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Related Experiment Video

Updated: Jan 18, 2026

Generating Homo- and Heterografts Between Watermelon and Bottle Gourd for the Study of Cold-responsive MicroRNAs

Generating Homo- and Heterografts Between Watermelon and Bottle Gourd for the Study of Cold-responsive MicroRNAs

Published on: November 20, 2018

Generative AI and Blockchain-Integrated Multi-Agent Framework for Resilient and Sustainable Fruit Cold-Chain

Abhirup Khanna¹, Sapna Jain², Anushree Sah¹

¹School of Computer Science, UPES, Dehradun 248007, India.

Foods (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

This study introduces an AI-powered cold chain system using multi-agent reinforcement learning and blockchain. It significantly reduces fruit spoilage, energy use, and emissions for sustainable food logistics.

Keywords:

blockchain cold-chain logistics generative AI multi-agent reinforcement learning sustainable food systems

Related Experiment Videos

Last Updated: Jan 18, 2026

Generating Homo- and Heterografts Between Watermelon and Bottle Gourd for the Study of Cold-responsive MicroRNAs

Generating Homo- and Heterografts Between Watermelon and Bottle Gourd for the Study of Cold-responsive MicroRNAs

Published on: November 20, 2018

Area of Science:

Supply Chain Management
Artificial Intelligence
Blockchain Technology

Background:

Perishable fruit cold chains face fuel waste, poor coordination, and lack of adaptability.
Traditional systems struggle with dynamic, distributed, and secure food supply chain demands.

Purpose of the Study:

To develop a novel, intelligent, and sustainable cold chain architecture.
To enhance coordination, reduce waste, and improve efficiency in fruit logistics.

Main Methods:

Integrated multi-agent reinforcement learning (MARL), blockchain, and generative AI.
Utilized large language model (LLM) negotiation, Pareto optimization, and Non-Fungible Token (NFT) traceability.
Employed centralized training with decentralized execution (CTDE) for modular agents.

Main Results:

Achieved up to 50% reduction in spoilage and 35% energy savings.
Reduced travel time by 30% and emissions by 25%.
Improved delivery reliability, fruit quality, and system adaptability.

Conclusions:

The proposed framework offers a scalable, intelligent, and sustainable solution for food logistics.
The system is particularly suitable for resource-constrained or intermittently connected environments.
This work establishes a foundation for future-ready, resilient food supply chains.