Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.

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Having a system to measure food consumption is important to establish whether individual nutritional needs are being met in order to act quickly and to minimize the risk of undernutrition. Here, we tested a smartphone-based food consumption assessment system named FoodIntech. FoodIntech, which is based on AI using deep neural networks (DNN), automatically recognizes food items and dishes and calculates food leftovers using an image-based approach, i.e., it does not require human intervention to assess food consumption. This method uses one-input and one-output images by means of the detection and synchronization of a QRcode located on the meal tray. The DNN are then used to process the images and implement food detection, segmentation and recognition. Overall, 22,544 situations analyzed from 149 dishes were used to test the reliability of this method. The reliability of the AI results, based on the central intra-class correlation coefficient values, appeared to be excellent for 39% of the dishes (n = 58 dishes) and good for 19% (n = 28). The implementation of this method is an effective way to improve the recognition of dishes and it is possible, with a sufficient number of photos, to extend the capabilities of the tool to new dishes and foods.

International audienceThis paper describes an agent oriented framework supporting bio-inspired mechanisms which takes profit of the intrinsic hardware parallelism of the pervasive platform developed within the Perplexus IST European project. The proposed framework is a flexible and modular means to describe and simulate complex phenomena such as biologically plausible neural networks or culture dissemination. Associated to this framework and based on the multiprocessor architecture of the Perplexus platform nodes, a tool suite capable of accelerating parallelizable agents is described. Therefore, this contribution combines the software flexibility of agent-based programming with the efficiency of multiprocessor hardware execution. This framework has been successfully tested with two experiments: a proof of concept application made of robots that autonomously improve their behaviours according to their environment and a spiking neural network simulation. These results prove that the framework and its associated methodology are relevant in the context of the simulation of complex phenomena