# Fine-Grained Age-Class Identification of Moso Bamboo Using an Improved Lightweight YOLO11 Model

## TL;DR

This paper introduces a lightweight AI model for accurately identifying age classes of moso bamboo using close-range images, improving both accuracy and efficiency.

## Contribution

The novel YOLO11-GCR model integrates Ghost convolution, CBAM, and RFB for efficient and accurate fine-grained age-class classification of moso bamboo.

## Key findings

- The YOLO11-GCR model achieved an mAP@0.5 of 0.913 and an mAP@0.5–0.95 of 0.895 on an independent test set.
- The model has 2.62 × 10⁶ parameters and 6.2 GFLOPs, making it computationally efficient.
- It shows improved classification stability for visually similar age classes like II-du and III-du.

## Abstract

Accurate identification of moso bamboo (Phyllostachys edulis) age classes is essential for effective forestry resource management, yet existing methods often struggle to achieve a satisfactory balance between accuracy and computational efficiency under complex field conditions. To address this challenge, this study proposes a lightweight object detection model, termed YOLO11-GCR, for fine-grained moso bamboo age-class classification based on close-range imagery. The proposed approach builds upon the YOLO11 framework and incorporates Ghost convolution, the Convolutional Block Attention Module (CBAM), and a Receptive Field Block (RFB) to reduce model complexity, enhance discriminative feature representation, and improve sensitivity to subtle texture variations among age classes. A dataset consisting of 9538 annotated bamboo culm images covering four age classes (I-du to IV-du) was constructed and divided into training, validation, and independent test sets with strict spatiotemporal separation. Experimental results indicate that YOLO11-GCR achieves robust detection performance with a lightweight architecture of 2.62 × 106 parameters and 6.2 GFLOPs, yielding an mAP@0.5 of 0.913 and an mAP@0.5–0.95 of 0.895 on the independent test set. Notably, the model demonstrates improved classification stability for visually similar age classes, such as II-du and III-du. Overall, this study presents an efficient and practical imaging-based solution for automated moso bamboo age-class recognition in complex natural environments.