Articles
Application of artificial intelligence in reducing postharvest loss through sustainable valorisation: advancing circular economy and resource efficiency
Article number
1466_60
Pages
495 – 502
Language
English
Abstract
Postharvest losses are responsible for a large percentage of global food waste and occur in various phases, which include handling, storage, processing, transportation, and retailing.
These losses are major contributors to greenhouse gas emissions, economic losses, and wastage of resources.
Artificial intelligence (AI) has become a revolutionary tool for preventing postharvest loss through valorisation of unavoidable waste streams in a circular economy.
Therefore, this study explored the application of AI-driven valorisation of postharvest waste for postharvest loss reduction.
Recent advancements in AI-based approaches have proven impactful in postharvest monitoring, prediction, sorting, process improvements, storage management, and supply chain optimisation.
The application of AI-driven technologies, such as the Internet of Things, computer vision, machine learning, and digital twins, enables real-time monitoring, shelf-life extension, spoilage reduction, and logistics enhancement.
Furthermore, the integration of AI into valorisation pathways improves the conversion of damaged or surplus postharvest biomass into high-value products such as biofuels, enzymes, bioactive molecules, and biopolymers.
However, studies on sustainability assessment of AI-driven valorisation, especially the economic and environmental impacts, face challenges related to data limitations, policy gaps, system interoperability, and stakeholder acceptance.
Therefore, this study highlights research gaps and offers recommendations that demand researchers’ attention to enhance AI adoption in reducing postharvest losses and promoting sustainable resource recovery.
These losses are major contributors to greenhouse gas emissions, economic losses, and wastage of resources.
Artificial intelligence (AI) has become a revolutionary tool for preventing postharvest loss through valorisation of unavoidable waste streams in a circular economy.
Therefore, this study explored the application of AI-driven valorisation of postharvest waste for postharvest loss reduction.
Recent advancements in AI-based approaches have proven impactful in postharvest monitoring, prediction, sorting, process improvements, storage management, and supply chain optimisation.
The application of AI-driven technologies, such as the Internet of Things, computer vision, machine learning, and digital twins, enables real-time monitoring, shelf-life extension, spoilage reduction, and logistics enhancement.
Furthermore, the integration of AI into valorisation pathways improves the conversion of damaged or surplus postharvest biomass into high-value products such as biofuels, enzymes, bioactive molecules, and biopolymers.
However, studies on sustainability assessment of AI-driven valorisation, especially the economic and environmental impacts, face challenges related to data limitations, policy gaps, system interoperability, and stakeholder acceptance.
Therefore, this study highlights research gaps and offers recommendations that demand researchers’ attention to enhance AI adoption in reducing postharvest losses and promoting sustainable resource recovery.
Publication
Authors
K.A. Jimoh, O.A. Fawole
Keywords
artificial intelligence, sustainability, postharvest loss, bioresources, bioeconomy
Online Articles (74)