RECREO

RECREO
Resource-Efficient and Climate-change REsilient hazelnut Orchard

Project Name: RECREO – Resource-Efficient and Climate-change REsilient hazelnut Orchard

Acronym: RECREO

Consortium

 Sigma Consulting Srl (capofila)
 Terrasystem Srl
 Università degli Studi della TUSCIA – Dipartimento di Scienze Agrarie e Forestali (DAFNE)
 Università degli Studi ROMA TRE – Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche (ICITA)

Project Description

The RECREO project aims to develop a Decision Support System (DSS) for certain agronomic aspects of hazelnut orchards, including:
1. Rational irrigation management based on the crop’s actual needs in relation to its phenological stage and expected seasonal production.
2. Development of “early detection” systems for primary nutritional deficiencies to promote sustainable orchard nutrition management, enabling site-specific and cultivar-specific fertilization protocols for hazelnut and a significant reduction in nutrient runoff from the orchard ecosystem.
These objectives will be pursued by integrating data collected from IoT instruments installed on-site (weather stations, soil moisture sensors, AgroCam for phenology observation, and Sap Flow sensors to measure sap flow), along with remote data from multispectral optical satellite sensors (Sentinel 2 and Landsat 8), thermal infrared radiance (Landsat 8 and MODIS), and Synthetic Aperture Radar (SAR from Sentinel1). Additionally, key moments in hazelnut phenology will involve data collection using remote and proximal sensing, such as ultra-high spatial and radiometric resolution sensors mounted on drones (Unmanned Aerial Vehicle – UAV).
The data will be processed to represent key Soil-Vegetation-Atmosphere (SVAT) variables, such as soil moisture, crop vigor, nutritional status, and actual evapotranspiration, as well as remote tracking of irrigation activities. Satellite data will be processed using radiative transfer models, energy balance simulation, and numerical inversion methods, including artificial intelligence-based approaches, to estimate important crop status variables, such as leaf and canopy water content, chlorophyll content, Leaf Area Index (LAI), and actual evapotranspiration (ETa).
All information will be collected, stored, and organized in a database to provide a comprehensive description of the system’s state. This state will be evaluated by DSS models to determine recommendations for irrigation and fertilization interventions addressing potential nutritional deficiencies. These models will use innovative machine learning, statistical data processing, and signal processing on images, videos, and field data. Specifically:
i) For defining the predictive model for irrigation recommendations, methods ranging from system theory for “model-based” representation to machine learning for “model-free” representation will be evaluated.
ii) For defining the “early detection” system for primary nutritional deficiencies in hazelnut, solutions based on machine learning techniques will be analyzed.

Details

Grant

Lazio Region, POR-FESR Lazio 2021-2027, Public Notice “Competitive Repositioning RSI,” Area 2 “Blue Economy, Green Economy, and Agrifood,” approved by Executive Determination No. G18823 of 28/12/2022, published in Lazio Regional Bulletin No. 108 on 29/12/2022

 

 

 

Funding Contribution

€403,278.88
Project approved for funding with Determination No. G14867 of 09/11/2023, published in Lazio Regional Bulletin No. 93 – Supplement No. 1 on 21/11/2023

Duration of the project
18 months