Abstract
Inefficient logistics concerning handling, transport and storage solutions results in 44% of fresh food volume in Southeast Asia being lost post-harvest. This figure is compounded by the urgent agricultural and ecological challenges of our time that highlight the pressing need for more resilient local food economies. The research here aims to present an evaluation and exploration of the possibilities of data-driven approaches for fresh food management. Traditional food quality assessments are subjective, intuition-based and lead to suboptimal margins along with immense food wasted due to variability in decision-making. Through utilizing algorithms combining spectral and olfactory sensors to capture internal quality parameters of food and proprietary machine learning models, the solution provides an accurate assessment of ripeness, spoilage and shelf life that renders actionable insights and business intelligence on the optimal time and price to sell the produce. Moreover, metrics like storage conditions, transport duration can also be determined. This in turn enables stakeholders to reduce losses and capture more optimal margins from the same produce. Integrating sensory tech with analytics prediction systems have applications that range from smart logistics, warehouse, and retail management of fresh food and could spell the potential for leaner, robust food supply chains while raising levels of self-sufficiency in urban clusters.