Rationalizing the use of chemicals in the field to optimize resources and reduce environmental impact.
Drones: mapping and spectral indices (NDVI, LAI) as support for the development of prescription maps.
Preserving the chemical and physical characteristics of the soil, ensuring its quality, and increasing biodiversity.
MULTISPECTRAL IMAGES
We integrate data from satellites (hyperspectral) and surveys conducted in the field by our specialized drones (multispectral). Following the processing of these images, it is possible to visualize useful indicators of the plant's health: NDVI (Normalized Difference Vegetation Index) shows the difference in vigor of various areas of the vineyard, while NDWI (Normalized Difference Water Index) is an index of water stress. These indicators are displayed in the form of maps that are easily interpretable by the human eye, thanks to selected color scales.
DRONES
Drones are the most effective means to obtain a precise, high-resolution view of what is happening in the field.
Our certified UAV operators safely pilot drones of various sizes, from the 900-gram commercial DJI Mavic Pro to larger
aircraft, up to 1 meter in diameter. HERMES-00 is a hexacopter designed and built in collaboration with the
Department
of Industrial Engineering (DII) at the University of Trento. It features a geolocation system
with RTK correction that, interfacing with the territory's base stations, guarantees
centimetric-level accuracy.
We use different types of cameras, both RGB and multispectral, to gather valuable information about
the health and mapping of the field.
FIELD MAPPING
Our image processing systems, combined with the precision GPS of our drones, allow
us to develop not only vegetative indices (NDVI) but also RGB 3D models and field or plot perimeters.
From these georeferenced data, it is possible to create prescription maps: the field is divided into smaller
areas based on the actual amount of water or fertilizer required. Knowing the right quantity to apply to each zone
makes it easy to efficiently manage resources, reduce waste, and save costs.
The extreme importance of mapping one's own land, giving spatial and temporal value to the data
acquired daily, becomes understandable. Only in this way the winemaker can modulate every intervention according
to the actual needs.
SENSORS
The installation of field sensors, both meteorological and more specific for vine monitoring, is a fundamental
activity for innovation and "digitalization" in agriculture.
Sensors allow us to collect a large amount of data useful for assessing the health status of the crop
and subsequently for planning targeted interventions.
Racemus.ai leverages data acquired from a network of low-power wireless sensors designed
and integrated by our technicians. It includes sensors of various types:
- Weather stations.
- Soil moisture and temperature sensors.
- eaf wetness and temperature sensors.
The data is accessible on our platform, along with an always-available historical record.
We also use historical data for the development of predictive models.
PREDICTIVE MODELS
Predictive models not only provide important decision support to winemakers and agronomists
but also help implement targeted interventions to save resources and safeguard the crop.
The combination of data from various sources, along with knowledge of the state of the art
in viticulture, has allowed our Data Science experts to develop customized algorithms capable of predicting:
- The development stage of the vines (phenological phases).
- The risk of downy mildewpathogen development.
Predictive models are integrated with alert systems (notifications and dedicated dashboards)
that allow the farmer to prepare the right interventions before the event occurs.
Contact us for further information.
Write to us at the address info@racemus.ai.
Racemus.ai was born from a research project funded by the Autonomous Province of Trento, with the goal
of combining advanced technologies and tools with traditional vine cultivation methods.
At the center of it all is the winemaker, who can benefit both in terms of field management and decision support.
Among the main activities are environmental monitoring and water control in the wine sector, aimed at increasing
agricultural sustainability and product quality.
The project has seen important collaborations with local entities, including the University of Trento and the
Edmund Mach Foundation, a research, training, and experimentation center in the agricultural and environmental field.
The home of these studies is Trentino, a land dedicated to viticulture with over 10,000 hectares of vineyards.
See our GIT repository for more details.