Food waste is an important social and environmental issue that the current society faces, where one third of the total food produced is wasted or lost every year while more than 820 million people around the world do not have access to adequate food. However, as we move towards a decentralized Web 3.0 enabled smart city, we can utilize cutting edge technologies such as blockchain, artificial intelligence, cloud computing and many more to reduce food waste in different phases of the supply chain.
In this book, we introduce FoodSQRBlock and SmartNoshWaste - two blockchain based multi-layered frameworks in the food supply chain utilizing cloud computing, QR code and reinforcement learning to reduce food waste.
Table of Contents
1 Preface
2 Introduction
3 Prerequisite: Blockchain, AI, QR Code and Cloud Computing
3.0.1 Blockchain Technology
3.0.2 AI and Reinforcement Learning
3.0.3 QR Code
3.0.4 Cloud Computing
4 FoodSQRBlock: FoodSQRBlock: Digitizing Food Production and the Supply Chain Data
4.1 Proposed Framework: FoodSQRBlock
4.2 Experimental evaluation: case study & large scale integration of FoodSQRBlock
4.2.1 Experimental Evaluation
4.2.2 Analysis & Discussion
4.3 Future Direction and Discussion
4.4 Summary
5 SmartNoshWaste: Using Blockchain & Machine Learning in Food Supply Chain to Reduce Waste
5.1 Proposed Framework: SmartNoshWaste
5.1.1 Assumptions and data system architecture of SmartNoshWaste
5.1.2 Machine learning module of SmartNoshWaste
5.2 Experimental evaluation: Case study with real food data
5.3 Future Direction and Discussion
5.4 Summary
Objectives & Core Themes
This book explores the integration of blockchain technology, artificial intelligence, and cloud computing to modernize food supply chains in smart cities, specifically aiming to reduce food waste and improve traceability.
- Development of blockchain-based frameworks for food production transparency.
- Implementation of QR code technology to bridge the gap between digital data and consumer accessibility.
- Application of reinforcement learning to optimize food surplus and minimize waste.
- Scalability and feasibility analysis of decentralized data systems in cloud environments.
Excerpt from the Book
Encoding Module: If we consider fi as the ith instance of the food item being produced at a farm or manufacturing plant, then the following information (info(fi)) about the produce: produce name (pi), type (ti), farm/manufacturing plant id (farmi), size of produce (si), production date (pdatei), expiry/best before date (edatei), could be digitized such that these information could be passed along with the block for traceability and verification purposes by the consumer. Therefore, the digitized information could be represented as follows:
info(fi) = {pi ,ti , farmi ,si ,pdatei ,edatei} (4.1)
In info(fi), the unique farm id (farmi) is stored, which correlates with the farm data (farm name, Geo-location of the farm), stored in a database maintaining records of all the farms/manufacturing plants. Here, the unique farm id is also generated using the hash function on the stored details of the farm/manufacturing plant, and hence, ensuring that the farm id is unique for each farm. In the genesis block (block 0), info(fi) is stored. Whenever the produce is transported or processed by an entity in the supply chain a new block is generated, which holds the original info(fi) as well as the hash of the previous block. QR code could be generated at any point in the supply chain and it holds the information passed in the block (info(fi) and hash of previous block). Fig. 4.3.(a) shows the QR code generated at the shop when the produced milk is transported to the shop (from farm to warehouse/distribution center to shop).
Summary of Chapters
1 Preface: The author shares his personal motivation and the inspiration for developing technologies to fight food waste.
2 Introduction: Provides an overview of smart cities and the critical role of Web 3.0 technologies in addressing global food wastage and safety challenges.
3 Prerequisite: Blockchain, AI, QR Code and Cloud Computing: Explores the fundamental technical concepts required to understand the proposed frameworks.
4 FoodSQRBlock: FoodSQRBlock: Digitizing Food Production and the Supply Chain Data: Introduces a blockchain-based framework that digitizes food production information to enhance traceability.
5 SmartNoshWaste: Using Blockchain & Machine Learning in Food Supply Chain to Reduce Waste: Presents an advanced multi-layered framework that utilizes reinforcement learning to minimize food waste.
Keywords
Blockchain, Artificial Intelligence, Smart Cities, Food Supply Chain, Food Waste, Traceability, Reinforcement Learning, Cloud Computing, QR Code, Web 3.0, Sustainability, Data Digitization, Q-Learning, Farm-to-Fork, Waste Reduction
Frequently Asked Questions
What is the primary focus of this book?
The book focuses on utilizing advanced technologies like blockchain, AI, and cloud computing to digitize food supply chain data, ultimately aiming to reduce food waste and improve consumer transparency.
What are the central themes discussed in the work?
The central themes include digital traceability in food systems, the role of decentralization in smart cities, the application of machine learning for waste optimization, and the integration of diverse technologies into a unified framework.
What is the main research objective?
The objective is to propose and evaluate two specific frameworks (FoodSQRBlock and SmartNoshWaste) that leverage blockchain and AI to make food production data accessible and to optimize food surplus.
Which scientific methods are employed?
The author employs system architecture design, blockchain hashing, and reinforcement learning (specifically Q-learning) to model and optimize food waste patterns based on real-world consumption data.
What does the main body of the text cover?
The main body details the technical prerequisites, presents the architecture of the proposed frameworks, and includes experimental evaluations using cloud-based simulations and real-world potato consumption data.
Which keywords best describe this research?
The core keywords include blockchain, food supply chain, artificial intelligence, sustainability, smart cities, and reinforcement learning.
How does FoodSQRBlock differ from SmartNoshWaste?
FoodSQRBlock primarily focuses on digitizing and ensuring the traceability of food production data, whereas SmartNoshWaste extends this by adding a machine learning layer to actively minimize food waste.
What role does the 'nosh app' play in this research?
The nosh app serves as a real-world data source, providing anonymized consumption and wastage data on potatoes, which is used to evaluate the efficiency and prediction accuracy of the SmartNoshWaste framework.
Why is the f-value significant in the SmartNoshWaste framework?
The f-value represents the amount of food surplus that the reinforcement learning agent suggests reducing, which directly correlates to the reduction of overall food waste in a specific supply chain phase.
- Quote paper
- Somdip Dey (Author), 2022, An Introduction to Blockchain and AI in Food Supply Chain in Smart Cities. Reducing Waste, Munich, GRIN Verlag, https://www.grin.com/document/1194960
