Jean-Pierre da Costa

Jean-Pierre da Costa is a Professor at Université de Bordeaux, affiliated with the Institut Polytechnique de Bordeaux and the IMS (Integration of Material to System) Laboratory. His research focuses on signal and image processing with applications in precision agriculture and plant disease detection, particularly within the MOTIVE team of the Signal and Image Processing research group.

Dr. da Costa's research spans computer vision, deep learning, and proximal sensing technologies applied to agricultural challenges. He specializes in developing automated systems for crop monitoring, disease diagnosis, and precision farming operations. His work bridges theoretical image processing with practical agricultural solutions, with significant contributions to grapevine disease detection (particularly Flavescence Dorée and downy mildew), mechanical weeding systems, and vineyard monitoring technologies. The research combines algorithm development with real-world field validation, often involving multi-year experimental studies across different grape varieties and crop types.

His publication record shows a consistent trajectory toward increasingly sophisticated AI-driven agricultural solutions. Early work focused on fundamental image processing techniques, while recent publications demonstrate integrated systems combining proximal sensing, deep learning, and real-time decision making. The research spans both fundamental materials science (pyrocarbon nanostructure analysis) and practical agricultural applications, showing remarkable interdisciplinary breadth. A common thread is the development of robust, field-deployable technologies that operate effectively in challenging outdoor conditions.

Dr. da Costa collaborates extensively with agricultural researchers, viticulturists, and industry partners including STMicroelectronics, Stellantis, and various French research institutions. His work often involves multi-year projects with practical implementations, such as the BIPBIP project (Bloc-outil et Imagerie de Précision pour le Binage Intra-rang Précoce) funded by ANR. The MOTIVE team he leads develops complete solutions from image acquisition through to actionable agricultural insights, with several publications describing systems that operate in real-time on low-power embedded platforms suitable for field deployment.