Ripen Banana Dataset: A Comprehensive Resource for Carbide Detection and Ripening Stage Analysis to Enhance Food Quality and Agricultural Efficiency

We introduce the “Ripen Banana” dataset, a newly developed collection featuring two distinct classes of ripen banana images: carbide and non-carbide. The dataset contains images from raw to ripe bananas that have been ripened with carbide and without carbide. The images collected from various locations in Sirajganj, Bangladesh. It contains 1404 original images and 6410 augmented images, resulting in 7814 images overall. The original images were taken against carefully selected white backgrounds. This was made it by evaluating these images for their sharpness and brightness levels to ensure they meet the quality standards that are required. After that, we organize each image into specified subfolders to ensure simple and fast access for large range of machine learning and deep learning models. This dataset offers significant opportunities for advancements in agricultural practices, nutrition and the food chain, food environment monitor, and computer vision technologies. This “Ripen Banana”

dataset can be utilized to facilitate data-driven methods in food inspection, banana ripening systems, and post-harvest management. The dataset documents the stages of ripening, offering an opportunity to improve agricultural practices, improve the quality and safety of fruit production, and protect the food supply chain. The “Ripen Banana” dataset can be a valuable resource for future study in the fields of food science and nutrition, among people around machine learning background.