Flutter-Based Real-Time Mobile App for Fruit Shelf-Life Prediction (FSP) Using Multi-Modality Imaging

Abstract

The present work uses flutter and transfer learning-based techniques for predicting the shelf-life of fruits in real-time by reducing the demand for huge number of samples and longer training durations. The work uses mango fruit as a case study, by capturing two types of images axiomatically through smartphone rear camera and a Seek thermal camera. A set of models based on transfer learning were created, then trained, and applied post-training quantization for optimizing the model size and low latency. The models were subsequently converted into TFLite format with TF-Select Ops and deployed in the Flutter app code, and thereby generating the APK files for real-time execution on mobile devices. The performance metrics of the models were evaluated on accuracy, model size, and latency. With a minor level of degradation in accuracy, the model with quantization outperforms and achieves better latency and 12x times reduction in size. The overall accuracy achieved for both the cameras (normal and Thermal) on mobile device was 90% and 93.33% respectively, for predicting the fruit shelf life.