Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize
Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize
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DOI：
10.3389/fpls.2019.00552
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发表时间：
2019-04-30
影响因子：
5.6
通讯作者：
Boddupalli, Maruthi Prasanna
中科院分区：
文献类型：
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作者：
Loladze, Alexander;Augusto Rodrigues, Francelino, Jr.;Boddupalli, Maruthi Prasanna
焦油斑复合体（TSC）是中南美洲玉米主要叶面病害之一，由至少两种真菌病原菌Phyllachora maydis和Monographella maydis引起。2015年在美利坚合众国也发现了梅氏单胞虫，此后该病原体在该国玉米种植区传播。尽管遥感技术越来越多地用于植物表型分析，但它们尚未应用于玉米的TSC抗性表型分析。在本研究中，利用无人机（UAV）测试了两个作物季玉米在病压和无病条件下的几种多光谱植被指数（VIs）和热成像。在病害胁迫下，籽粒产量、营养指数（MCARI2）与冠层温度之间存在密切关系。TSC疾病进展曲线下面积与3个营养指标（RDVI、MCARI1和MCARI2）之间也存在较强的相关性。此外，我们还证明，在最敏感的玉米杂交品种中，TSC可能导致高达58%的产量损失。我们的研究结果表明，本研究中测试的RS技术可以用于玉米TSC抗性的高通量表型分析，并有可能用于玉米其他叶面疾病的表型分析。这可能有助于减少开发改良玉米种质所需的成本和时间。讨论了利用RS技术进行抗病表型分析的挑战和机遇。
Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in themaize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed.