Watt Michael S, Bartlett Michael, Soewarto Julia, de Silva Dilshan, Estarija Honey Jane C, Massam Peter, Cajes David, Yorston Warren, Graevskaya Elizaveta, Dobbie Kiryn, Fraser Stuart, Dungey Heidi S, Buddenbaum Henning
Scion, 10 Kyle St., Christchurch 8011, New Zealand.
Scion, P.O. Box 3020, Rotorua 3010, New Zealand.
Phytopathology. 2023 Aug;113(8):1405-1416. doi: 10.1094/PHYTO-02-23-0078-R. Epub 2023 Oct 6.
Myrtle rust, caused by the fungus , is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants () that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (F1 score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatal conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (F1 score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
由该真菌引起的桃金娘锈病是一种严重病害，会影响许多桃金娘科物种。繁殖桃金娘科物种的商业苗圃易患桃金娘锈病，因此需要一种可靠的方法，以便在病害出现肉眼可见症状之前及早期进行检测。本研究使用了时间序列热成像以及从81株蒲桃植株（）上采集的可见到短波红外光谱测量数据，这些植株要么接种了桃金娘锈病病菌，要么保持无病状态，数据采集期为10天。利用这些数据，研究目标为：（i）使用热指数和窄带高光谱指数（NBHI），通过来自较老的抗性绿叶和较嫩的易感红叶的数据，对桃金娘锈病进行肉眼可见症状出现前及早期检测的模型准确性进行量化；（ii）识别用于病害检测的最重要的NBHI和热指数。通过对验证数据集进行预测，利用热成像得出的指数构建的模型能够在症状出现前一天（1 DBS）以及早期症状出现后的所有阶段，完美地（F1分数 = 1.0；准确率 = 100%）从感染植株中区分出对照植株。与对照植株相比，感染桃金娘锈病的植株具有更低且变化更大的归一化冠层温度，这与更高的气孔导度和蒸腾作用相关。利用从绿叶得出的NBHI，在症状出现前3天、2天和1天（F1分数范围 = 0.89至0.94）实现了出色的肉眼可见症状出现前分类。在病害发展的肉眼可见症状出现前及早期阶段对桃金娘锈病进行准确表征，表明可以在苗圃环境中开发出一种强大的检测方法。[公式：见正文] 版权所有© 2023作者。本文是一篇根据知识共享署名4.0国际许可协议分发的开放获取文章。
