Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging

Abstract

Grapevine yellows (GY) are serious phytoplasma-caused diseases affecting viticultural areas worldwide. At present, two principal agents of GY are known to infest grapevines in Germany: Bois noir (BN) and Palatinate grapevine yellows (PGY). Disease management is mostly based on prophylactic measures as there are no curative in-field treatments available. In this context, sensor-based disease detection could be a useful tool for winegrowers. Therefore, hyperspectral imaging (400–2500 nm) was applied to identify phytoplasma-infected greenhouse plants and shoots collected in the field. Disease detection models (Radial-Basis Function Network) have successfully been developed for greenhouse plants of two white grapevine varieties infected with BN and PGY. Differentiation of symptomatic and healthy plants was possible reaching satisfying classification accuracies of up to 96%. However, identification of BN-infected but symptomless vines was difficult and needs further investigation. Regarding shoots collected in the field from different red and white varieties, correct classifications of up to 100% could be reached using a Multi-Layer Perceptron Network for analysis. Thus, hyperspectral imaging seems to be a promising approach for the detection of different GY. Moreover, the 10 most important wavelengths were identified for each disease detection approach, many of which could be found between 400 and 700 nm and in the short-wave infrared region (1585, 2135, and 2300 nm). These wavelengths could be used further to develop multispectral systems.

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

“…Several recent studies, using different sensors, have confirmed the potential of hyperspectral data to detect plant pathologies in a reliable manner in various pathosystems associated with grapevines, such as leafroll-associated virus-3 [22,28,29], grapevine trunk disease [30][31][32], Flavescence dorèe [33][34][35], and powdery mildew [36]. However, to date, there are no available data concerning grapevine root rot disease identification through hyperspectral images, thus making this work the pioneer.…”

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

“…This makes the data collection, storage, and transferring not trivial and, more importantly, the analysis process to mine valuable information from the samples is challenging [19]. In this sense, machine learning techniques have been successfully used to analyze hyperspectral images [24] and deep learning models recently became popular to identify and classify pest and disease levels in agricultural fields [25][26][27][28][29]. For example, multilayer perceptron neural networks can be used with these same goals as they represent a powerful deep learning tool for high-performance modeling of complex problems and have been proven efficient in hyperspectral pest image classification [30].…”

Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard

“…Conventional IP-based methods have focused on the use of color, spectral, and texture information and filters to differentiate disease infections from healthy leaves and canopies. These methods have achieved good performance with advanced imaging modalities such as multispectral, hyperspectral (Bendel et al, 2020;Nguyen et al, 2021), and fluorescent imaging (Latouche et al, 2015). These methods are usually computationally efficient and provide pixel-level infection masks for infection severity calculation, but they need to be used concurrently with costly sensors and have limited generalizability to unseen datasets, presenting challenges of the model deployment in real world applications.…”