Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery
Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery
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DOI：
10.3390/rs15133301
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发表时间：
2023-06
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影响因子：
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通讯作者：
Canh Nguyen;V. Sagan;Juan Skobalski;Juan Ignacio Severo
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作者：
Canh Nguyen;V. Sagan;Juan Skobalski;Juan Ignacio Severo
粮食生产系统比以往任何时候都更容易受到疾病的影响，在气候变化的时代，这种威胁正在增加，因为气候变化为新出现的疾病创造了更有利的条件。幸运的是，科学家和工程师们正在大力推进农业创新以应对这一挑战。无人机遥感是其中的一项创新，因此被广泛应用于作物健康监测和表型分析。这项研究展示了航空遥感在及时诊断春小麦条锈病感染以及确定可干预时期以防止产量损失方面的多功能性。2021年生长季期间，一架配备航空多光谱传感器的小型无人机定期飞越阿根廷查卡布科（−34.64；−60.46）的一块试验田，并收集其遥感图像。采集后的地块级图像通过从光谱维度手工制作以疾病为中心的植被指数（VIs）以及从空间维度制作灰度共生矩阵（GLCM）纹理特征，进行了全面的特征工程处理。构建了一个包含支持向量机（SVM）、随机森林（RF）和多层感知器（MLP）的机器学习流程，以识别田间健康、轻度感染和重度感染地块的位置。一种依靠特征学习机制的定制三维卷积神经网络（3D - CNN）是一种替代预测方法。研究发现红边（690 - 740纳米）和近红外（NIR）（740 - 1000纳米）是区分健康和重度感染小麦的重要光谱波段。类胡萝卜素反射指数2（CRI2）、土壤调节植被指数2（SAVI2）以及在最佳距离d = 5和角度方向θ = 135°的GLCM对比度纹理是相关性最强的特征。基于3D - CNN的小麦疾病监测在播种后40天（DAS），即作物分蘖时，检测准确率就达到60%，在随后的孕穗和开花阶段（100 - 120 DAS）分别提高到71%和77%，对于光谱 - 空间 - 时间融合数据模型，准确率达到峰值79%。低成本多光谱无人机早期疾病诊断的成功不仅为作物育种和病理学提供了新的思路，还通过告知种植者一个预防期来帮助他们，在95%的置信水平下，这个预防期可能会保留3 - 7%的产量。
The food production system is vulnerable to diseases more than ever, and the threat is increasing in an era of climate change that creates more favorable conditions for emerging diseases. Fortunately, scientists and engineers are making great strides to introduce farming innovations to tackle the challenge. Unmanned aerial vehicle (UAV) remote sensing is among the innovations and thus is widely applied for crop health monitoring and phenotyping. This study demonstrated the versatility of aerial remote sensing in diagnosing yellow rust infection in spring wheats in a timely manner and determining an intervenable period to prevent yield loss. A small UAV equipped with an aerial multispectral sensor periodically flew over, and collected remotely sensed images of, an experimental field in Chacabuco (−34.64; −60.46), Argentina during the 2021 growing season. Post-collection images at the plot level were engaged in a thorough feature-engineering process by handcrafting disease-centric vegetation indices (VIs) from the spectral dimension, and grey-level co-occurrence matrix (GLCM) texture features from the spatial dimension. A machine learning pipeline entailing a support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) was constructed to identify locations of healthy, mild infection, and severe infection plots in the field. A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. The study found red-edge (690–740 nm) and near infrared (NIR) (740–1000 nm) as vital spectral bands for distinguishing healthy and severely infected wheats. The carotenoid reflectance index 2 (CRI2), soil-adjusted vegetation index 2 (SAVI2), and GLCM contrast texture at an optimal distance d = 5 and angular direction θ = 135° were the most correlated features. The 3D-CNN-based wheat disease monitoring performed at 60% detection accuracy as early as 40 days after sowing (DAS), when crops were tillering, increasing to 71% and 77% at the later booting and flowering stages (100–120 DAS), and reaching a peak accuracy of 79% for the spectral-spatio-temporal fused data model. The success of early disease diagnosis from low-cost multispectral UAVs not only shed new light on crop breeding and pathology but also aided crop growers by informing them of a prevention period that could potentially preserve 3–7% of the yield at the confidence level of 95%.