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Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.

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We introduce a novel set of features for a challenging image analysis task in agriculture where cell phone camera images of beet leaves are analyzed as to the presence of plant diseases. Aiming at minimal computational costs on the cellular device and highly accurate prediction results, we present an efficient detector of potential disease regions and a robust classification method based on texture features. We evaluate several first-and second-order statistical features for classifying textures of leaf spots and we find that a combination of descriptors derived on multiple erosion bands of the RGB color channels, as well as, the local binary patterns of gradient magnitudes of the extracted regions accurately distinguish between symptoms caused by five diseases, including infections of the fungi Cercospora beticola, Ramularia beticola, Uromyces betae, and Phoma betae, and the bacterium Pseudomonas syringae pv. aptata.

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Integrating 3rd party components in software systems provides promising advantages but also risks due to disconnected evolution cycles. Deciding whether to migrate to a newer version of a 3rd party component integrated into selfimplemented code or to switch to a different one is challenging. Dedicated evolution support for 3rd party component scenarios is hence required. Existing approaches do not account for open source components which allow accessing and analyzing their source code and project information. The approach presented in this paper combines analyses for code dependency, code quality, and bug tracker information for a holistic view on the evolution with 3rd party components. We applied the approach in a case study on a communication middleware component for industrial devices used at ABB. We identified 7 methods potentially impacted by changes of 3rd party components despite the absence of interface changes. We further identified selfimplemented code that does not need any manual investigation after the 3rd party component evolution as well as a positive trend of code and bug tracker issues.