通过无人机高光谱图像量化的整合色素和色素相关光谱指数,改进小麦条锈病的早期检测

IF 7.6 Q1 REMOTE SENSING
Anting Guo , Wenjiang Huang , Binxiang Qian , Kun Wang , Huanjun Liu , Kehui Ren
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引用次数: 0

摘要

小麦条锈病是影响小麦生长的重要病害,常被称为 "小麦癌症"。要使作物管理者能够实施有效的控制措施,及早准确地发现条锈病至关重要。用于作物病害检测的高光谱遥感方法已受到广泛关注。然而,高光谱数据中常用的光谱波段或光谱指数(SIs)往往无法准确捕捉与作物病害早期阶段相关的细微变化。在本研究中,我们提出了一种结合无人机高光谱图像中的色素和光谱指数对小麦条锈病进行早期检测的方法。我们使用安装在 S1000 型六旋翼无人机上的 UHD 185 高光谱传感器(450-950 nm)获取了接种后 7、16 和 23 天(DPI)的小麦条锈病高光谱图像。利用辐射传递模型从无人机高光谱图像中提取色素,包括叶绿素(Cab)、类胡萝卜素(Car)、花青素、Cab/Car 和 11 种与色素相关的 SI。利用这些参数和机器学习算法开发了小麦条锈病早期检测模型。结果表明,在 7、16 和 23 DPI 时,所选色素和 SI 能有效区分受条锈病感染的小麦和健康小麦。结合色素和 SIs 的模型(PSIMs)比仅依靠 SIs(SIMs)或色素(PMs)的模型表现更好。值得注意的是,在疾病的无症状阶段(7 DPI)和轻微症状阶段(16 DPI),基于射频的 PSIM 的总体准确率分别为 78.1% 和 81.3%。此外,PSIM 中的色素比 SI 的贡献更大,突出了色素在条锈病早期检测中的重要性。总之,本研究提出的色素与光谱指数相结合的方法有效提高了小麦条锈病的早期检测能力,并为其他作物病害的早期检测提供了有价值的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery
Wheat stripe rust is a significant disease affecting wheat growth, often referred to as the “cancer of wheat”. Early and accurate detection of stripe rust is crucial for enabling crop managers to implement effective control measures. Hyperspectral remote sensing methods for crop disease detection have gained significant attention. However, commonly used spectral bands or spectral indices (SIs) from hyperspectral data often fail to capture the subtle changes associated with the early stages of crop diseases accurately. In this study, we propose a method for early detection of wheat stripe rust by combining pigments and SIs retrieved from UAV hyperspectral imagery. We acquired hyperspectral images of wheat stripe rust at 7, 16, and 23 days post-inoculation (DPI) using a UHD 185 hyperspectral sensor (450–950 nm) mounted on an S1000 hexacopter UAV. Pigments, including chlorophylls (Cab), carotenoids (Car), anthocyanins, Cab/Car, and 11 pigment-related SIs, were extracted from UAV hyperspectral images using radiative transfer modeling. The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. Models that combine pigments and SIs (PSIMs) perform better than those relying solely on SIs (SIMs) or pigments (PMs). Notably, the RF-based PSIM achieved overall accuracies of 78.1 % and 81.3 % during the asymptomatic (7 DPI) and minimally symptomatic (16 DPI) phases of disease, respectively. Additionally, the pigments in the PSIM contributed more significantly than the SIs, highlighting the importance of pigments in the early detection of stripe rust. Overall, the method combining pigments and spectral indices proposed in this study effectively enhances the early detection of wheat stripe rust and offers valuable insights into the early detection of other crop diseases.
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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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