IoT, big data and artificial intelligence in agriculture and food industry

IoT, big data and artificial intelligence in agriculture and food industry

Table of Contents


Internet of things (IoT) results in a massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supply chain modernization, social media (for open innovation and sentiment analysis) in the food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain-based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes.


  • Author Keywords
    • precision agriculture,
    • social media,
    • gene sequencing,
    • blockchain,
    • sensors,
    • internet,
    • digital,
    • robotics.
  • IEEE Keywords
    • Artificial intelligence ,
    • Internet of Things ,
    • Big Data ,
    • Sensors ,
    • Agriculture,
    • Social networking (online)



INTERNET of things (IoT), big data and artificial intelligence (AI) are perhaps old buzzwords in the tech industry, that are making an impact only in very recent times. In fact, data from Google Trends search history for these topics shows that IoT and big data have drawn the considerable interest of broad-based internet users within the last five to six years, while AI remains a topic of interest for much over a decade (see Fig. 1). In fact, with the increase in communication devices, the volume of data generated is rising and AI is continuing to well-integrated into the lives of a big population of the planet in one form or the other. Unlike AI, IoT primarily being industrial technology remains to be of low interest to the general public. A natural topic of interest for agri-food scientists and engineers would be to maximize the impacts of these emerging information technologies for sustainably feeding the planet. As a first aim of this review, we will begin by briefly introducing these topics for those audiences who are coming from a background in agriculture and food sciences.

First coined by Kevin Ashton, IoT is a technology paradigm contemplated as a vast network of digitally connected devices and machines [1]. Here, the digital connection of the machines or ‘things’ occurs over the ‘internet’. IoT is sometimes also referred to as the Internet of Everything or the Industrial Internet. The influence of IoT arises from its ability to enable robust communication between the physical world with that of the digital, a concept often referred to as the fourth industrial revolution. In fact, the use of IoT in the industry is sometimes also referred to as the ‘Industrial Internet of Things (IIoT)’. In the IIoT framework, remote sensors gather information generated by machines (and increasingly, humans too) to increase efficiency, promote better decision-making and build competitive advantages, regardless of industry or company size. IoT platforms serve as the bridge between the devices’ sensors and the data networks, wherein the connected IoT devices exchange information using internet transfer protocols. The sensors of the devices within an IoT network yield large volumes of data that continuously stream to a “data lake”, which could be a local physical server or cloud-based storage (i.e. distributed across the internet worldwide) for enabling necessary data processing via appropriate algorithms or machine learning techniques to generate actionable insights. Thus, we note that IoT is essentially the means of generating and transmitting large amounts of data with information of practical use embedded in it. The concept and scope of big data, as a matter of fact, lacks a formal definition. Big data in the IoT context does not only refer to the structured or unstructured data, but also includes the aspects of analytics, insights, and (automated) decisions, all of which typically happen in real-time. In addition to the massive data generated from devices/sensors, social media is an important source of user-generated big data, which deserves special discussion. Though increasingly valuable, one should note that social media data does not (strictly) fall into the IoT framework. We will discuss the usefulness of social media big data analytics later in this review in a dedicated section. The recent developments in machine learning, artificial intelligence and boom in the data science field, coupled with improvements in computing power has enabled the automated decision support, real-time analytics for insights, and better performance of supervised (learning) models. A discussion of the relevant machine learning tools for artificial intelligence is also included later in this review.


IoT is recognized as one of the most important areas of future technology and is gaining considerable attention from a wide range of industries. With the implementation of IoT infrastructure in farming, farmers will be more efficient, intelligent, and connected, feeding vast amounts of information to analysts regarding crop yields, soil mapping, fertilizer applications, weather data, machinery, and animal health. The use of sensors is steadily increasing in early reporting of issues pertinent to crop health in farms, thereby enabling early checks for public health and safety. Efforts leading to easy integration of various IoT devices in terms of data and instruction flow from farm to consumer chain is important to obtain a viable and efficient IoT system. The food supply-chain industry is at the forefront of IoT adoption to track the consignments and re-route them in realtime. Food quality and authenticity evaluation using miniature spectral cameras has become popular in the industry and efforts are underway to bring this capability to consumers through their smartphones. The industry is also exploring the benefits of blockchain technology and next-generation genome sequencing for traceability in case of pathogen outbreaks and to ensure food safety. The huge volumes of data from social media are being analyzed for consumer behavior and crowdsourcing of ideas for new food product development. In conclusion, the key performance indices that IoT and big data technologies will be potentially impacting are economical (e.g. increased productivity, lower production cost, and higher quality), environmental (e.g. less resource consumption, lower emission, and carbon footprint) as well as social (e.g. improved public health, consumer demand-driven, quality of life improvement). The pace of innovations in the field of IoT, big data, and AI are astounding and tasks that seemed impossible a few years ago have now been implemented with great success. Embracing technology innovations and putting them to advantages are important for the success of modern agriculture and the food industry.


This work has received financial support from the New Zealand Government Ministry of Business, Innovation and Employment (MBIE), through AgResearch Strategic Science Investment Fund (SSIF); Project: Food Integrity Transparency – Verifiable and digital food integrity PRJ0126328/A25768. Authors acknowledge Dr Marlon M. Reis, AgResearch, for helpful discussions and constant encouragement.

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IoT, big data and artificial intelligence in agriculture and food industry



N. N. Misra, Y. Dixit, A. Al-Mallahi, M. S. Bhullar, R. Upadhyay and A. Martynenko,




IoT, big data and artificial intelligence in agriculture and food industry

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in IEEE Internet of Things Journal,



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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.