A fuzzy statistical view on a rhetorical analysis of blockchain implementations in the agricultural sector

Abstract

Blockchain has proven to be a highly secure, and distributed solution for managing data of any size. Security is provided due to the immutability and distributed verifiability properties of the blockchain. Different implementations of blockchains which range from simple blocks to smart contracts are used for this purpose. Majority of these implementations can be used for any kind of secure data storage application like home internet of things (HIoT), agriculture, medical data storage, etc. Out of these fields, agriculture is a highly distributed application which involves multiple entities to combine their processed data in order to form a strong supply chain. These entities include but are not limited to crop & soil monitoring, warehousing & distribution, retail & marketing, quality control, compliances, taxations, bills, food safety, etc. In order to store such a large amount of data with high security and optimum read/write performance requires careful planning of the blockchain structures. A wide variety of algorithms are available for this purpose, some of which are as simple as storing all the data into a single blockchain, to using side-chains for storage of different variety of data & then combining these chains via machine learning for effective representation, reproduction and data modification. In order to design an effective system, researchers & system designers must select the most optimum implementations of these algorithms, as suited for their given applications, which is a daunting task that requires large scale testing and continuous optimization. To reduce this complexity, this text provides a comprehensive discussion about the nuances, advantages and properties of these implementations as applied to different agricultural fields, which can assist researchers to use these implementations directly without the need of exhaustive algorithmic searching. Moreover, this text also recommends ways in which the existing algorithms can be improved for better performance under the given application