THE GOAL OF THIS COORDINATED PROJECT IS TO PROVIDE THE KNOWLEDGE, CONCEPTS AND TECHNOLOGICAL TOOLS REQUIRED IN THE DEVELOPMENT OF INTEGRATED WEED MANAGEMENT SYSTEMS (IWMS). THE PROJECT IS STRUCTURED AROUND TWO SCENARIOS: THE DRY-LAND CEREAL PRODUCTION SYSTEMS PREVAILING IN CENTRAL SPAIN AND THE VINEYARD SYSTEMS MORE WIDELY USED IN THESE SAME AREAS, BASED ON THE USE OF DEFICIT IRRIGATION. IN BOTH SCENARIOS WE WILL DEVELOP REMOTE SENSING TECHNOLOGIES BASED ON THE USE OF UNMANNED AERIAL VEHICLES (UAV) EQUIPPED WITH MULTISPECTRAL SENSORS FOR WEED DETECTION AND DISCRIMINATION AS WELL AS GROUND VEHICLES FOR PROXIMAL WEED DETECTION/DISCRIMINATION USING ARTIFICIAL VISION TECHNOLOGIES. MORE SPECIFICALLY, THIS SUBPROJECT IS FOCUSED ON THE PROXIMAL SENSING (DETECTION EQUIPMENT ONBOARD GROUND VEHICLES) AND AIMS FOR THE DESIGN AND THE DEVELOPMENT OF A SYSTEM CAPABLE OF DETECTING, CHARACTERIZING AND RECOGNIZING/CLASSIFYING BY USING AN OPEN ARCHITECTURE ABLE TO INTEGRATE DIFFERENT SENSORS AS WELL AS DIFFERENT DETECTION AND RECOGNITION METHODS. THE GENERAL PURPOSE METHODS WILL BE DESIGNED AND DEVELOPED FOR THE EXPECTED SCENARIOS (DRY-LAND CEREAL Y VINEYARD SYSTEMS). FURTHERMORE, THE SYSTEM WILL INCLUDE ARCHITECTURES TO THE INTEGRATION/MANAGEMENT OF THE METHODS AND THE DATA IN ORDER TO ADJUST THE SYSTEM BEHAVIOUR TO EVERY SPECIFIC SITUATION. THE RECOGNITION METHODS AS WELL AS THE INTEGRATION/MANAGEMENT ARCHITECTURES WILL BE BASED ON PARADIGMS OF ARTIFICIAL INTELLIGENCE SUCH AS MACHINE LEARNING. THE PROPOSED TECHNOLOGY WILL ALLOW, AMONG OTHERS, TO OBTAIN 2D AND 3D CROP MAPS. THIS INFORMATION WILL BE COMPLEMENTARY TO THE DATA PROVIDED BY THE REMOTE SENSING, GIVING ANOTHER POINT OF VIEW OF THE SCENE THAT WILL REDUCE THE UNCERTAINTY DUE TO THE OCCLUSIONS AND SHADOWS IN THE NADIR REGION PRODUCED BY EXTERNAL OBJECTS TO THE SCENE. THE PROXIMAL INFORMATION AS WELL AS THE REMOTE ONE, WILL BE THE BASIS FOR BUILDING MAPS IN ORDER TO TREAT LOCALLY THE INFESTED AREAS AND, THEREFORE, DEVELOPED TECHNOLOGIES WILL BE A KEY ELEMENT IN THE COORDINATED PROJECT.