The Design of a Deep Learning Model to Classify Passion Fruit for the Ageing Society

The objective of this research is to design of a deep learning model to classify passion fruit for the ageing society. The phenomenon of an increasingly aging population affects the way of life and well-being of all societies around the world. The growing trend of the elderly leads to risks and health problems. Many substances in the passion fruit can help with sleep problems, wrinkled skin, and lower high blood pressure. Eating purple passion fruit after harvest is found to be more sour than sweet, but the longer it is stored, the sweeter it tastes. It is expected that the 4-passion fruit group, which is categorized by post-harvest period, can be classified by convolutional neural networks (CNNs), which is a network in network (NiN) architecture in the Python program. The implications of the study are that classifying the passion fruit is appropriate for an ageing society. The NiN models can generate the accuracy of a trained dataset of 96.76% and a validation dataset of 95.89%. This model is used to make computer programs that make it easy to choose passion fruit and other fruits for the fruit juice industry.