Christian Hillnhütter

Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases.

Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases.

Neue Ansätze zur frühzeitigen Erkennung und Lokalisierung von Zuckerrübenkrankheiten

ZusammenfassungZeitgemäße Methoden im Pflanzenbau und Pflanzenschutz stehen im engen Zusammenhang mit der Nutzung moderner Technologien. Nah- und Fernerkundungsmethoden, wie hyper- und multispektrale Sensoren oder Thermografie, eröffnen für den Präzisionspflanzenschutz vielfältige Möglichkeiten, die landwirtschaftliche Produktion effizienter und umweltfreundlicher zu gestalten. Am Modell der Zuckerrübe und ihrer Pathogene, bodenbürtige Pilze, Nematoden und Blattpathogene, werden derzeit Untersuchungen zum Einsatz nicht invasiver Sensoren mit dem Schwerpunkt auf folgenden Fragestellungen durchgeführt: Ist eine frühzeitige Erkennung des Befalls durch Pathogene möglich? Können die Schadursachen sensorisch differenziert werden?AbstractModern methods in plant production and crop protection are closely related to modern technologies. Near-range and remote sensing, like hyper- and multispectral sensors or thermography, in precision pest management possess multiple opportunities to increase the productivity of agricultural production systems and do them more environmentally acceptable. Experiments are carried out on sugar beet plants and their pathogens to investigate the use of imaging and non-imaging hyperspectral sensors referring to the following questions: Is early detection of infection by pathogens possible? What is the potential to differentiate damage causing organisms?

Remote Sensing for the Detection of Soil-Borne Plant Parasitic Nematodes and Fungal Pathogens

This chapter reviews past developments and the present state-of-the-art remote sensing for the detection of soil-borne nematodes and plant pathogens . Nematodes and soil-borne pathogens are considered ideal targets for the application of precision agriculture with non-contact sensing methodologies. The clustered occurrence and low level of mobility of nematodes and pathogens in the soil and the induction of symptoms in the leaves make them perfect targets for remote sensing detection. Data obtained with infrared thermography and hyperspectral reflectance for the remote sensing of plant parasitic nematodes and root rotting fungi in sugar beet as well as delineation of complex-disease interactions is also presented. The management of these two pest groups usually relies on full field pesticide treatments, even when only a small section of the field is infested. This underscores the need for remote sensing of disease clusters and the resulting application of site-specific management .

Influence of different levels of resistance or tolerance in sugar beet cultivars on complex interactions between Heterodera schachtii and Rhizoctonia solani

The existence of synergistic interactions between the beet cyst nematode Heterodera schachtii and Rhizoctonia crown and root rot of Rhizoctonia solani (Anastomosis Group 2-2IIIB) in concomitant treatments was investigated on susceptible, tolerant and resistant sugar beet varieties. The influence of sequential inoculation of the two organisms was also analysed. Leaf reflectance measurements were made with a hyperspectral spectroradiometer to monitor symptom development of the concomitant disease interaction as compared with the effects of the disease organisms present singly in both studies. The results demonstrated that a significant interaction exists between H. schachtii and R. solani on sugar beet. However, the three susceptible cultivars responded differently to the concomitant treatments. The susceptible cultivar showed synergistic levels of damage when simultaneously inoculated with H. schachtii and R. solani . Synergistic effects were also detected in the concomitant treatments on the R. solani tolerant cultivar. Conversely, damage was lower on the H. schachtii resistant cultivar in the concomitant treatments. Heterodera schachtii reproduction was negatively affected and R. solani development positively influenced on the susceptible and tolerant cultivars in the concomitant treatments when compared with the individual controls. Sequential inoculation of sugar beet seedlings caused higher levels of disease when compared with the two organisms inoculated singly. Leaf reflectance gave reproduced results in the detection of Rhizoctonia crown and root rot disease development. Normalised Difference Vegetation Index (NDVI) values of leaf reflectance showed high correlations to plant and visual disease symptom rating variables over the duration of the experiments. The NDVI allowed disease severity detection without damage to the plant. The results demonstrated that hyperspectral reflectance can be used effectively to monitor aetiopathology of R. solani and may be an effective tool for early detection of Rhizoctonia crown of root rot symptoms in the field, in breeding programme tests and for the early prediction of yield impact.

Synergistic damage by interactions between Ditylenchus dipsaci and Rhizoctonia solani (AG 2–2IIIB) on sugar beet

The aim of this study was to investigate interactions between Ditylenchus dipsaci and Rhizoctonia solani. Both pathogens are known to cause problems in the primary sugar beet production areas in Germany. Furthermore, the organisms’ ecological niches in the soil and on the beet overlap. Hence, it is probable that these parasites interact and have a deleterious synergistic impact on sugar beet production. The stem and bulb nematode, D. dipsaci, is a migratory endoparasite that penetrates the sugar beet seedling during the spring when temperatures are low. The main symptoms include distorted, bloated petioles and leaves. The fungus causing Rhizoctonia crown and root rot, R. solani, enters the plant at the beet-leaf transition zone. Synergistic damage was obtained when both organisms occurred on the same plant. Hyperspectral leaf reflectance data was used to calculate a vegetation index, the Normalised Difference Vegetation Index (NDVI), which could successfully be used to discriminate between growth reduction caused by R. solani and by dual inoculation (disease complex). High correlations were observed between ratings of disease symptoms and the vegetation index over a time series of seven weeks.

Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.