Smart Postharvest Management: Leveraging AI for Reduced Food Loss, Waste, and Improved Quality

Food loss continues to be a major global challenge that impacts environmental sustainability, economic stability and food security.An inventive strategy for lowering food loss across the supply chain is AI-driven monitoring.The foundation of human civilization has always been agriculture, which supplies the vital resources needed for growth and nutrition.Higher quality crops with improved nutritional value, increased resilience to pests and diseases and improved adaptability to varying climatic conditions are in greater demand as the world's population continues to grow.Despite their effectiveness, traditional agricultural methods frequently fail to effectively meet these objectives; therefore, an innovative strategy for raising crop quality is the incorporation of artificial intelligence (AI) into agricultural operations.This paper examines the role of AI-driven monitoring in reducing food loss, focusing on its applications, benefits and implications for the food industry.AI driven technologies like machine learning, IoT-based smart sensors and computer vision can enhance efficiency in food production, storage, transportation and retail.By utilizing AI-driven solutions, stakeholders can optimize resource utilization, reduce waste, and contribute to sustainable food systems.AIassisted processing can optimize various stages of crop production, from planting and growing to harvesting and postharvest management, thereby improving the overall quality of agricultural produce.

Authors

Publication Details

Journal
Journal of Agriculture and Rural Development Studies.
Published
2025-09-30
DOI
https://doi.org/10.35219/jards.2025.3.01
Citations
1
Primary Topic
Food Waste Reduction and Sustainability
Type
article
Field-Weighted Citation Impact
1.16
Controls
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article

Smart Postharvest Management: Leveraging AI for Reduced Food Loss, Waste, and Improved Quality

Akudo Francilia Onyegbula, Titilope Abosede Fashanu, Damilola Olubunmi Ariyo, Tawakalitu Ahmed et al.
1 citations
Journal of Agriculture and Rural Development Studies.
Food Waste Reduction and Sustainability
1.16
article

Smart Postharvest Management: Leveraging AI for Reduced Food Loss, Waste, and Improved Quality

Akudo Francilia Onyegbula, Titilope Abosede Fashanu, Damilola Olubunmi Ariyo, Tawakalitu Ahmed, Blessing Iyanuoluwa Adediran, Stephen Olufemi Oyeyipo
article en
1 citations

Abstract

Food loss continues to be a major global challenge that impacts environmental sustainability, economic stability and food security.An inventive strategy for lowering food loss across the supply chain is AI-driven monitoring.The foundation of human civilization has always been agriculture, which supplies the vital resources needed for growth and nutrition.Higher quality crops with improved nutritional value, increased resilience to pests and diseases and improved adaptability to varying climatic conditions are in greater demand as the world's population continues to grow.Despite their effectiveness, traditional agricultural methods frequently fail to effectively meet these objectives; therefore, an innovative strategy for raising crop quality is the incorporation of artificial intelligence (AI) into agricultural operations.This paper examines the role of AI-driven monitoring in reducing food loss, focusing on its applications, benefits and implications for the food industry.AI driven technologies like machine learning, IoT-based smart sensors and computer vision can enhance efficiency in food production, storage, transportation and retail.By utilizing AI-driven solutions, stakeholders can optimize resource utilization, reduce waste, and contribute to sustainable food systems.AIassisted processing can optimize various stages of crop production, from planting and growing to harvesting and postharvest management, thereby improving the overall quality of agricultural produce.

Journal of Agriculture and Rural Development Studies.Vol. 2(3)
Openalex Percentile: Top 17%
Food Waste Reduction and Sustainability
1.16
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