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).

In recent years, there has been an increasing consumer demand for fresh fruits of improved quality. Nowadays, pomegranate fruits attained increased popularity because of their high nutritional values and pharmacological characteristics. In order to assess the fruit's quality, the prediction of physiochemical parameters is necessary. Thus, the proposed work utilized a deep learning model for prediction analysis. The proposed work has studied a non-destructive determination of physiochemical parameters for pomegranate fruits. The internal images of the pomegranate fruit are obtained using magnetic resonance imaging (MRT). Then, the fruit quality is determined by predicting the physiochemical parameters like TSS, pH, acidity and firmness by combining the features of physiochemical and the GLCM (Gray Level Co-occurrence Matrix). The proposed study used the LSTM (Long-Short Term Memory) model and Stacked Dense Deep LSTM (SDD-LSTM) for prediction purposes. The performance of both the LSTM and SDD-LSTM based prediction models is measured by evaluating the metrics like a square of the correlation coefficient (r 2 ), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). Also, the obtained results of the proposed prediction models, along with the combined features, are compared with other machine learning techniques. The result analysis shows that the proposed SDD-LSTM and GLCM have a higher potential for non-destructive assessment of physicochemical values of pomegranate fruit, which may be helpful in fruit post-harvest management.