The banana plant, Musa Paradisiaca, produces fruit that is abundant in vitamins, minerals, and carbohydrates. The majority of farmers still examine the maturity of fruits by themselves. Image processing is a technique or approach that can be used to manipulate images into the needed data in order to gain specific information. The maturity of a fruit can now be identified more accurately through the application of image processing technologies. Regarding the color of a banana's skin, the maturity degree of the fruit has varied advantages. Green is favorable for regulating blood sugar levels, yellowish green is favorable for dieters, yellow has a high antioxidant content and is favorable for digestion, and yellow with brown specks is favorable for boosting the immune system and preventing cancer. Convolutional neural network (CNN) technology can be used as one of the most efficient ways for computers to learn from images. To choose fruit based on color intensity and gather the best fruit, individuals may be assisted by a creative solution utilizing computer vision and deep learning. This project uses computer vision based on viewing, identifying, and interpreting an image to measure the quantity and maturity of banana fruit. The system needs to go through stages to process each layer of the image that is provided in order to extract fruit from them. Deep learning and image-based computer vision will make the system to determine the quantity and maturity of fruit. After a person (user) uploads an image, this system uses a convolutional neural network (CNN) to assess fruit images. The computer compares the classifier image to a disk-stored image from the dataset. The system detects fruit ripeness with greater than 95% accuracy using the pretrained model VGG16. Based on their knowledge and dependability to ensure they buy fruit of good quality; it is anticipated that this technique would be useful in the hands of those who collect fruit.