Summary
CERBERUS will capitalize on early detection to reduce pesticide use and on intelligent spray applications to sustainably eradicate pests at their early stages, when damage is reduced and the impact of treatments is high even with low spray rates. The project will combine large-scale crop observation models enhanced by the high revisiting time and diversity of bands offered by Copernicus, with the high reliability of proximal sensing granted by IoT insect traps reporting at a daily basis, robot-based monitoring at less than 1 m from the crops, and the granularity resulting from citizen data using dedicated apps. Data coming from multiple sources will be merged through a cloud platform, which will produce risk maps and spraying recommendations for users by applying AI algorithms. CERBERUS will be the outcome of a multi-actor approach by co-creating and sharing knowledge with all the different type of actors along the whole project. Cerberus will be validated for three quarantine pests (F. dorée, X. fastidiosa, B. dorsalis) and three commonly managed pests (L. botrana, B. oleae, C. capitata), and for the three most important specialty crops in the Mediterranean basin: wine-production vineyards, olive oil producing orchards, and citrus plantations. The multi-actor approach will be strengthened by the complementarity of the consortium: three academic partners, three technology companies, one citizen science specialist, a government agency involved in crop protection, and five end-users from Italy, Cyprus and Spain, providing two pilot plots per country and target crop. The proposed concept and methodology of CERBERUS has the potential to deploy an innovative crop surveillance system, enhanced by early detection in high-value crops to firmly step ahead in the effective application of sustainable phytosanitary measures and in the co-creation of crop protection policies.
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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/101134878 |
Start date: | 01-01-2024 |
End date: | 31-12-2027 |
Total budget - Public funding: | 4 891 830,00 Euro - 4 891 830,00 Euro |
Cordis data
Original description
CERBERUS will capitalize on early detection to reduce pesticide use and on intelligent spray applications to sustainably eradicate pests at their early stages, when damage is reduced and the impact of treatments is high even with low spray rates. The project will combine large-scale crop observation models enhanced by the high revisiting time and diversity of bands offered by Copernicus, with the high reliability of proximal sensing granted by IoT insect traps reporting at a daily basis, robot-based monitoring at less than 1 m from the crops, and the granularity resulting from citizen data using dedicated apps. Data coming from multiple sources will be merged through a cloud platform, which will produce risk maps and spraying recommendations for users by applying AI algorithms. CERBERUS will be the outcome of a multi-actor approach by co-creating and sharing knowledge with all the different type of actors along the whole project. Cerberus will be validated for three quarantine pests (F. dorée, X. fastidiosa, B. dorsalis) and three commonly managed pests (L. botrana, B. oleae, C. capitata), and for the three most important specialty crops in the Mediterranean basin: wine-production vineyards, olive oil producing orchards, and citrus plantations. The multi-actor approach will be strengthened by the complementarity of the consortium: three academic partners, three technology companies, one citizen science specialist, a government agency involved in crop protection, and five end-users from Italy, Cyprus and Spain, providing two pilot plots per country and target crop. The proposed concept and methodology of CERBERUS has the potential to deploy an innovative crop surveillance system, enhanced by early detection in high-value crops to firmly step ahead in the effective application of sustainable phytosanitary measures and in the co-creation of crop protection policies.Status
SIGNEDCall topic
HORIZON-CL6-2023-GOVERNANCE-01-16Update Date
12-03-2024
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