UAS-derived vegetation indices detect wheat leaf rust infection and its influence on grain yield and canopy temperature

IF 2 3区 农林科学 Q2 AGRONOMY
Crop Science Pub Date : 2025-04-27 DOI:10.1002/csc2.70062
Rahul Raman, Haly L. Neely, Nithya Rajan, Mahendra Bhandari, Jeffrey Siegfried, Amir M. H. Ibrahim, Curtis B. Adams, Robert H. Hardin
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引用次数: 0

Abstract

Leaf rust is a major biotic factor affecting wheat yield globally. However, the visual scoring technique to assess fungal disease in breeding programs requires significant expert manual labor and time. Unmanned aerial systems have the potential to scan large acreage in a short time for disease screening. An experiment was conducted at College Station and Castroville, TX, in 2018–2019 and 2019–2020 to assess the performance of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (GCI) in detecting leaf rust infection. Other measurements included proximal canopy temperature, grain yield, and visual screening for infection type and severity. A significant positive relationship (p < 0.001; R2 = 0.42–0.62) of grain yield with all three vegetation indices (VIs) was observed in mid-April 2019 at College Station. At College Station, the highest leaf rust severity coincided with the senescence stage in mid-April 2020. No relationship between the VIs and grain yield was observed. In mid-April 2020, when the leaf rust infection was high, the VIs showed a significant negative relationship (p < 0.05; R2 = 0.27) with grain yield at Castroville. All three VIs showed a significant linear negative relationship with canopy temperature at College Station (p < 0.05; R2 = 0.3–0.34) and Castroville (p < 0.001; R2 = 0.52–0.54) in mid-April 2020. At high leaf rust severity, the repeatability of GCI was less than NDVI and NDRE at both locations in 2019 and 2020. These results may differ if multiple factors affect winter wheat simultaneously.

Abstract Image

基于uas的植被指数可检测小麦叶锈病及其对产量和冠层温度的影响
叶锈病是影响全球小麦产量的主要生物因子。然而,在育种计划中评估真菌疾病的视觉评分技术需要大量的专家手工劳动和时间。无人机系统具有在短时间内扫描大面积疾病筛查的潜力。2018-2019年和2019-2020年分别在德克萨斯州的College Station和Castroville进行了归一化植被指数(NDVI)、归一化红边指数(NDRE)和叶绿素指数(GCI)检测叶锈病的试验。其他测量包括近冠层温度、粮食产量和感染类型和严重程度的目测筛查。显著正相关(p <;0.001;2019年4月中旬,大学城粮食产量与3种植被指数(VIs)的相关性R2 = 0.42-0.62。在大学城,叶锈病的严重程度与2020年4月中旬的衰老阶段相吻合。VIs与籽粒产量没有关系。2020年4月中旬叶锈病侵染高发期,VIs呈显著负相关(p <;0.05;R2 = 0.27)与Castroville的粮食产量有关。这三个VIs与College Station的冠层温度呈显著的线性负相关(p <;0.05;R2 = 0.3-0.34)和Castroville (p <;0.001;R2 = 0.52-0.54)。在高叶锈病严重程度下,2019年和2020年两个地点GCI的可重复性都低于NDVI和NDRE。如果多种因素同时影响冬小麦,结果可能会有所不同。
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来源期刊
Crop Science
Crop Science 农林科学-农艺学
CiteScore
4.50
自引率
8.70%
发文量
197
审稿时长
3 months
期刊介绍: Articles in Crop Science are of interest to researchers, policy makers, educators, and practitioners. The scope of articles in Crop Science includes crop breeding and genetics; crop physiology and metabolism; crop ecology, production, and management; seed physiology, production, and technology; turfgrass science; forage and grazing land ecology and management; genomics, molecular genetics, and biotechnology; germplasm collections and their use; and biomedical, health beneficial, and nutritionally enhanced plants. Crop Science publishes thematic collections of articles across its scope and includes topical Review and Interpretation, and Perspectives articles.
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