Shelf Life Prediction of Post-Harvested Pomegranate using Enhanced Deep Learning

Abstract

One of the major requirements of agriculture is the quality assessment and ripeness of the agricultural products using non-destructive techniques. The ability of deep learning (DL) models to accurate classification and prediction are increasing. The work's major aim is to use the DL model to predict the shelf life of pomegranate fruits. Initially, the input MRI pomegranate images are resized to the proper size. Then, the features are extracted and classified using the deep learning model (DL) S-ResNet-152 (Squeeze based ResNet-152). This DL model classifies fruits as healthy or unhealthy. Further, for optimizing the layers and minimizing the loss function, the metaheuristic optimization improved sandpiper optimization (ISO). Then, the healthy fruits are considered for the prediction process. Here, the pomegranate fruit's shelf life is predicted using the DL model. The features like physiochemical and physiological loss in weight (PLW) and Firmness are predicted for determining the fruit quality. These features are given as input to the hybrid DL model bidirectional gated auto network (Bi-GRU-AN) is used for the prediction of shelf life. The performance of the proposed classification and prediction results are compared with other DL models in terms of the square of correlation coefficient (R 2 ), root mean square error of calibration (RMSEC), and root mean square error of validation (RMSEV).