Enhancing transparency and efficiency in blockchain harvest: Empowering farmers and consumers through transparent trading in agricultural applications

Abstract

With the development of edge-cutting technologies, digital agriculture farming, product selling, and purchasing have been increasing progressively. On the other hand, transparency in digital agricultural agrochemicals (pesticides or herbicides) substances of products needs to be monitored carefully between production and selling to customers in a transparent form. Recently, blockchain has emerged as a decentralized technology, which is the most potent decentralized technology. It connects many nodes and validates their data transparency during application sharing. Therefore, it is a motivation to use blockchain technology for digital agriculture applications to meet data transparency, security, and privacy requirements. In this paper, we present enhancing transparency and efficiency in blockchain harvest: empowering farmers and consumers through transparent trading in agricultural application tasks. The application tasks are a mixture of agricultural things (IoT) sensors, products, monitoring, fertilizers, transport tracking, farmers, consumers, and institutional data for processing in digital healthcare applications. Our objective is to process the agricultural tasks during production and trading in an immutable, transparent, secure, and private form. To meet constraints such as processing time, blockchain validation, access control, and cyber-attacks, we suggest enhancing transparency and efficiency in blockchain harvesting and empowering farmers and consumers to trade securely and transparently. In proposed blockchain agriculture, all nodes are heterogeneous and connected; therefore, to avoid any time failure, cyberattacks, or block failure, we establish them in a stable form. This paper presents the agriculture blockchain harvest multi-tasking scheduling (ABHMTS) algorithm methodology, which amalgamates different methods: smart contracts, local offloading, multi-tasking, and blockchain validation with malware detection in blocks during data transfer among other nodes. The experimental results in simulation show that ABHMTS improved the transparency of agricultural task data by 98% and executed them with the minimum processing time by 20%, and reduced the risk of resource failure and resource consumption by 10% with higher malware detection in the framework as compared to baseline approaches.