Work

LT-YO-AN: lightweight YOLOv12 with asymmetric neck for apple fruitlet detection in complex orchard environments

Year: 2026

Type: article

Abstract: Accurate detection of apple fruitlets in complex orchard environments is a prerequisite for precision fruit thinning but remains challenging due to small target sizes, variable illumination, and dense foliage. To address these issues, a lightweight YOLOv12 architecture, termed LT-YO-AN, was proposed. The backbone integrates RepDown re-parameterizable downsampling and D3C2f aggregation modules, enabling hierarchical feature extraction with reduced spatial redundancy and channel-level compression. An asymmetric neck is introduced to explicitly enhance the high-resolution P2 layer for small-target sensitivity, while deliberately weakening the P5 layer via channel reduction (256 to 128) to alleviate redundancy. This design is complemented by a four-scale detection head (P2-P5) and a width scaling strategy (factor = 0.25), which together allocate computational resources preferentially to shallow features, yielding a compact model optimized for apple fruitlet detection. Evaluations on a real-world orchard dataset demonstrate that LT-YO-AN achieves an mAP50-95 of 0.675 and a recall of 0.860, outperforming benchmark YOLO variants. Although the computational complexity increases to 11.5 GFLOPs, the model maintains a compact size of 3.5 MB with 1.49 M parameters, making it suitable for edge deployment. This study provides a viable technological solution for early-stage fruit detection and automated orchard management.

Source: Frontiers in Plant Science

DOI: 10.3389/fpls.2026.1958019

Authors: Dandan Wang, Bo Wang

Institutions: Xi'an University of Science and Technology, Xi'an Jiaotong University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,801