Classification of pineapple ripeness using YOLOv8 and convolutional neural networks under varied environmental conditions - Mihuandayani, Mihuandayani; Prasetya, Syadina Arya; Mamuaya, Supit; Tomatala, Michel Farrel; Tuntun, Ritham (Edelweiss Applied Science and Technology, 2025)
Proper fruit harvesting timing is crucial in agriculture to ensure optimal product quality. Particularly, manually determining fruit ripeness requires significant time and expertise from farmers. Inaccuracies in harvest timing often lead to resource wastage and lower crop quality. Simultaneously, advancements in image-based classification technology offer promising solutions to address these challenges. Convolutional Neural Networks (CNN) are powerful deep learning architectures effective in recognizing complex patterns in image data, enabling high-accuracy visual information processing. YOLOv8 (You Only Look Once version 8) represents a recent implementation of object detection algorithms renowned for its ability to swiftly and accurately detect objects in real-time. Many studies have used limited data under controlled conditions. Additionally, there is a lack of research exploring how YOLOv8 and CNN models can be adapted to various environmental conditions, such as natural lighting and diverse backgrounds. This study proposes the integration of CNN with YOLOv8 to autonomously classify fruit ripeness stages, specifically focusing on pineapples. This method facilitates automated detection and classification of fruit ripeness, thereby enhancing harvest management efficiency for farmers. Performance testing of the YOLOv8 system yielded promising results with a mean Average Precision (mAP) of 88.5%, Precision of 78.4%, and Recall of 84.2%. These findings affirm the system’s capability to consistently and accurately assess pineapple ripeness across various field conditions. By harnessing CNN and YOLOv8 technologies, we introduce an innovative approach to fruit harvesting management applicable in modern agricultural practices.