The rapid development of image-based phenotyping methods based on ground-operating devices or unmanned aerial vehicles (UAV) has increased our ability to evaluate traits of interest for crop breeding in the field. A field site infested with beet cyst nematode (BCN) and planted with four nematode susceptible cultivars and five tolerant cultivars was investigated at different times during the growing season. We compared the ability of spectral, hyperspectral, canopy height-and temperature information derived from handheld and UAV-borne sensors to discriminate susceptible and tolerant cultivars and to predict the final sugar beet yield. Spectral indices (SIs) related to chlorophyll, nitrogen or water allowed differentiating nematode susceptible and tolerant cultivars (cultivar type) from the same genetic background (breeder). Discrimination between the cultivar types was easier at advanced stages when the nematode pressure was stronger and the plants and canopies further developed. The canopy height (CH) allowed differentiating cultivar type as well but was much more efficient from the UAV compared to manual field assessment. Canopy temperatures also allowed ranking cultivars according to their nematode tolerance level. Combinations of SIs in multivariate analysis and decision trees improved differentiation of cultivar type and classification of genetic background. Thereby, SIs and canopy temperature proved to be suitable proxies for sugar yield prediction. The spectral information derived from handheld and the UAV-borne sensor did not match perfectly, but both analysis procedures allowed for discrimination between susceptible and tolerant cultivars. This was possible due to successful detection of traits related to BCN tolerance like chlorophyll, nitrogen and water content, which were reduced in cultivars with a low tolerance to BCN. The high correlation between SIs and final sugar beet yield makes the UAV hyperspectral imaging approach very suitable to improve farming practice via maps of yield potential or diseases. Moreover, the study shows the high potential of multi-sensor and parameter combinations for plant phenotyping purposes, in particular for data from UAV-borne sensors that allow for standardized and automated high-throughput data extraction procedures.

From 2017 to 2020 an extensive monitoring for bacterial and viral yellowing diseases was carried out in southern and central Germany. The monitoring recorded for the first time the infestation of sugar beets with yellowing viruses and the disease „Syndrome Basses Richesses“ (SBR). To map the yellowing virus infestation, samples were examined for the presence of several virus species (BYV, BtMV, poleroviruses). The disease SBR was investigated in this study using the more common γ-3 proteobacterium “Candidatus Arsenophonus phytopathogenicus”. In this study samples were chosen, which showed yellowing symptoms. The coordination of the sampling was carried out by the Association of Hessian-Palatinate Sugar Beet Growers. Results clearly show the extent of the heavily infested area from SBR to southern Hesse, Rhine-Hesse and Franconia. The spread of SBR can be explained by the migration of the leafhopper Pentastiridius leporinus. Furthermore, the regional and parallel spread of mixed infections of both yellowing diseases was shown for the first time, which probably contributed to the strong sugar yield losses observed in practice. Causes and effects of mixed infections of both yellowing diseases require further research. Over the four-year study period, a continuous increase in SBR infections was observed. Therefore, the need for development of appropriate management systems to control SBR is very high.

The rapid spread of the bacterial yellowing disease Syndrome des Basses Richesses (SBR) has a major impact on sugar beet (Beta vulgaris) cultivation in Germany, resulting in significant yield losses. SBR-causing bacteria are transmitted by insects, mainly the Cixiid planthopper Pentastiridius leporinus. However, little is known about the biology of this emerging vector, including its life cycle, oviposition, developmental stages, diapauses, and feeding behavior. Continuous mass rearing is required for the comprehensive analysis of this insect. Here we describe the development of mass rearing techniques for P. leporinus, allowing us to investigate life cycle and ecological traits, such as host plant choice, in order to design agronomic measures that can interrupt the life cycle of nymphs in the soil. We also conducted field studies in recently-infected regions of Rhineland-Palatinate and south Hesse, Germany, to study insect mobility patterns and abundance at four locations during two consecutive years. The soil-depth monitoring of nymphs revealed the movement of the instars through different soil layers. Finally, we determined the prevalence of SBR-causing bacteria by designing TaqMan probes specific for two bona fide SBR pathogens: Candidatus Arsenophonus phytopathogenicus (Gammaproteobacteria) and Candidatus Phytoplasma solani (stolbur phytoplasma). Our data suggest that P. leporinus is spreading northward and eastward in Germany, additionally, the abundance of SBR-carrying planthoppers is increasing. Interestingly, P. leporinus does not appear to hibernate during winter, and is polyphagous as a nymph. Stolbur phytoplasma has a significant impact on SBR pathology in sugar beet.