Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges

Abstract

Blockchain quickly became an important technology in many applications of precision agriculture discipline. The need to develop smart P2P systems capable of verifying, securing, monitoring, and analyzing agricultural data is leading to thinking about building blockchain-based IoT systems in precision agriculture. Blockchain plays the role of pivotal in replacing the classical methods of storing, sorting and sharing agricultural data into a more reliable, immutable, transparent and decentralized manner. In precision farming, the combination of the Internet of Things and the blockchain will move us from only smart farms only to the internet of smart farms and add more control in supply-chains networks. The result of this combination will lead to more autonomy and intelligence in managing precision agriculture in more efficient and optimized ways. This paper exhibits a comprehensive survey on the importance of integrating both blockchain and IoT in developing smart applications in precision agriculture. The paper also proposed novel blockchain models that can be used as important solutions for major challenges in IoT-based precision agricultural systems. In addition, the study reviewed and clearly discussed the main functions and strengths of the common blockchain platforms used in managing various sub-sectors in precision agriculture such as crops, livestock grazing, and food supply chain. Finally, the paper discussed some of the security and privacy challenges, and blockchain-open issues that obstacles developing blockchain-IoT systems in precision agriculture.

Introduction

There is a growing body of literature that recognizes the importance of utilizing emerging technologies in precision agriculture (Zhang et al., 2002, McBratney et al., 2005, Nikkil et al., 2010). Precision agriculture is a new technology that utilizes Information Technology (IT), satellite technology, Geographical Information System (GIS), and remote sensing for enhancing all functions and services of the agriculture sector (Khanal et al., 2017). Today, precision agriculture started to rely upon Mobile apps (Jagyasi et al., 2013), smart sensors (Sartori and Brunelli, 2016), drones (Puri et al., 2017), cloud computing (Mekala and Viswanathan, 2017), Artificial Intelligence (AI) (Jha et al., 2019), internet of Things (IoT)Ahmed et al., 2018), and blockchain (Ge et al., 2017). Based on these technologies, it is become possible to process and access real-time data about the conditions of the soil, crops, and weather along with other relevant services such as crops and fruits supply chain, food safety, and animal grazing.

Many statistical reports announced that precision agriculture will add more improvement to the global economy based on the use of advanced technology in all agriculture subsectors. According to market research and advisory firm, the global market of precision agriculture will grow at an average rate of 13.7 percent to reach 10.55 billion U.S. dollar by 2025 (Xinhuanet, 2020). In addition, the global precision farming market is evaluated to grow from USD 7.0 billion in 2020 to USD 12.8 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 12.7% (Markets and Markets, 2020).

In precision agriculture, forecasting and predictive analytic software systems can use agricultural data to provide farmers with guidance about soil management, crop maturity rotation, optimal planting times, and harvesting times, etc. For example, machine learning technology can be integrated with remote sensing for accurate forecasting of crop production and nitrogen levels estimation in precision agriculture (Chlingaryan et al., 2018, Lee et al., 2010). In addition, historical crop planting maps can be used for developing a machine learning system for predicting annual crop planting (Zhang et al., 2019, Elavarasan et al., 2018). Predicting crop growth in the smart greenhouse using a self-learning model and IoT data is another contribution of machine learning in precision agriculture (Kocian et al., 2020). AgriProduction (Dos Santos et al., 2019) is another system that able to anticipate agricultural problems related to soil humidity, temperature, and leaf growth based on both LoRa IoT technology and the ARIMA prediction model.

Sensor technology and IoT can also mitigate various challenges in precision agriculture (Tzounis et al., 2017). Agricultural monitoring systems can provide surveillance services that maintain the plant growing at an optimal level and early anticipate the conditions that lead to epidemic plant disease outbreak based on wireless sensor networks installed in the planted area (Khattab et al., 2019, Ibrahim et al., 2019). Smart irrigation systems based on IoT and sensor technology is another solution for the shortage of clean water resources that are necessary for a lot of plants kinds as well as achieving optimum water-resource utilization in the precision agriculture (Huong et al., 2018). Building flexible and automated platforms able to cope with soilless needs in full smart greenhouses using moderately saline water is an important issue in precision agriculture that depends on combining IoT with cloud and edge computing for mitigating this challenge.

Deep learning technology represents a recent technology in precision agriculture (Kamilaris and Prenafeta-Bold, 2018). This new technology can help in designing automated and reliable fruit detection systems for fruit yield estimation and automated harvesting through applying neural network models on imagery data obtained from two modalities: color (RGB) and Near-Infrared (NIR)Sa et al., 2016) like Mango fruit detection (Koirala et al., 2019), cotton detection and segmentation (Li et al., 2017), and apple detection and segmentation (Kang and Chen, 2019). Utilizing deep learning for visual detection and recognition of weeds in grasslands is an additional contribution of deep learning in terms of weed control in precision agriculture discipline (Kounalakis et al., 2018).

Decision Support Systems, data analysis, and data mining become a significant technique for managing many services in precision agriculture (Zhai et al., 2020). Managing smart farms through web-based decision support systems can help in complying with many requirements in precision agriculture such as crop production, optimizing farming costs, and monitoring market dynamics in more efficiency (Narra et al., 2020). In addition, many steps have been made to improve irrigation decisions based on various irrigation decision models that can help the farmers to carry out critical irrigation actions and optimize irrigation depth (Car, 2018, El Baki et al., 2018). Moreover, water loses reduction and improving water supply efficiency can be achieved through automating irrigation canal operations, and manipulating both known and unknown patterns of water demands that can be recognized by different irrigation systems in the farms (Shahdany et al., 2019).

Agricultural robotics has brought also a significant development for various applications in precision agriculture (Pedersen et al., 2006). The objective of agricultural robotics is more than just the use of robotics for specific functions in precision agriculture, but most of the recent the automatic agricultural vehicles are multi-function, such that it can be used for weed detection, agrochemical dispersal, terrain leveling, irrigation, field supervision, as well as tree fruit production (Cheein and Carelli, 2013, Bergerman et al., 2015). Moreover, developing a smart drone system becomes an interesting and significant technology in precision agriculture (Mogili and Deepak, 2018). Smart drones able to solve many big challenges in precision agriculture such as irrigation monitoring, weed identification, crop dusting, crop monitoring, pesticide spraying as well as deterring fertility levels, identifying bacteria, fungus or diseases based on Infra-red radiation commonly reflected from sensors or thermal imagery (Smith, 2020).

Recently, blockchain represents the last new technology that can be used for mitigating significant challenges in precision agriculture, especially when integrated with IoT technology (Tripoli and Schmidhuber, 2018). According to a new market intelligence report by BIS Research, employing blockchain technology in precision agriculture,and food supply chain markets is anticipated to increase from $41.9 M in 2018 to $1.4B by 2028 (BIS Research, 2018). Blockchain can introduce a variety of benefits and support in several applications in precision agriculture. For instance, Smart farming, supply chain monitoring and tracking (Lin et al., 2018, Bordel et al., 2018, Tse et al., 2017, Casado-Vara et al., 2018), finance management (Chinaka, 2016), data assurance and security (Mann et al., 2018, Xie et al., 2017, Liang et al., 2017). The Increased utilization of the public blockchain in food markets has also motivated the governments to restructure their legislative frameworks and regulations to consider blockchain in its economic policies. Recently, the need for blockchain in precision agriculture is mandatory to bridge the demand and supply gap along with attaining sustainability in the ecosystem.

Although some reviews studies in integrating blockchain with IoT have been introduced (Dorri et al., 2017, Fernáez-Caramés and Fraga-Lamas, 2018), these reviews didn’t go deep into investigating the benefits and solutions that blockchain can introduce for developing new applications in precision agriculture (Bermeo-Almeida et al., 2018). As a response to this limitation in the literature, this paper is one of the first mature studies that introduces a holistic approach and a systematic review for investigating more contributions of blockchain technology in precision agriculture. The rest of this paper is designed as follows: Section 2 introduces an overview of blockchain technology. Section 3 discusses the major contributions of Blockchain in IoT applications. Section 4 discusses the major uses cases of integrating blockchain with IoT in precision agriculture. Section 5 discusses some challenges and open problems that obstacle building blockchain-IoT networks in precision agriculture. Finally, Section 6 summarizes the general conclusion.