Early disease detection is a major challenge in horticulture. Integrated Pest Management (IPM) (Stern et al., 1959;Altner et al., 1977) combines prophylactic, biological and physical methods to fight bioagressors of crops while minimizing the use of pesticides. This approach is particularly promising in the context of ornamental crops in greenhouses because of the high level of control needed in such agrosystems. However, IPM requires frequent and precise observations of plants (mainly leaves) which are not compatible with production constraints.Our goal is early detection of bioagressors. In this paper, we present a strategy based on advances in automatic interpretation of images applied to leaves of roses scanned in situ. We propose an cognitive vision system that combines image processing, learning and knowledge-based techniques. This system is illustrated with automatic detection and counting of a white fly (Trialeurode Vaporariorum Westwood) at mature stage. We have compared our approach with manual methods and our results showed that automatic processing is reliable. Special attention was paid to low infestation cases which are crucial to agronomic decisions.

Manuscript AbstractNumerical modelling is now used routinely to make predictions about the behaviour of environmental systems. Model calibration remains a critical step in the modelling process and different approaches have been taken to develop guidelines to support engineers and scientists in this task. This article reviews currently available guidelines for a river hydraulics modeller by dividing them into three types: on the calibration process, on hydraulic parameters, and on the use of hydraulic simulation codes. The article then presents an integration of selected guidelines within a knowledge-based calibration support system. A prototype called CaRMA-1 (Calibration of River Model Assistant) has been developed for supporting the calibration of models based on a specific 1D code. Two case studies illustrate the ability of the prototype to face operational situations in river hydraulics engineering, for which both data quality and quantity are not sufficient for an optimal calibration. Using CaRMA-1 allows the modeller to achieve the calibration task in accordance with good calibration practice implemented in the knowledge base. Relevant reasoning rules can easily be added to the knowledge base to extend the prototype range of applications. This study thus provides a framework for building operational support tools from various types of existing engineering guidelines.

This work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations.

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Abstract-In software product line (SPL) engineering, a software configuration can be obtained through a valid selection of features represented in a feature model (FM). With a strong separation between requirements and reusable components and a deep impact of high level choices on technical parts, determining and configuring an well-adapted software configuration is a long, cumbersome and error-prone activity. This paper presents a modeling process in which variability sources are separated in different FMs and inter-related by propositional constraints while consistency checking and propagation of variability choices are automated. We show how the variability requirements can be expressed and then refined at design time so that the set of valid software configurations to be considered at runtime may be highly reduced. Software tools support the approach and some experimentations on a video surveillance SPL are also reported.

This paper summarizes the experience of teaching objectoriented modeling and UML for more than a decade to various audiences (academic or corporate, software developers or not). We present the characteristics of the audiences. We investigate which modeling concepts are well accepted and which are not. We identify a number of general problems: background of attendees, object-oriented language as a prerequisite, limitations of tools, methodological issues, impact of company organizations. We propose (partial) solutions and a list of pragmatic advices.