Deep CNN model for predicting shelf life of fresh fruits and vegetables using temperature simulation data for optimized transport and storage

India is one of the largest producers of fruits and vegetables globally, contributing about 14% of global production., a significant portion of this produce, approximately 30-40%, is lost due to inefficient storage and transportation systems, resulting in an economic loss of ₹92,651 crores (2018). The objective is to develop a deep learning-based model that uses temperature simulation data to accurately predict the shelf life of fresh fruits and vegetables, ensuring optimized transport and storage conditions. The title refers to a machine learning-based approach, specifically using deep CNN models, to predict how long fresh produce (fruits and vegetables) will remain viable based on temperature data collected during storage and transport. This system helps in making decisions to reduce spoilage and improve logistics. Traditionally shelf-life predictions relied on fixed temperature guidelines, manual quality checks, and general estimations from historical data, often leading to inaccuracies and higher wastage. Traditional systems lack precision and adaptability, resulting in inefficient management of fresh produce, leading to higher spoilage and economic losses during transportation and storage. This calls for a more dynamic approach. Reducing post-harvest losses is critical to ensuring food security, especially in India. Machine learning models can offer precise shelf-life predictions by incorporating real-time data, thereby reducing waste, improving economic returns, and ensuring fresher produce reaches consumers. A deep learning-based model, using a CNN trained on temperature data collected from storage and transport conditions, can predict the shelf life of fresh produce in real-time. This would allow stakeholders to adjust storage temperatures and optimize distribution routes, ensuring minimal wastage. The AI model can also provide alerts for optimal consumption times and transport decisions. This system will significantly improve efficiency and reduce the cost associated with spoilage.