Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards

TL;DR

This paper presents a novel integration of YOLO11 with CBAM for multi-season segmentation of tree trunks and branches in apple orchards, improving detection accuracy across seasonal variations.

Contribution

The study introduces a customized YOLO11-CBAM model trained on multi-season data, demonstrating enhanced segmentation performance for orchard tree components year-round.

Findings

YOLO11m-seg with CBAM achieved 0.83 precision for trunks.

YOLO11x-seg had 0.91 precision in dormant season validation.

Canopy season validation showed YOLO11s-seg with 0.64 precision for trunks.

Abstract

In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, trained on a mixed dataset of dormant and canopy season apple orchard images, aimed to enhance the segmentation of tree trunks and branches under varying seasonal conditions throughout the year. The model was individually validated across dormant and canopy season images after training the YOLO11-CBAM on the mixed dataset collected over the two seasons. Additional testing of the model during pre-bloom, flower bloom, fruit thinning, and harvest season was performed. The highest recall and precision metrics were observed in the YOLO11x-seg-CBAM and YOLO11m-seg-CBAM respectively. Particularly, YOLO11m-seg with CBAM showed the highest precision of 0.83 as performed for the Trunk class in training, while without the CBAM, YOLO11m-seg achieved 0.80 precision score for the Trunk class. Likewise, for branch class, YOLO11m-seg with CBAM achieved the highest precision score value of 0.75 while without the CBAM, the YOLO11m-seg achieved a precision of 0.73. For dormant season validation, YOLO11x-seg exhibited the highest precision at 0.91. Canopy season validation highlighted YOLO11s-seg with superior precision across all classes, achieving 0.516 for Branch, and 0.64 for Trunk. The modeling approach, trained on two season datasets as dormant and canopy season images, demonstrated the potential of the YOLO11-CBAM integration to effectively detect and segment tree trunks and branches year-round across all seasonal variations. Keywords: YOLOv11, YOLOv11 Tree Detection, YOLOv11 Branch Detection and Segmentation, Machine Vision, Deep Learning, Machine Learning