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Related Concept Videos

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Issues And Trends In Healthcare Delivery System

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Related Experiment Video

Updated: May 13, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies
09:43

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

RiceBlock-0.2: secured precision rice farming framework using blockchain technology.

Bello Musa Yakubu1,2, Abdullah Alabdulatif3, Chang Ge4

  • 1Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.

Scientific Reports
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

RiceBlock integrates blockchain and AI for secure, automated precision rice farming. This framework ensures data integrity and enhances decision-making for improved agricultural outcomes.

Keywords:
Agronomic decision automationBlockchain-enabled precision agricultureDecentralized agricultural systemsIoT-based smart farmingPrivacy-preserving data managementSecure multi-sensor authenticationStage-aware smart contracts

Related Experiment Videos

Last Updated: May 13, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies
09:43

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Area of Science:

  • Agricultural Technology
  • Computer Science
  • Cybersecurity

Background:

  • Precision agriculture requires secure data acquisition, authenticated device interactions, and transparent decision-making.
  • Existing systems face challenges in integrating Internet of Things (IoT), blockchain, and artificial intelligence (AI) for robust agricultural solutions.
  • Secure and automated agronomic decision-making is crucial for optimizing crop yields and resource management.

Purpose of the Study:

  • To present RiceBlock, a novel blockchain-IoT framework for secure data collection and automated agronomic decisions in precision rice farming.
  • To enhance data integrity, confidentiality, and device authentication in agricultural IoT systems.
  • To provide a scalable and privacy-preserving architecture for AI-driven agricultural intelligence.

Main Methods:

  • Implemented SHA-256 hashing and AES-256-GCM encryption for data integrity and confidentiality.
  • Utilized Ethereum digital signatures and timestamp verification for mutual authentication between sensor nodes, cluster heads, and user mobile devices.
  • Employed Solidity smart contracts under Proof-of-Authority (PoA) consensus for governing device identity, data provenance, and AI-driven decisions.

Main Results:

  • RiceBlock achieved a mean transaction latency of 5.73 seconds and a throughput of 22 transactions per second.
  • Demonstrated high authentication success rate (98.2%) with low false acceptance (1.8%) and equal error (2.1%) rates.
  • Confirmed decision accuracy with an F1-score of 0.93 and ROC-AUC of 0.96, showing resilience against replay and forgery attacks.

Conclusions:

  • RiceBlock effectively secures environmental data collection and automates agronomic decisions for precision rice farming.
  • The framework provides a scalable, privacy-preserving architecture, successfully bridging blockchain-based trust with AI-driven agricultural intelligence.
  • The system offers enhanced security and transparency, paving the way for more efficient and reliable precision agriculture.