Work

Classification of Banana Ripeness Degree Using Yolo-v8

Year: 2025

Type: article

Abstract: ABSTRACT The ripeness level of bananas is a key factor for both producers and consumers, affecting the entire logistics chain of the fruit and its final price. Artificial intelligence techniques can be applied to enhance the efficiency of these processes. This article presents BananaRipe, a web application with mobile-friendly computer vision features designed to determine the ripeness level of bananas using a YOLO-v8-based classification model. In particular, a web application was developed in Python using the Flask microframework, integrated with a classification model trained on a dataset containing eight banana-ripeness classes. Test results showed that BananaRipe achieved 95.96% accuracy and 97.59% recall, demonstrating highly satisfactory compared to other models in the literature.

Source: Engenharia Agrícola

DOI: 10.1590/1809-4430-eng.agric.v45nespe120240193/2025

Authors: Emannuel D. G. de Freitas, José L. Martins Neto, João P. H. Neves, Danielo G. Gomes

Institution: Instituto Federal de Educação, Ciência e Tecnologia do Ceará

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $95