The objective of the ATLANTIDE project is the complete integration of knowledge and enabling technologies for the definition and implementation of agricultural production models that aim to efficiently combine inputs (water, fertilizers, pesticides, energy, time) with outputs (increasing efficiency, improving quality, reducing production losses, reducing resource use, reducing land use, reducing ecological footprint). The project foresees the realization of an enabling Framework for the manipulation, aggregation and fusion of agro-food field data that allows the development of Precision Agriculture in a European context.
Through the development of advanced technologies, an innovative paradigm of farm management (agricultural, forestry and zootechnical) is to be realized that, based on observation and measurement, is able to elaborate the response to the set of inter- and intra-field quantum-qualitative variables that intervene in the production system with the aim of optimizing yields in view of an advanced climate and environmental, economic, productive and social sustainability.
The project intends to achieve the following objectives
- Precision phenotyping of specific plant materials by obtaining detailed metabolomic profiles at single plant level under field conditions. The complex of the information obtained will allow the identification of genotype/crop technique combinations able to induce particular metabolic profiles in plants.
- Quantification and evaluation of plant biodiversity and agro-pastoral productivity and other eco-system services, and design of innovative and site-specific agronomic management and monitoring systems. The RS tools will be applied for monitoring the management of pasture improvement practices (leguminous infestation, fertilisation).
- Differentiated and optimized management of crops on the basis of new knowledge, acquired through a monitoring protocol of environmental phenomena, carried out with precision farming techniques and capable of responding to individual crop management needs.
- Design of sensor systems, integrated with the automation of greenhouse environment management activities, in order to improve the management of resources, with a consequent reduction of environmental impact and a more regular morphophysiological development of plants under cultivation, thanks to the reduction of environmental and nutritional stress causes and the prevention of major diseases.
- Design and management of an automated system for individual food administration and a system for early diagnosis of pregnancy in small ruminants.
- Use of innovative sensors derived from classification algorithms (Deep and Machine Learning) that installed directly on the agricultural tractor, on robot ground farm or on drones, transmit the information obtained, (physiological state of the plant, presence of biotic and abiotic stress, vegetative responses to different cultivation practices, monitoring and execution of phytosanitary treatments, monitoring and cluster counting, etc…) in order to prepare an innovative and efficient decision support system for farms, aimed at the sustainability of cultivation practices, grape quality control and management of oenological objectives.