Erich-Christian Oerke

Recent advances in sensing plant diseases for precision crop protection

Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach—including plant pathology, engineering, and informatics—is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.

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.

Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography.

ABSTRACT Disease progress of downy mildew on cucumber leaves, caused by the obligate biotrophic pathogen Pseudoperonospora cubensis, was shown to be associated with various changes in transpiration depending on the stage of pathogenesis. Spatial and temporal changes in the transpiration rate of infected and noninfected cucumber leaves were visualized by digital infrared thermography in combination with measurements of gas exchange as well as microscopic observations of pathogen growth within plant tissue and stomatal aperture during pathogenesis. Transpiration of cucumber leaf tissue was correlated to leaf temperature in a negative linear manner (r = -0.762, P < 0.001, n = 18). Leaf areas colonized by Pseudoperonospora cubensis exhibited a presymptomatic decrease in leaf tem perature up to 0.8 degrees C lower than noninfected tissue due to abnormal stomata opening. The appearance of chlorosis was associated with a cooling effect caused by the loss of integrity of cell membranes leading to a larger amount of apoplastic water in infected tissue. Increased water loss from damaged cells and the inability of infected plant tissue to regulate stomatal opening promoted cell death and desiccation of dying tissue. Ultimately, the lack of natural cooling from necrotic tissue was associated with an increase in leaf temperature. These changes in leaf temperature during downy mildew development resulted in a considerable heterogeneity in temperature distribution of infected leaves. The maximum temperature difference within a thermogram of cucumber leaves allowed the discrimination between healthy and infected leaves before visible symptoms appeared.

Global crop production and the efficacy of crop protection - current situation and future trends

Actual and potential crop losses of eight major food and cash crops have been estimated by evaluating data from literature and field experiments. Total losses were calculated from yield reductions due to pathogens, animal pests and weeds on a regional, continental and global level. Since 1965, worldwide production of most crops has increased considerably. Simultaneously, crop losses in wheat, potatoes, barley and rice increased by 4 to 10 percent, in maize, soybean, cotton and coffee losses remained unchanged or slightly decreased. The efficacy of crop protection practices was calculated as the percentage of potential losses prevented by control. The efficacy is highest in cotton (55 percent), it reaches only 34 to 38 percent in the food crops rice, wheat and maize. The variability among cropping areas is high: In Western Europe, 61 percent of potential crop losses is prevented, in North America and Oceania 44, in all other regions 38 percent. Due to the small share of Western Europe in worldwide production of 8 percent, the efficacy of actual crop protection worldwide is only 40 percent.In view of population growth and rising food demand crop production has to be increased substantially. As potential loss rates often increase with attainable yields high productivity largely depends on effective crop protection management. Scenarios for the production of food crops by the year 2025 in developed and in developing countries are given. Recent and future developments in crop protection can contribute to establish sustainability in agriculture and to preserve natural resources. However, although effective control methods have been developed for most biotic yield constraints, the use of crop protection products is regulated by economic considerations rather than by food demand.

Precision Crop Protection - the Challenge and Use of Heterogeneity

Precision farming is an agricultural management system using global navigation satellite systems, geographic information systems, remote sensing, and data management systems for optimizing the use of nutrients, water, seed, pesticides and energy in heterogeneous field situations. This book provides extensive information on the state-of-the-art of research on precision crop protection and recent developments in site-specific application technologies for the management of weeds, arthropod pests, pathogens and nematodes. It gives the reader an up-to-date and in-depth review of both basic and applied research developments. The chapters discuss I) biology and epidemiology of pests, II) new sensor technologies, III) applications of multi-scale sensor systems, IV) sensor detection of pests in growing crops, V) spatial and non-spatial data management, VI) impact of pest heterogeneity and VII) precise mechanical and chemical pest control.

Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images

Understanding the response dynamics of plants to biotic stress is essential to improve management practices and breeding strategies of crops and thus to proceed towards a more sustainable agriculture in the coming decades. In this context, hyperspectral imaging offers a particularly promising approach since it provides non-destructive measurements of plants correlated with internal structure and biochemical compounds. In this paper, we present a cascade of data mining techniques for fast and reliable data-driven sketching of complex hyperspectral dynamics in plant science and plant phenotyping. To achieve this, we build on top of a recent linear time matrix factorization technique, called Simplex Volume Maximization, in order to automatically discover archetypal hyperspectral signatures that are characteristic for particular diseases. The methods were applied on a data set of barley leaves (Hordeum vulgare) diseased with foliar plant pathogens Pyrenophora teres, Puccinia hordei and Blumeria graminis hordei. Towards more intuitive visualizations of plant disease dynamics, we use the archetypal signatures to create structured summaries that are inspired by metro maps, i.e. schematic diagrams of public transport networks. Metro maps of plant disease dynamics produced on several real-world data sets conform to plant physiological knowledge and explicitly illustrate the interaction between diseases and plants. Most importantly, they provide an abstract and interpretable view on plant disease progression.

Oils for Increased Efficacy of Metarhizium anisopliae to Control Whiteflies

The efficacy of Metarhizium anisopliae in combination with sublethal concentrations of oils and potassium-oleate for biological control of whiteflies was tested under controlled conditions. Three commercial products (Biola ® , Naturen ® , Neudosan ® ) and five experimental formulations of plant oils were tested. The efficacy of M. anisopliae against Trialeurodes vaporariorum and Bemisia tabaci without additives was about 50%. At 1/20 of their recommended dosages, all compounds tested significantly increased the efficacy of M. anisopliae for the control of T. vaporariorum , with the formulated sunflower oil Biola ® giving the highest synergistic effect, reaching nearly 100% control. Not only was the level of control increased but also the speed of action was improved, resulting in a higher reliability of control. Three of seven additives showed no effects on the viability of conidia on the leaf surface, whereas the formulations of the other oils and oleates reduced the longevity of spores. The synergistic effect of Biola resulted from the more even distribution of M. anisopliae conidia on leaves and insects. Other positive effects of oils on the efficacy of M. anisopliae are discussed in relation to an extended spectrum of environmental conditions and pests to be controlled.

Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet

Belowground symptoms of sugar beet caused by the beet cyst nematode (BCN) Heterodera schachtii include the development of compensatory secondary roots and beet deformity, which, thus far, could only be assessed by destructively removing the entire root systems from the soil. Similarly, the symptoms of Rhizoctonia crown and root rot (RCRR) caused by infections of the soil-borne basidiomycete Rhizoctonia solani require the same invasive approach for identification. Here nuclear magnetic resonance imaging (MRI) was used for the non-invasive detection of belowground symptoms caused by BCN and/or RCRR on sugar beet. Excessive lateral root development and beet deformation of plants infected by BCN was obvious 28 days after inoculation (dai) on MRI images when compared with non-infected plants. Three-dimensional images recorded at 56 dai showed BCN cysts attached to the roots in the soil. RCRR was visualized by a lower intensity of the MRI signal at sites where rotting occurred. The disease complex of both organisms together resulted in RCRR development at the site of nematode penetration. Damage analysis of sugar beet plants inoculated with both pathogens indicated a synergistic relationship, which may result from direct and indirect interactions. Nuclear MRI of plants may provide valuable, new insight into the development of pathogens infecting plants below- and aboveground because of its non-destructive nature and the sufficiently high spatial resolution of the method.

Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants

In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we “wordify” the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.