PERANCANGAN SISTEM DETEKSI KEMATANGAN BUAH NAGA MERAH BERBASIS WEB MENGGUNAKAN METODE ALGORITMA YOLO (YOU ONLY LOOK ONCE) - Nurhayati, Nurhayati; Kusuma, Aditya (JUTECH : Journal Education and Technology, 2025)
Artificial intelligence (AI) plays a crucial role in modern agriculture, particularly through digital image processing that enhances efficiency and accuracy in decision-making. This research focuses on the development of a deep learning- based dragon fruit ripeness detection application using the YOLOv8 algorithm. The dragon fruit produced from the Hylocereus and Selenicereus cacti has high economic value, but it requires proper post-harvest detection. The manual process of determining the ripeness of dragon fruit is often inaccurate and time-consuming, making AI-based solutions essential. The YOLOv8 algorithm, known for its ability to detect objects in real-time with high accuracy, is implemented in this application to classify the ripeness of dragon fruit. This research successfully designed a system capable of accurately detecting the ripeness of dragon fruit using the Yolo Algorithm. (You Only Look Once). The designed web application provides an intuitive user interface that makes it easy for users to make real-time decisions through internet-connected devices. By simplifying the maturity determination process, this solution can help accelerate the assessment and sorting of dragon fruit maturity.