A novel multimodal deep learning approach to shelf life estimation of agricultural produce

The UN reports that almost 700 million people can't afford food, while 1.3 billion tons of food are wasted yearly—sufficient to feed the hungry worldwide four times over, as per the Food and Agriculture Organization. Roughly 14% of food waste occurs during transport. To tackle this, the solution utilizes deep learning to predict agricultural produce decay, enabling timely interventions to reduce spoilage and enhance food accessibility and affordability. The development process started with the placement of tomatoes and strawberries in multiple locations with different conditions. Data such as temperature, humidity, and images were collected at intervals until decay. This data trained regression AI models to forecast decay, and detection AI models to isolate produce in large batches. The best models were selected through experimentation with various parameters. These models were then deployed a camera-attached Raspberry Pi prototype, which can be installed during transport, monitor produce, and alert supervisors if any spoilage is detected or predicted to occur soon, so that protective measures can be taken, and in turn prevent food waste and lower prices.