Plant health is a pillar of global food security and ecosystem sustainability, yet it is increasingly threatened by climate change and water-resource depletion:drought and soil salinization constrain crop yields through complex morpho-physiological responses, especially in Mediterranean regions. Meeting futurefood demand while reducing agrochemical use, in line with the European Green Deal, requires a shift from reactive to proactive and predictive plant-healthmanagement. Current monitoring approaches, however, remains a bottleneck: traditional techniques are often destructive or limited in resolution, emergingwearables are rigid, heavy, and opaque, and most measure isolated organ properties without capturing inter-organ interactions or coupling with theenvironment.
MOSAIC overcomes these limitations through minimally invasive wearable sensors and imaging technologies enabling continuous, multi-organ monitoring ofplant responses across scales — from local tissue to whole-plant dynamics — in relation to environmental conditions. It addresses three main challenges(CHs): developing non-invasive, long-term stable wearables compatible with living tissues (CH1); integrating responses across organs and scales (CH2);and establishing synchronized, long-term multimodal monitoring for predictive interpretation (CH3).
To this end, the project develops a platform (i.e. the MOSAIC platform) built upon three Key Enabling Technologies: i) adaptive plant-integrated wearable(bio)sensors for growth, water content, sap flow, and VOC signalling; ii) non-contact imaging for canopy and root observation; and an IoT infrastructure forsynchronized, longitudinal multimodal acquisition.
The platform is validated on olive trees (Olea europaea L.), representative of Mediterranean agroecosystems and sensitive to drought and salinity, throughcontrolled-environment experiments and field deployment in olive groves.
Four specific objectives, organized into five Milestones over 36 months, guide a five-unit interdisciplinary consortium spanning sensing, materials science,FEM modelling, plant science, agronomy, IoT, and data analysis.
The originality of MOSAIC lies not in any single device but in the systematic unification of complementary dimensions (contact and non-contact sensing,localized and canopy-scale observation, and the biochemical, structural, and physiological state of the plant together with its surroundings) within onesynchronized, longitudinal, experimentally validated architecture.
By enabling early detection of stress anomalies and coordinated whole-plant monitoring, MOSAIC will support more efficient irrigation, fertilization, and crop protection, advancing sustainable agriculture. It will generate new knowledge on coordinated stress-response mechanisms, openly released time-resolveddatasets, and a framework extensible to biotic stress and pathogen detection, of strong relevance for the Mediterranean olive value chain.
MOSAIC overcomes these limitations through minimally invasive wearable sensors and imaging technologies enabling continuous, multi-organ monitoring ofplant responses across scales — from local tissue to whole-plant dynamics — in relation to environmental conditions. It addresses three main challenges(CHs): developing non-invasive, long-term stable wearables compatible with living tissues (CH1); integrating responses across organs and scales (CH2);and establishing synchronized, long-term multimodal monitoring for predictive interpretation (CH3).
To this end, the project develops a platform (i.e. the MOSAIC platform) built upon three Key Enabling Technologies: i) adaptive plant-integrated wearable(bio)sensors for growth, water content, sap flow, and VOC signalling; ii) non-contact imaging for canopy and root observation; and an IoT infrastructure forsynchronized, longitudinal multimodal acquisition.
The platform is validated on olive trees (Olea europaea L.), representative of Mediterranean agroecosystems and sensitive to drought and salinity, throughcontrolled-environment experiments and field deployment in olive groves.
Four specific objectives, organized into five Milestones over 36 months, guide a five-unit interdisciplinary consortium spanning sensing, materials science,FEM modelling, plant science, agronomy, IoT, and data analysis.
The originality of MOSAIC lies not in any single device but in the systematic unification of complementary dimensions (contact and non-contact sensing,localized and canopy-scale observation, and the biochemical, structural, and physiological state of the plant together with its surroundings) within onesynchronized, longitudinal, experimentally validated architecture.
By enabling early detection of stress anomalies and coordinated whole-plant monitoring, MOSAIC will support more efficient irrigation, fertilization, and crop protection, advancing sustainable agriculture. It will generate new knowledge on coordinated stress-response mechanisms, openly released time-resolveddatasets, and a framework extensible to biotic stress and pathogen detection, of strong relevance for the Mediterranean olive value chain.