Monitoring of Banana Ripening Process and Identification of Banana type using FRCNN, LSTM, And Color Segmentation

This study evaluates the level of Ripeness, Type, and Ripeness Prediction of a Banana fruit. As image processing continues to adapt in numerous situations, it is only a matter of time that it will be used in the field of agriculture where it will provide an output which is akin to a human-level judgement. In addition, it will also use deep learning to predict when the banana would reach the next stage or stages of ripeness. The study will be beneficial to local farmers, markets, and traders of bananas as it will help in sorting their crops to ensure they are delivered and sold within the proper quality following the National Nutritional Council (NNC). This research aims to conduct the Image Processing and Prediction Analysis through a Non-destructive manner. Two Deep Learning models were used namely Faster Region-based Convolutional Neural Network and Long-Short Term Memory. The obtained overall accuracy of 96.57%, 98.25%, and 94.44% has been recorded for the Ripeness Stage Detection, Banana Type Detection, and Next Stage Prediction respectively.