Bendel Nele, Kicherer Anna, Backhaus Andreas, Klück Hans-Christian, Seiffert Udo, Fischer Michael, Voegele Ralf T, Töpfer Reinhard
Institute for Grapevine Breeding, Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Geilweilerhof, 76833 Siebeldingen, Germany.
Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Straße 5, 70599 Stuttgart, Germany.
Plant Methods. 2020 Oct 21;16:142. doi: 10.1186/s13007-020-00685-3. eCollection 2020.
Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging.
Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult.
In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.
诸如葡萄枝干病害（GTDs）中的埃斯卡病是葡萄栽培面临的最具毁灭性的威胁之一。由于缺乏有效的预防和治疗方法，埃斯卡病在全球范围内造成了严重的经济损失。由于症状并非连续出现，因此很难评估葡萄园里这种病害的实际发生率。所以，需要进行年度监测。在此背景下，症状的自动检测对葡萄种植者而言可能是一大解脱。光谱传感器已被证明在病害检测方面很成功，能够进行无损、客观且快速的数据采集。本研究的目的是评估连续三年使用地面高光谱和航空多光谱成像技术在田间检测叶片埃斯卡病症状的可行性。
利用原始田间数据或人工标注数据成功开发了高光谱病害检测模型。下一步，将这些模型应用于植株尺度。虽然使用标注数据的模型在开发过程中表现更好，但在实际应用中，使用原始数据的模型显示出更高的分类准确率。此外，还测试了病害检测模型对未知数据的可转移性。尽管可见和近红外（VNIR）波段显示出了有前景的结果，但此类模型的转移具有挑战性。初步结果表明，可以在症状出现前检测到外部症状，但这需要进一步评估。此外，通过识别用于区分任务的最重要波长，模拟了一种特定应用的多光谱方法，然后将其与实际多光谱数据进行比较。尽管基于地面的多光谱病害检测取得了成功，但航空检测仍然困难。
本研究介绍了用于检测叶片埃斯卡病症状的地面高光谱和航空多光谱方法。两种传感器系统似乎都适用于田间病害检测，尽管航空数据采集还需进一步优化。我们的病害检测方法有助于监测葡萄园中的植物表型。
