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.

Grapevine leafroll disease (GLD) is considered one of the most widespread grapevine virus diseases, causing severe economic losses worldwide. To date, six grapevine leafroll-associated viruses (GLRaVs) are known as causal agents of the disease, of which GLRaV-1 and -3 induce the strongest symptoms. Due to the lack of efficient curative treatments in the vineyard, identification of infected plants and subsequent uprooting is crucial to reduce the spread of this disease. Ground-based hyperspectral imaging (400–2500 nm) was used in this study in order to identify white and red grapevine plants infected with GLRaV-1 or -3. Disease detection models have been successfully developed for greenhouse plants discriminating symptomatic, asymptomatic, and healthy plants. Furthermore, field tests conducted over three consecutive years showed high detection rates for symptomatic white and red cultivars, respectively. The most important detection wavelengths were used to simulate a multispectral system that achieved classification accuracies comparable to the hyperspectral approach. Although differentiation of asymptomatic and healthy field-grown grapevines showed promising results further investigations are needed to improve classification accuracy. Symptoms caused by GLRaV-1 and -3 could be differentiated.

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.

Background: 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. Results: 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. Conclusions: 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.

Soils in perennial cropping systems, such as vineyards, have good prospects for storing carbon since less management is required with minimum disturbance to the soil that might prevent rapid turnover of organic matter. In addition, incorporation of organic matter into the subsoil instead of conventional topsoil application might increase its resistance to decomposition through physical isolation and the buildup of organo-mineral complexes. However, the stability of organic matter in agricultural land could also be highly dependent on individual systems, soil properties and climatic conditions.In our study, the stability of high carbon organic materials (i.e., compost and a Terra Preta-like material) after deep (30-60 cm) incorporation into the soil of a vineyard in western Germany was investigated with respect to greenhouse gas emissions. Portable gas analyzers were used for long-term in-situ monitoring of greenhouse gas emissions. Additional parameters quantified were soil redox potential using Pt electrodes and the concentration of greenhouse gases in the pore space of the soil using air samplers.The deeply incorporated soil organic amendments showed good stability with respect to N2O and CH4 emission, whereas 30.4% and 51.7% of the compost and the Terra Preta-like material, respectively, was decomposed and released as atmospheric CO2 after two years of observation. Oxygen availability at different soil depths throughout the sampling period, indicated by redox potential values of 300 to 700 mV, played a role in the turnover of organic matter in the treatments. Higher CO2 concentration in the treatments in the deeper soil layer (30-50 cm) compared to the control was also consistent with higher CO2 emission at the soil surface.To investigate the site-specific influence on the stability of organic matter, the emission of greenhouse gases will also be quantified in different vineyards at different locations with similar management.  

Background: 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.Results: 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.Conclusions: In this study, different 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.