Big Data in Smart Farming – A review

By Sjaak Wolfert, L. Ge, Cor N. Verdouw, M. Bogaardt. 2017

2,693 citations recorded by the source corpus.

Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.

Subfields: Food Science, Industrial and Manufacturing Engineering, Information Systems

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “3) Smart Agriculture: In order to produce sustainable food resources in smart city, smart farming [177], [178] has become a trend to meet the food supply demand in a smart city.”
Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and Opportunities for Sustainable Development (2020, background): “Mobile apps and other social media platforms can be used to provide information on weather, rainfall, and soil humidity to allow better farm management and productivity, as well as information on markets [57].”
From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges (2020, background): “literatures [25], [84], [86] have presented comprehensive discussions on the challenges of adopting big data analytics in agriculture.”
From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management (2020, background): “[20] published a review on big data applications in Smart Farming.”
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges (2018, background): “The complexity of the data can range from structured to nonstructured data [11], [34] which can be in the form of text, images, audio, and video.”
Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies (2021, background): “It involves the use of information and communication technologies (ICTs) in the cyber-physical cycle of farm management, with technologies such as the IoT and cloud computing, robotics, and artificial intelligence (AI) [20]–[24].”
IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry (2020, neutral): “A detailed review of the role of big data in smart farming is already available [15]; hence, our aim is to provide an exposition of the developments where many agri-food-automation companies are actively contributing or reaping the benefits.”
Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review (2020, background): “Technologies, like Remote Sensing, Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Big Data Analytics (BDA) and Machine Learning (ML) are particularly promising and they can give a new breakthrough in agricultural practices [2,3] .”
Research and development in agricultural robotics: a perspective of digital farming. (2018, background): “Agricultural field robots and manipulators have become an important part in different aspects of digital farming [2] and precision agriculture [3] .”
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, background): “This can happen by the usage of current disruptive technological platforms, which enable continuous monitoring of the physical agricultural environment (through wireless sensors) while producing endless amounts of data (Wolfert et al. 2017).”
Security and Privacy in Smart Farming: Challenges and Opportunities (2020, background): “The most noticeable feature of smart farming is its communication ability between the smart devices, resulting in an unprecedented amount of generated data [7], [111].”