by Mirco Boschetti (IREA-CNR) and Erwin Schoitsch (AIT)
“Smart Anything, Everywhere” is the new hype around IoT, internet of things, combined with intelligence, autonomy and connectivity. Smart systems are today’s drivers of innovation; in all areas of industry and society, highly automated, intelligent systems are taking over tasks, services – and maybe one day, control of our lives.
One of the most diverse, and for many ICT and technology professionals, unfamiliar, areas of application is farming: here the whole life-cycle from soil to fork, from livestock and the field to end-customer, is covered.
With the global population predicted to exceed nine billion by 2040, food production is a major challenge, which will be further exacerbated by climate change, reduced water supplies in many regions, and the environmental impacts of intensive plant and livestock production. The Food and Agricultural Organisation of the UN (FAO) advises that digital technologies be adopted to help increase productivity, to address the food security risk faced in some regions of the world . Studies indicate that if each region of the world can maximise its efficiency of agricultural land management practices, (depending on local climate and environmental conditions) there should be adequate food supplies to support the population.
In Europe, digital technologies could help farmers face other more specific challenges, such as profitability, environmental footprint and sustainability of their businesses.
ICT and agricultural technology advances of recent decades are converging into smart farming, which encompasses precision farming, which covers site-specific crop management responding to inter and intra-field variability in crops. This will impact all areas of agriculture, including efficiency of crop and livestock management; increased production at less cost; conservation of resources such as water and energy; reducing the amount and environmental impacts of fertiliser and pesticides whilst maximising their effectiveness by optimising the timing, location and quantity of application (“minimum environmental footprint”); and preserving and guaranteeing food quality and safety throughout the life cycle and the food value chain in a workflow-like manner. Smart farming is a playground that hosts various stakeholders and technologies: natural science disciplines, including biology, agronomy, meteorology and soil science devoted to crop management and resource preservation, breeding and genetics aiming to develop plants that are more resistant to stressors and require less input; different specialists of livestock and plant health management; a huge variety of sensors from IoT in situ to measure (micro) environmental variables, crop status and animal welfare, and remote sensors such as drones and satellite data to monitor farm and territorial scale conditions; data science to interconnect and exploit existing OGC (Open Geospatial Consortium) data and weather forecast from modelling; ICT solutions for big data handling and interpretation, and mathematical algorithms to pass from measure to decision; actuator technologies and automated machinery or robots to transform decision into action as well as cloud-based data and information systems on a local and more regional basis, for co-operatives to support each other and for large agricultural enterprises to provide a range of services.
From “real-time” to long-term optimisation, all methods and technologies are relevant in this context to achieve the common goal of sustainable and productive farming at high standards of food safety, quality and traceability.
Smart farming solutions can also contribute where labour is the bottleneck of the system. This occurs in developed countries where there is a shortage of labour resources in the agricultural sector coupled with a huge need for more intensive production to satisfy food demand. Labour is also an issue in developed countries for many farming activities, particularly for small farms or marginal farming with fields that are dispersed or difficult to access. Specialised workers are needed for specific agricultural production in areas that are difficult to manipulate, e.g., remote or steep pastures in alpine areas, or for high demand crops (e.g., vineyards) and organic production. In all of these contexts labour can be automated by (almost) autonomous machines and other means within the precision farming paradigm. Last but not least, security issues have to be tackled to achieve integrity, guarantee the availability of data and maintain communication, and to preserve privacy and IPRs of producers, users and data providers.
The European Commission, DG Connect, well aware of the rising importance of internet of things, has put considerable effort into digitising our industry and the internet of things, and in 2015 initiated AIOTI, Alliance for Internet of Things Innovation, as a European portal and initiative to support the creation of large-scale pilots (LSP projects) in this area. AIOTI provided 12 documents in different areas of interest, including the working group (WG06) on “Smart farming and Food Safety” , which was an important input for the Horizon 2020 Call on IoT large-scale pilots.
One of the first areas identified by the EC as being worthy of funding and support was “Smart Farming and Food Security” (Pilot 2). The description of this LSP was:
- Allow monitoring and control of plant and animal products from farm to fork
- Help farmers’ decision making with regards to inputs and management processes
- Design architectures to “program” objects for optimal behaviour
- Enable consumers to access traceability information throughout the whole food chain.
One LSP on “Smart Farming and Food Security” was granted, called IoF2020, Internet of Food and Farm 2020 (30 million euros over four years), with 73 partners, coordinated by Wageningen University. In the second half of 2018, IoF2020 will have an open call of five million euros for new partners .
In the meantime, AIOTI has become an independent association with almost 200 members (Sept. 2016), covering many sectors in 13 working groups . AIOTI WG 06, Smart farming and Food Safety, focuses on the following key objectives, linked to other WG topics as well, because the issues addressed by smart farming are manifold and quite diverse :
- Focus on efficiencies across the ‘from farm to fork’ chain: cropping, livestock farming, food processing and food distribution are all parts of the value chain to deliver products to the final consumer. This includes reducing food loss by improved production and distribution.
- Focus on (livestock) farming and environment. The environmental impact of the livestock sector is large and includes greenhouse emissions, waste, manure and inefficient feeding. Data-driven smart farming and synergies in the distribution chain can contribute to more sustainable production.
- Focus on agriculture and water: Irrigated agriculture currently accounts for 70 percent of world water withdrawals or pollution, and particularly in regions of southern Europe, dramatic improvements in agricultural water use are necessary.
Besides the big initiatives mentioned above, there are already a large number of national and European projects active in this area; a selection of these can be found referenced in the articles of this issue, but there are many more, such as those referenced in the AIOTI publications and the European Cordis website (e.g., AfarCloud, an Horizon 2020 ECSEL JU smart farming project).
The multiversity of subtopics and issues is quite well represented in the articles submitted and presented in this special theme.
Precision farming is looked at by several articles from different viewpoints:
- Building an IoT infrastructure for precision farming,
- Achieving sustainable soil management in conditions of high spatial variability,
- Data information and decision support systems to support farmers and other stakeholders as a service (monitoring prescription maps in one case, building a knowledge centre based on IoT and big data technologies or by applying mathematical methods - models and algorithms - for complex decision making to a range of farming types in two other cases), or
- by describing holistic approaches as concept (from automated machinery to the cloud) or implementation (by pioneering farmers in Tuscany, under support of a university and a regional agricultural consortium).
Livestock management is described from a dairy management point of view (insemination decisions, for instance) and the contactless monitoring of animal growth parameters by optical methods for health and productivity reasons, as decision support to farmers.
Field monitoring is performed by:
- drone swarms to detect and fight different kinds of weeds,
- swarms of intra-canopy sensors, which collect data for disease prediction models,
- remote sensors on board of satellite and aerial platform, which are exploited in an agricultural related application, to acquire images as a fundamental source for preservation of natural habitats (bio-diversity conservation).
Data science (“big data”), reasoning and decision support for the agricultural sector and the research community is the topic of three articles:
- Forecasting allocation of biomass crops (as opposed to food crops) for planning non-food farming activities,
- virtual research environments
- methods for massive high throughput plant phenotyping for the agriculture and food research communities.
Other challenges discussed are security issues (analysis of security threats and risks of IoT services for smart farming, data integrity and availability), a new method (3D X-ray imaging) for rapid food quality inspection, and an engineering solution to exploit biogas residues as a source of nitrogen fertiliser combining energy production and reduction of agricultural pollution.
Of course, topics overlap. If we look at technologies rather than farming sectors and applications, we find contributions related to sensors, remote sensing, modelling, machine learning, IoT, ICT methods, cloud and big data related.
This demonstrates the diversity of challenges and issues to be tackled in smart farming and food safety (security), and highlights the importance of adopting a holistic approach when it comes to practical implementations.