Journal Article

SSViT-YOLOv11: fusing lightweight YOLO & ViT for coffee fruit maturity detection

Yu, Qiudong, Guo, Shiyi, Liu, Yifan, Liu, Ling, Geng, Shuze

2025

Overview

Accurate identification of coffee fruit maturity is critical for optimizing harvest timing and ensuring bean quality, but manual inspection is time-consuming and prone to subjectivity. Automated visual detection faces challenges including subtle color differences among maturity stages, frequent occlusions within fruit clusters, variable lighting, and abundant small-scale targets. In this paper, we propose SSViT-YOLOv11, an improved YOLOv11n-based framework that integrates Single Scale Vision Transformer (SSViT) into the backbone and refines multi-scale feature fusion to enhance context modeling and small-object representation. The C3K2 modules in YOLOv11n are integrated with Arbitrary Kernel Convolution (AKConv) and multi-scale convolutional attention (MSCA) is added in the head, effectively improving detection accuracy and rendering the model more lightweight. Experimental results show that SSViT-YOLOv11 achieves superior performance across multiple evaluation metrics. Specifically, the model attains a precision of 81.1%, a recall of 77.4%, and a mean Average Precision (mAP@50) of 84.54%, while operating at 23 FPS and requiring only 2.16 million parameters. These results indicate that the proposed model offers a favorable balance of accuracy, inference speed, and model compactness, making it well suited for assisting farmers in coffee fruit maturity assessment.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / Algorithms / Coffee / coffee fruit maturity / Coffee industry / Computer vision / Cultivars / Deep learning / Design / Fruits / lightweight / Maturity / object detection / Optimization / Real time / vision transformer / YOLOv11