Deep Ensemble Learning with YOLO and RESNET for Calamansi Ripeness Classification

Abstract

Nowadays, traditional calamansi ripeness classification relies on visual inspection by agricultural workers, which is a labor-intensive task and unpractical for large-scale farms. To turn modernization, this study introduces a digital ripeness classification approach leveraging deep learning ensemble. The initial stage is designed to detect the whole lifecycle from inflorescence to fruit development, while the subsequent stage focuses on efficient ripeness classification. For rapid inference, YOLOv11 (You Look at Once) has been adopted. Additionally, RESNET (Residual Network) has been optimized to enhance the model's accuracy, precision, and recall. To confirm its effectiveness, the proposed model was evaluated against YOLO variants using a custom dataset in a real-world environment. Results indicate that the proposed approach has better performance than YOLOv11 and YOLOv12, achieving Precision, Recall, F1, and mAP @ 0.5 scores of 0.6035,0.7981,0.6872, and 0.623, respectively.