Table 2

The analytical framework

CategoryVariablesResults%
Authors 37 
 Academics2567%
 Collaborations822%
 Practitioners411%
Type of source 37 
 Article2157%
 Conference proceeding1643%
Location of the study 37 
 Yes2465%
 
  • -

    Asia

1146%
 
  • -

    America

729%
 
  • -

    Europe

624%
 
  • -

    Oceania

28%
 
  • -

    Africa

28%
 No1335%
Research method 37 
 Case study2670%
 Literature review1130%
Agricultural sector 37 
 Cultivation of plants1540%
 General terms1540%
 Animal production616%
 Fish farming13%
Problems to solve-objective to achieve 37 
 Increase efficiency and optimization maximizing farm returns2670%
 Manage the environmental impact and external changes2465%
 Predict and manage the farm complexity1951%
 Feed the increasing global population-food security924%
 Other objectives25%
Technology used 37 
 Decision support system (DSS)2157%
 Artificial intelligence and machine learning1849%
 Big data analytics1643%
 Internet of things (IOT)1540%
 Drones822%
 Robots822%
 Cloud computing719%
 Geographical indication system (GIS)616%
 Other technologies616%
 Biotechnology411%
 Blockchain38%
 Autonomous devices38%
Applications in agriculture37 
 Precision farming and agronomic applications2465%
 Agronomic planning and economic applications2157%
 Water optimization and environmental management applications1540%
 Food supply chain applications and traceability514%
Mentions a business model37 
 No2054%
 Yes1746%
 
  • -

    Smart farming Business model

1376%
 
  • -

    Data driven business model

847%
 
  • -

    Industry 4.0 business model

215%
Mentions the possibility to lead a new business model 37 
 No3184%
 Yes616%
 
  • -

    Platform business model in the food supply chain

233%
 
  • -

    Agritech 4.0 with integrated smart food supply chain

233%
 
  • -

    Supply chain management 5.0

117%
 
  • -

    New information-based system based on traceability

117%
Connects to sustainability issues 37 
 Yes2362%
 
  • -

    Reduce the use of pesticides, heavy metals and nitrates which pollute agricultural soil and water

835%
 
  • -

    Reduce the consume and loss of water

626%
 
  • -

    Climate-oriented and ecologically friendly applications

522%
 
  • -

    Food security in a sustainable way

522%
 
  • -

    Making sustainable the ecological impact of food production

417%
 No1438%
Explain the advantages 37 
 Yes3492%
 
  • -

    Organizational advantages and decision support

2471%
 
  • -

    Efficiency benefits and productivity increase

1647%
 
  • -

    Environmental benefits

26%
 
  • -

    Food safety and easy compliance

26%
 No38%
Explain the disadvantages 37 
 No3081%
 Yes719%
 
  • -

    The water limits compliance inevitably leads to some losses in the farm income

114%
 
  • -

    The system doesn't work without a standard power supply

114%
 
  • -

    Some will always think that is absurd, disappointing and danger for humankind

114%
 
  • -

    Difficult to create a unique system for different areas and crops

114%
 
  • -

    Inevitable carbon dioxide emission as a consequence of intensive use of information technologies

114%
 
  • -

    Environmental impact in the food chain from genetically engineered crops which will destroy the actual situation

114%
 
  • -

    Complexity to realize

114%
 
  • -

    Unrealizability on areas without the extensive available data regarding soil and geology

114%
Explain the barriers 37 
 No2362%
 Yes1438%
 
  • -

    Farmers lack of technical knowledge about ICT and other emerging technologies

750%
 
  • -

    Lack of equipment, Internet access, storage capacity and high-quality data

750%
 
  • -

    High investment costs and low perceived effectiveness

643%
 
  • -

    Mismatch between applications and farmer practical needs

429%
 
  • -

    Data control and data security

321%
 
  • -

    Lack of integration and complexity of the food supply chain

214%
 
  • -

    Large energy consumption and unsustainability

214%
 
  • -

    User psychological barriers to adoption

17%
Research implications 37 
 No2157%
 Yes1643%
 
  • -

    Extend and integrate the research with new data or focus on new related problems

1062%
 
  • -

    Test the validity and accuracy of the proposed method

425%
 
  • -

    Focus on new aspects not yet deepened

319%
 
  • -

    Focus on develop new solutions and new technologies

319%
Practical implications 37 
 Yes2670%
 
  • -

    Support farmers in the decision-making process

1335%
 
  • -

    Support everyday farm operations increasing efficiency and effectiveness

1027%
 
  • -

    Provide farmers useful forecasts to manage the farm unpredictability planning their activity

719%
 
  • -

    Provide farmers new solutions with integrated technologies

38%
 No1130%
Policy implications 37 
 No2876%
 Yes924%
 
  • -

    Governments should use the agricultural data to improve policy-making and decision-making learning from data

444%
 
  • -

    Governments should subscribe new investments to enhance the technological transition

444%
 
  • -

    Governments should create advisory units to support the farmers awareness about complex technological tasks

222%
 
  • -

    Governments should support the social innovation to engage younger generations to be more involved in the honey and bee industry

111%

Source(s): Authors work

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