Productivity of crops grown for human consumption is at risk due to the incidence of pests, especially weeds, pathogens and animal pests. Crop losses due to these harmful organisms can be substantial and may be prevented, or reduced, by crop protection measures. An overview is given on different types of crop losses as well as on various methods of pest control developed during the last century.Estimates on potential and actual losses despite the current crop protection practices are given for wheat, rice, maize, potatoes, soybeans, and cotton for the period 2001–03 on a regional basis (19 regions) as well as for the global total. Among crops, the total global potential loss due to pests varied from about 50% in wheat to more than 80% in cotton production. The responses are estimated as losses of 26–29% for soybean, wheat and cotton, and 31, 37 and 40% for maize, rice and potatoes, respectively. Overall, weeds produced the highest potential loss (34%), with animal pests and pathogens being less important (losses of 18 and 16%). The efficacy of crop protection was higher in cash crops than in food crops. Weed control can be managed mechanically or chemically, therefore worldwide efficacy was considerably higher than for the control of animal pests or diseases, which rely heavily on synthetic chemicals. Regional differences in efficacy are outlined. Despite a clear increase in pesticide use, crop losses have not significantly decreased during the last 40 years. However, pesticide use has enabled farmers to modify production systems and to increase crop productivity without sustaining the higher losses likely to occur from an increased susceptibility to the damaging effect of pests.The concept of integrated pest/crop management includes a threshold concept for the application of pest control measures and reduction in the amount/frequency of pesticides applied to an economically and ecologically acceptable level. Often minor crop losses are economically acceptable; however, an increase in crop productivity without adequate crop protection does not make sense, because an increase in attainable yields is often associated with an increased vulnerability to damage inflicted by pests.

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.

Pathogenesis of Pseudoperonospora cubensis causing downy mildew of cucumber resulted in changes in the metabolic processes within cucumber leaves including the transpiration rate. Due to the negative correlation between transpiration rate and leaf temperature, digital infrared thermography permitted a non-invasive monitoring and an indirect visualization of downy mildew development. Depending on the stage of pathogenesis and the topology of chloroses and necroses, infection resulted in a typical temperature pattern. Spatial heterogeneity of the leaf temperature could be quantified by the maximum temperature difference (MTD) within a leaf. The MTD increased during pathogenesis with the formation of necrotic tissue and was related to disease severity as described by linear and quadratic regression curves. Under controlled conditions, changes in temperature of infected leaves allowed the discrimination between healthy and infected areas in thermograms, even before visible symptoms of downy mildew appeared. Environmental conditions during thermographic measurement, in particular air temperature and humidity, as well as water content and age of the leaf influenced the temperature of its surface. Conditions enhancing the transpiration rate facilitated the detection of changes in leaf temperature of infected leaves at early stages of infection. As modified by environmental conditions, MTD alone is not suitable for the quantification of downy mildew severity in the field.

The detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods. A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 – 700 nm) and near infrared (700 – 1000 nm) range 3 to 14 days after inoculation. Data analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes. With this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.