M. Sangeetha, Goriparthi Thejaswini, A Shoba, Snehal Gaikwad · 6 authors
Abstract In this paper, an Internet of Things (IoT) based Crop Monitoring and Classification system is proposed for automated sensing, storing, and monitoring real-time parameters that play an important role in determining a crop’s quality and yield. Sensors are placed in-situ in the field and the warehouse to monitor the crop. The long-Range wide area network (LoRa) module is used for communication between the sensing unit placed at the field, warehouse, and data processing unit. The yield is classified based on qualitative analysis posed by the imperative sensor data. Further, to enable an equitable gateway of resource sharing between the distributor and the farmer, a Blockchain-based transaction is taught to enhance trust and security. This proposed method aims to eliminate intermediaries in the trade, thereby helping farmers get the price for their product details stored in an immutable database, which also displays the farmer’s quality of the crop reaped.
Pest can be a serious topic in agricultural areas especially rice plantations. The pest destroys the plants before harvesting time. Because of the presence of the pest, the yield of agricultural products is decreasing. From a technical point of view, an agricultural professional should identify not only the type of pest that destroys rice plants but also overcome the pest. This research proposes a paper review on pest detection systems by using Blockchain technology and the Internet of Things involving all parties involved. The use of the review is to have a broad overview regarding the functional combination between IoT and Blockchain technologies to reduce the pest problems. Smart contract technology Blockchain may be held to determine automatic alert in the system and know how to resolve the problem accurately and all information is verified by blockchain system without a human interception. IoT devices can be attached to rice plantations to monitor, determine and send the information of the pests. Our paper explains the combination of IoT and Blockchain technologies in order to improve any possibility of success rate by getting the pests. Thus, IoT replaces human manual checking in pest identification to reduce human error. So that, the harvesting time can be increased and the agriculture yields are good. The search comparison results show that ScienceDirect has the highest search value
Terry Griffin, Keith D. Harris, Jason K. Ward, Paul Goeringer · 5 authors
Abstract Distributed ledger technology applied to Big Data in agriculture presents challenges and opportunities. Opportunities exist to solve decades‐old farm data management problems. Real‐world examples of applying distributed ledger technology to current farm data problems in cotton include (1) yield monitor data quality assurance, (2) sustainability metrics and resource tracking of cotton lint quality data from ginner back to subfield locations, and (3) increasing supply chain coordination by providing more information to warehouse managers. The culmination of the discussion across three aspects of cotton production data is of interest to farmers, researchers, policy makers, and consumers.
Lu Wang, Longqin Xu, Zhiying Zheng, Shuangyin Liu · 8 authors
The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people's daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.
Milan Marković, Naomi Jacobs, Konrad M. Dryja, Peter Edwards · 5 authors
In this article, we discuss our experience of realising a prototype IoT-based food safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used - as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance.
Community Supported Agriculture (CSA) model is an efficient solution that not only solves the problem of the agricultural product's origin but also provides a method to share the market risk between consumers and producers. In the CSA model, consumers order and pay for products in advance. Then, the producers will send packages of fresh products to consumers after a fixed period corresponding to the paid amount. However, the critical weakness of the CSA model is the lack of solutions for both sides to demonstrate the product's quality, which makes consumers unsure about the condition of receiving products. In consequence, consumers become hesitant to order and pay money in advance. In this paper, we will propose a novel solution that allows consumers to track their products through agricultural diaries recorded by farmers every day. The key difference of the proposed solution is to leverage Blockchain technology advantages in authenticating and protecting the integrity of information. Such that, consumers could track all steps in the production process quickly and reliably. Meanwhile, producers can build and increase their enterprise branding by transferring product information transparently and responsibly.
Abstract In modern cities, smart irrigation systems are designed to operate via Internet of things (IoT) based sensor units having precise measurements of irrigation requirements such as amount of water, crop temperature, and humidity to build a robust supply chain ecosystem. The usage of sensors and networking units enable the optimal usage of irrigation resources, and is termed as precision irrigation (PI). Thus, PI leverage an efficient solution to handle the scarcity of essential resources such as food, water, land units, and crop yields. Thus, farmers gets better returns in the market due to high production. However, in PI, the exchange of crop readings from sensor units to actuators are processed through open channels, that is, Internet. Thus, it open the doors for malicious intruders to deploy network and sensor‐based attacks on PI‐sensors, to drain the available resources, and battery power of sensor nodes in the network. This reduces the optimum and precise utilization of irrigation resources, low‐yield crops and damaged crops in supply chain systems. This leads to dissatisfaction among agriculture stakeholders such as quality control units, logistics, suppliers, and customers. Motivated from the above discussions, the survey presents the advantages of integrating blockchain (BC) with PI to handle issues pertaining to security, trust, and transactional payments among agriculture stakeholders. The survey is directed to achieve threefold objective‐ attack models and countermeasures in PI systems, integration of BC in PI to mitigate attack models, and research challenges in deploying BC in PI. To address the first objective, the survey proposes an in‐depth comparative analysis of traditional irrigation systems with PI, with discussions on attack models. To address the second objective, the survey proposes an integration model of BC with PI to secure IoT sensors, and maintain trust and transparency among stakeholders. Finally, the survey addresses the open research challenges of deploying BC in PI‐based irrigation systems, and presents a case‐study of AgriChain as an industry ready‐solution that envisions BC with PI ecosystem. Thus, the proposed survey acts as a roadmap for agriculture industry stakeholders, researchers, to deploy BC in IoT‐based PI that leverages an efficient, robust, trust‐worthy, and secure ecosystem.
Machine learning has evolved with high performing computing algorithms along with Robotics and Artificial intelligence technologies. SWARM robotic system is a diligence of multi robot intelligence with collaborative communication approach. SWARM robots play a major role in precision agriculture. SWARM robots mainly focus on aspects like coordination, decentralized control and self organization. These technologies have created new opportunities in multidisciplinary agricultural domain. Integration of Machine learning principles with SWARM robotics will form a novel solution to make agricultural practice even more intelligent and accurate. In this paper, we present a comprehensive review of research related to adoption Machine learning principles in precision agriculture for various autonomous agricultural activities using SWARM robots.
Yorghos Voutos, Γεώργιος Δρακόπουλος, Phivos Mylonas
Smart agriculture is increasingly becoming a paramount financial sector with important implications on a global scale. The real time weather and soil status monitoring as well as the desired higher food quality are major drivers behind this technological, ecological, and financial trend. This work explores the enticing prospect of combining IoT and smart contract technologies with smart agriculture in order to deliver not only higher quality agricultural products, but also improving the associated supply chain and agricultural logistics, thus resulting in multiple benefits for all the parties involved. Emphasis is placed on deriving similarity metrics for tuples describing soil and climate conditions based on numerical and possibly categorical data. Moreover, a sample implementation of one such metric is given in Solidity, a high level language for formulating smart contracts designed for the Ethereum Virtual Machine is also provided as a concrete example. Finally, aspects of agricultural asset digitization, a crucial step for smart contracts relying on physical objects are also discussed.
The high demand for food makes it necessary to implement plant phenotyping processes into breeding programs to deal with global food security. Image-based plant phenotyping generates vast amounts of data. Traditionally, this data has been managed in a centralized manner requiring that an administrator grants access permissions. This approach has some limitations when sharing data; for instance, we cannot track whether data has been shared with the permission of the owner. This research proposes a blockchain-based system for data access control management in the field of image-based plant phenotyping. This system integrates Ethereum blockchain to allow users to be the administrators of their data. Thus, users can grant or deny permissions to access their data without relying on any central administrator. This system is part of the research work at the Plant Phenotyping and Imaging Research Centre (P2IRC) in Saskatoon, Canada.
Percival Lucena, Alécio Pedro Delazari Binotto, Fernanda da Silva Momo, Henry Kim
One of the key processes in Agriculture is quality measurement throughout the transportation of grains along its complex supply chain. This procedure is suitable for failures, such as delays to final destinations, poor monitoring, and frauds. To address the grain quality measurement challenge through the transportation chain, novel technologies, such as Distributed Ledger and Blockchain, can bring more efficiency and resilience to the process. Particularly, Blockchain is a new type of distributed database in which transactions are securely appended using cryptography and hashed pointers. Those transactions can be generated and ruled by special network-embedded software -- known as smart contracts -- that may be public to all nodes of the network or may be private to a specific set of peer nodes. This paper analyses the implementation of Blockchain technology targeting grain quality assurance tracking in a real scenario. Preliminary results support a potential demand for a Blockchain-based certification that would lead to an added valuation of around 15% for GM-free soy in the scope of a Grain Exporter Business Network in Brazil.