成本和高效率解决这些问题的潜力。这项工作提出了一种新的基于深度卷积神经网络（DCNN）的方法，用于使用无人机捕获的非常高空间分辨率的高光谱图像进行自动农作物病害检测。该模型引入了多个Inception-Resnet层进行特征提取，并进行了优化，以建立最合适的网络深度和宽度。得益于卷积层处理三维数据的能力，该模型将空间和光谱信息用于锈病检测。该模型是用无人机在整个作物周期的五个不同日期收集的高光谱图像进行校准的，该模型是在一个控制良好的田间试验中使用健康和锈病感染的小麦地块进行的。它的性能进行了比较，跨采样日期和随机森林，传统的分类方法，其中只使用光谱信息的代表。结果发现，该方法在整个生长周期中具有高性能，特别是在疾病传播的后期阶段。所提出的模型（0.85）的整体准确性高于随机森林分类器（0.77）。这些结果表明，结合光谱和空间信息是一个合适的方法，以提高农作物病害检测的精度与高分辨率无人机高光谱图像。
Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.