Banana Fruit Ripeness Assessment with Transfer Learning Models to Achieve Sustainable Quality

Fruit ripeness is a crucial factor in agriculture as it dictates the quality of the fruit. Manually determining the maturity of fruit has various drawbacks, including the need for a lot of labor, time, and potential for inconsistency. One of the most important economic sectors in the world is agriculture. Still, there are instances when the manual method of judging fruit maturity is utilized. Fruit ripeness may be automatically classified with the emerging technologies of artificial intelligence. A hybrid deep-learning approach is suggested in this study for fruit ripeness identification. In order to determine when bananas are ripe, this study uses deep learning models called Xception and Inception that have already been trained. First, the input photographs were processed using both models to extract primary characteristics. Then, the pictures were categorized according to the degree of ripeness of the bananas. Incorporating Co-Lab and other deep learning software, the models were put into action. The Xception pre-trained network achieved a classification accuracy of 98.99%, significantly outperforming state-of-the- art models in a performance evaluation.