Big data technology (BDT) is a new technological paradigm that is driving the entire economy, including low-tech industries such as agriculture where it is implemented under the banner of precision farming (PF).
BDT builds on geo-coded maps of agricultural fields and the real-time monitoring of activities on the farm in order to increase the efficiency of resource use, reduce the uncertainty of management decisions. Under PF, yield is increased due particularly to the precise selection and application of exact types and doses of agricultural inputs (crop varieties, fertilizers, pesticides, herbicides, irrigation water) for optimum crop growth and development.
European farming system represents a mixture of small and bigger farms. In order for this pilot to account for both small and bigger farms, agriculture data to be inputted into the big data analytics system will be gathered on a finer and a larger scale. The finer scale is tailored to both farm sizes but with a particular focus on farms with more financial resources such as big farms or cropping systems with high net returns per hectare of land. Finer scale data are mainly collected from proximal sensors, while larger scale data will be mainly derived from earth observation (EO) and include agriculturally relevant information collected using remote sensing technologies and earth surveying techniques, and from data coming from agriculture machinery.
EO and finer scale information will be used through big data analytics (WP4) to monitor and assess the status of, and changes in, the agriculture pilots implemented in this project all across European Union.
Big data analytics system will then provide pilot managers with highly localized descriptive (better and more advanced way of looking at an operation), prescriptive (timely recommendations for operation improvement i.e., seed, fertilizer and other agricultural inputs application rates, soil analysis, and localized weather and disease/pest reports, based on real-time and historical data) and predictive plans (use current and historical data sets to forecast future localized events and returns).
Structure of the agriculture pilots
A. Precision Horticulture including vine and olives
A1. Precision agriculture in olives, fruits, grapes and vegetables
A1.1. Precision agriculture in olives, fruits, grapes (links: definition, intermediate results, final report)
A1.2. Precision agriculture in vegetable seed crops (links: definition, intermediate results, final report)
A1.3. Precision agriculture in vegetables -2 (Potatoes) (links: definition, intermediate results, final report)
A2. Big Data management in greenhouse eco-systems
B. Arable Precision Farming
B1. Cereals and biomass crops
B1.1. Cereals and biomass crops (links: definition, intermediate results, final report)
B1.2. Cereals and biomass and cotton crops 2 (links: definition, intermediate results, final report)
B1.3. Cereals and biomass crops 3 (links: definition, intermediate results, final report)
B1.4. Cereals and biomass crops 4 (links: definition, intermediate results, final report)
B2. Machinery management and environmental issue
C. Subsidies and insurance
C2. CAP support
Forest pilots will aim to:
- Identify automatically forest health and damages such as snow, thunder storms (wind), dryness, rains and fires, from satellite images; production of maps of implemented cuttings for monitoring purposes.
- Optimization of tree resources through detailed characterization of trees using tools including airborne laser scanning (knottiness, carvery), and smart trees assignment i.e., which trees go to saw mills, pulp/paper, textiles, biofuels etc, in order to match offer and demand.
- Integrated tools are developed and new management plans are implemented that take into account non-wood products and conservation areas while at the same time maximizing timber production and economic yield. This represents a step forward for most European countries where traditional methods for forest management are based on “static” management plans, created at the planting stage
Structure of the forestry pilots
A. Multisource and data crowdsourcing /e-services
A1. Easy data sharing and networking
A2. Monitoring and control tools for forest owners
B. Forest Health / Remote sensing
B1. Forest damage remote sensing
B2. Invasive alien species control – plagues – forest management
2.3.2-FH. Monitoring of forest health (links: definition, intermediate results, final report)
2.3.2-IAS. Invasive alien species control and monitoring (links: definition, intermediate results, final report)
C. Forest data management services
C1. Web-mapping service for the government decision making
C2. Shared multiuser forest data environment
According to the Food and Agricultural Organization (FAO), the world’s marine fisheries expanded continuously to a production peak of 86.4 million tons in 1996, but have since exhibited a general declining trend. Global recorded production was 82.6 million tons in 2011 and 79.7 million tons in 2012. The fraction of assessed stocks fished within biologically sustainable levels 6 has exhibited a decreasing trend, declining from 90 percent in 1974 to 71.2 percent in 2011.
According to the World Bank and the FAO, fisheries are an underperforming global asset. It is estimated that its production could be increased by $50 billion per year, if one could achieve better management and less overcapitalization of the fishing fleets. This means that a further growth in the blue economy mainly will arise from better management of the sea resources, reduction of fisheries effort and increased value of caught fish.
The fisheries pilots will focus on the small pelagic fisheries in the North Atlantic Ocean and the tropical tuna fisheries. The areas encompassed by these pilots have an annual capture production above 13 million tons.
In addition to fuel consumption, costs and downtime associated with maintenance and breakdown are important for both vessel economy and environmental impact. This pilot will develop technologies to improve vessel energy efficiency and engine preventive maintenance by providing support for operational choices such as:
- Vessel loading (In order to reduce the hull resistance and reduce fuel consumption);
- Weather routing (Reduce fuel consumption by taking weather conditions into account);
- Condition based maintenance (Proactive maintenance based on machinery sensors) ;
- Optimization of the order the fish aggregating devices (FADs) are visited;
- Provide the crew and ship owners with information which benefits fisheries planning based on best suitable fishing grounds, combined with the best suitable fishing methods and tools.
On the other hand, the fishery pilot will:
- demonstrate that the combination of available information from existing sources, such as catch reports, oceanographic measurements, oceanographic simulations, stock simulations and stock observations, can be used for improving assessment of fish stocks and their distribution. Part of these data will be derived from remote sensing, while others will be collected using vessels equipped with appropriate sensors and communication and communication tools.
- Provide information for predicting the development of various market segments, so that the fisheries may be targeted against the most beneficial fisheries; the consumer get informed on environmental impact footprint of fishery products so he can make informed purchasing decision.
The structure of the pilots is shown in the table below:
A: Fishing vessels immediate operational choices
A1: Oceanic tuna fisheries immediate operational choices (links: definition, intermediate results, final report)
A2: Small pelagic fisheries immediate operational choices (links: definition, intermediate results, final report)
B: Fishing vessel trip and fisheries planning
C: Fisheries sustainability and value
C1: Pelagic fish stock assessments (links: definition, intermediate results, final report)
C2: Small pelagic market predictions and traceability (links: definition, intermediate results, final report)