Dataset for: Towards the high-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato

This dataset corresponds to a field trial experiment carried out in the facilities of CIP-San Ramon between September 2021 and February 2022 with the aim: i) to assess LAMP accuracy in virus load determination compared with quantitative PCR, and ii) to associate temporal remotely sensed data to susceptible, tolerant, and resistant genotypes to combined infection by SPVD utilizing Machine Learning algorithms. Three SPVD (SPFMV, SPCLV, and SPCSV) were evaluated in twelve genotypes subjected to three treatments: healthy control, infected by graft, and natural infected. For this, spatial data at canopy level recovered from multispectral and thermal images were obtined in 4 diferent phenological stage during the growing season. Also symtom score, total virus load, carbon isotopic discrimination and total biomass yield were measurement.

该数据集对应于2021年9月至2022年2月期间在CIP-圣拉蒙（CIP-San Ramon）设施开展的田间试验，旨在：i) 评估环介导等温扩增技术（LAMP）在病毒载量测定中的准确性，并与定量聚合酶链式反应（quantitative PCR）进行比较；ii) 利用机器学习（Machine Learning）算法，将时间序列遥感数据与对甘薯病毒病复合侵染（SPVD）表现为易感、耐受及抗性的基因型相关联。本研究对12个基因型进行了三种处理（健康对照、嫁接感染及自然感染），并评估了三种甘薯病毒病相关病毒（SPFMV、SPCLV及SPCSV）的侵染情况。试验期间，在生长季的4个不同物候期，从多光谱及热成像数据中获取了冠层水平的空间数据；同时测定了症状评分、总病毒载量、碳同位素判别值及总生物量产量。