Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex

被引:99
作者
Di Gennaro, Salvatore F. [1 ]
Battiston, Enrico [2 ]
Di Marco, Stefano [3 ]
Facini, Osvaldo [3 ]
Matese, Alessandro [1 ]
Nocentini, Marco [2 ]
Paliotti, Alberto [4 ]
Mugnai, Laura [2 ]
机构
[1] CNR, Ist Biometeorol IBIMET, Via G Caproni 8, I-50145 Florence, Italy
[2] Univ Florence, Sez Patol Vegetale & Entomol, Dipartimento Sci Prod Agroalimentari & Ambiente D, Piazzale Cascine 28, I-50144 Florence, Italy
[3] CNR, Ist Biometeorol IBIMET, Via Gobetti 101, I-40129 Bologna, Italy
[4] Univ Perugia, Dipartimento Sci Agr & Ambientali, Borgo XX Giugno 74, I-06128 Perugia, Italy
关键词
precision viticulture; disease detection; asymptomatic plant; trunk disease; VITIS-VINIFERA L; SPECTRAL REFLECTANCE; CHLOROPHYLL FLUORESCENCE; PHENOLIC-COMPOUNDS; LEAVES; STRESS; IDENTIFICATION; PHOTOGRAPHY; DEFICIENCY; SEVERITY;
D O I
10.14601/Phytopathol_Mediterr-18312
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Foliar symptoms of grapevine leaf stripe disease (GLSD, a disease within the esca complex) are linked to drastic alteration of photosynthetic function and activation of defense responses in affected grapevines several days before the appearance of the first visible symptoms on leaves. The present study suggests a methodology to investigate the relationships between high-resolution multispectral images (0.05 m/ pixel) acquired using an Unmanned Aerial Vehicle (UAV), and GLSD foliar symptoms monitored by ground surveys. This approach showed high correlation between Normalized Differential Vegetation Index (NDVI) acquired by the UAV and GLSD symptoms, and discrimination between symptomatic from asymptomatic plants. High-resolution multispectral images were acquired during June and July of 2012 and 2013, in an experimental vineyard heavily affected by GLSD, located in Tuscany (Italy), where vines had been surveyed and mapped since 2003. Each vine was located with a global positioning system, and classified for appearance of foliar symptoms and disease severity at weekly intervals from the beginning of each season. Remote sensing and ground observation data were analyzed to promptly identify the early stages of disease, even before visual detection. This work suggests an innovative methodology for quantitative and qualitative analysis of spatial distribution of symptomatic plants. The system may also be used for exploring the physiological bases of GLSD, and predicting the onset of this disease.
引用
收藏
页码:262 / 275
页数:14
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