Evaluation of Sensor-based Early Detection Methods for Grapevine Diseases like Palatinate grapevine yellows, Bois noir, Grapevine leafroll disease and Esca

Während ihrer Standzeit über mehrere Jahrzehnte kann die Weinrebe (Vitis vinifera ssp. vinifera) von einer Vielzahl unterschiedlicher Pathogene infiziert werden, wovon einige in der Rebe verbleiben und sich so im Laufe der Zeit ansammeln. Derzeit stehen nur prophylaktische Ansätze zum Schutz vor vielen dieser endogenen Krankheiten zur Verfügung. Diese Maßnahmen beinhalten beispielsweise visuelle Bonituren im Weinberg und anschließendes Roden infizierter Reben oder Pathogennachweise in Rebvermehrungsanlagen, die dazu beitragen sollen gesundes Pflanzmaterial zu produzieren. Der Einsatz von Sensor-basierten Verfahren zur Krankheitsdetektion kann hierzu einen entscheidenden Beitrag leisten. Hyperspektrale Sensoren erfassen objektiv und nicht-invasiv die Reflektion von Pflanzen im visuellen Bereich des Lichts (400 – 700 nm) sowie im Nahinfrarot- (700 – 1000 nm) und kurzwelligen Infrarot-Bereich (1000 – 2500 nm). Biochemische und biophysikalische Änderungen, die durch Pathogenbefall induziert werden, führen zu Abweichungen in den Reflektionsspektren, welche mit Hilfe verschiedener Machine und Deep Learning Modelle analysiert und Infektionen dadurch frühzeitig erkannt werden können. Im Rahmen dieser Arbeit wurde die Eignung der Hyperspektralanalyse zur Erkennung der Rebkrankheiten FD Pfalz (PGY) und Schwarzholzkrankheit (BN), welche durch Phytoplasmen hervorgerufen werden, sowie der Viruserkrankung Blattrollkrankheit (GLD) und der durch Pilze induzierten Esca Krankheit evaluiert. Anhand von Gewächshauspflanzen konnten unter kontrollierten Bedingungen Detektionsmodelle für BN und PGY erstellt werden, die bis zu 96% der Pflanzen korrekt als gesund oder infiziert klassifizierten. Die Erkennung von infizierten, aber nicht symptomatischen Pflanzen bedarf allerdings noch weiterer Versuche. Da die Symptomentwicklung beider Krankheiten stark von Umgebungsfaktoren abhängig ist, wurden zusätzlich Triebe verschiedener Rebsorten aus dem Freiland analysiert. Auch hier konnten sehr gute Klassifikationsgenauigkeiten von bis zu 100% erzielt werden, was die Vermutung nahelegt, dass beide Krankheiten auch direkt im Feld detektiert werden können. Die Erkennung von GLD erfolgte zunächst ebenfalls an Gewächshauspflanzen. Dabei konnten 83 – 100% der symptomatischen Reben und 85 – 100% der infizierten, aber nicht symptomatischen Reben von Kontrollpflanzen unterschieden werden. Darüber hinaus wurden im Zeitraum von 2016 bis 2018 rund 500 Reben im Freiland näher untersucht. Auch hier wurden ähnlich hohe Klassifikationsgenauigkeiten erzielt. Außerdem konnte das Potential der Hyperspektralanalyse zur Erkennung infizierter, aber nicht symptomatischer Reben im Freiland gezeigt werden. Da die Ergebnisse jedoch sehr stark zwischen den Versuchsjahren schwankten, bedarf es weiterer Untersuchungen zur abschließenden Bewertung dieses Aspekts. Darüber hinaus konnten Symptome der beiden GLD-Krankheitserreger Grapevine leafroll-associated virus-1 und Grapevine leafroll-associated virus-3 erfolgreich voneinander unterschieden werden. Auch die Erkennung von Esca wurde über drei Jahre hinweg im Feld durchgeführt. Hyperspektrale Detektionsmodelle konnten erfolgreich sowohl für originale Felddaten als auch für manuell annotierte Daten erstellt werden. Erste Ergebnisse zeigten zudem deutlich das Potential der präsymptomatischen Detektion. Des Weiteren wurde die Übertragbarkeit der Modelle auf unbekannte Daten simuliert. Dabei zeigte sich, dass die Modelle zwar von einem Jahr auf ein anderes Jahr übertragen werden können, die Modellperformanz dabei jedoch deutlich nachlässt. Basierend auf den Hyperspektraldaten wurden für alle Krankheiten und alle Analyseansätze die wichtigsten Wellenlängen ermittelt, um eine Vereinfachung des komplexen Systems zu ermöglichen. Anhand dieser Wellenlängen könnten schließlich multispektrale Sensoren entwickelt werden, die schneller, günstiger und flexibler einsetzbar sind als hyperspektrale Anwendungen. Im Fall von Esca wurden bereits im Rahmen dieser Arbeit zusätzlich Multispektraldaten mittels Drohne erhoben und mit dem simulierten Ansatz aus den Hyperspektraldaten verglichen. Obwohl die simulierten Multispektraldaten sehr gute Ergebnisse erzielten und somit das Potential dieser Methode aufzeigen, bleibt die Luft-gestützte Krankheitserkennung derzeit noch eine Herausforderung.Grapevine (Vitis vinifera ssp. vinifera) as a perennial crop is typically grown over several decades. During their lifespan, vines may be infected by a number of different pathogens, some of which remain inside the vine and thus accumulate over time. At the moment, only prophylactic measures are available to reduce the spread of many endogenic diseases, which include visual ratings and subsequent uprooting of infected grapevines in the field or mandatory pathogen tests in nurseries to provide healthy planting material. Sensor-based approaches could significantly contribute to early disease diagnosis. Hyperspectral sensors detect plants’ reflection objectively and non-invasively in the visible range of light (400 – 700 nm) but also in the near infrared (700 – 1000 nm) and short-wave infrared region (1000 – 2500 nm). Biochemical and biophysical changes induced by pathogen infestation cause deviations in the reflectance spectra, which can be analyzed by different machine and deep learning models enabling disease detection at early infection stages. In this study, the suitability of ground-based hyperspectral analyses for the detection of the grapevine phytoplasma diseases Palatinate grapevine yellows (PGY) and Bois noir (BN), the virus infection grapevine leafroll disease (GLD), and the fungal Esca disease has been evaluated. Disease detection models for both BN and PGY could be developed under controlled conditions using greenhouse plants. These models were able to classify plants correctly as either healthy or infected with an accuracy of up to 96%. However, identification of infected but symptomless plants needs further improvements. Since symptoms of both diseases may vary strongly depending on environmental factors, shoots collected in the field from different cultivars were also analyzed. Again, high classification accuracies of up to 100% could be achieved leading to the assumption that both diseases might also be detectable directly in the field. GLD detection was at first tested using different greenhouse plants. Thereby, 83 – 100% of symptomatic and 85 – 100% of infected but symptomless vines could be correctly identified. Moreover, approximately 500 grapevines were analyzed directly in the field during the years 2016 – 2018 leading to similar classification accuracies. Furthermore, the potential of hyperspectral analyses for the in-field detection of infected but symptomless vines could be shown. However, results strongly differed between experimental years, therefore, further analyses are necessary for a final evaluation of this aspect. Moreover, symptoms caused by the two main GLD agents Grapevine leafroll-associated virus-1 and Grapevine leafroll-associated virus-3 could successfully be discriminated. Esca disease detection was also performed directly in the field during three consecutive years. Thereby, hyperspectral detection models could successfully be established for original field data as well as for manually annotated data. In addition, first results clearly showed the potential of pre-symptomatic disease detection. Moreover, model transferability to unknown data was tested but remains challenging and will require to include further experimental years. Based on hyperspectral data, most important wavelengths were determined for every disease and every analysis approach in order to simplify this complex system. Multispectral sensors could eventually be developed using these wavelengths being faster, cheaper and more flexible than a hyperspectral application. In the case of Esca, additional airborne multispectral data were acquired during this study and compared to a multispectral simulation based on hyperspectral approaches. Although, the simulated multispectral data achieved good results, thus, showing the potential of this method, airborne disease detection needs to be improved

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

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

Phenoliner: A multi-sensor field phenotyping platform

Because of the perennial nature and size of grapevine, the acquisition of phenotypic data is mostly restricted to the vineyard. The Phenoliner, presented here, is a new type of ground-based, robust, field phenotyping platform. Following the concept of a movable tunnel, the vehicle is based on a grape harvester. It is equipped with different sensor systems within the tunnel (multi-camera system, hyperspectral cameras) and on top of the vehicle (RTK-GPS, orientation and speed sensors). Through an artificial light source in the tunnel, it is independent of external light conditions. In combination with the artificial background, the Phenoliner allows standardized acquisition of high-quality, geo-referenced sensor data. The multi-camera system is used for the automated acquisition of coloured 3D data of multiple vine rows for the automated calculation of yield parameters that can be used for yield prediction. The hyperspectral cameras are used to detect spectral data in a broad range of spectral bands covering a spectrum from 400 to 2500 nm to evaluate, for example, the health status. The Phenoliner allows fast, robust, and precise screening of grapevines for several traits. The platform described can be extended by additional sensors at any given time

Phenoliner: A new field phenotyping platform for grapevine research

In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data