Deep learning model in fruit freshness detection

Fruits are usually used as complementary foods because they have various propertiesBut the harmful microorganism in the fruit causes the fruit to rot. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from ۵۵ to ۷۳ Watts during the training process and ۲۰ to ۲۷ Watts during the testing process. For accuracy, the result is ۹۸.۶۴%. However, compared to previous studies with the MobileNetV۲ model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F۱-score in Rotten Banana.