Work

Adaptive Localization of Picking Points for Safflower Filaments Across Full Growth Stages in Unstructured Field Environments

Year: 2026

Type: article

Abstract: To address the challenges of low manual harvesting efficiency and high difficulty in automated picking of safflower filaments in the unstructured field environments of Xinjiang, this study proposes an intelligent harvesting method that integrates lightweight visual detection and adaptive localization. Firstly, a safflower image dataset covering multiple scenarios and growth stages was constructed. An improved lightweight detection model, named SSO-YOLO, was proposed based on the YOLOv11n model. By introducing the StarNet backbone network, the SEAttention mechanism, and structural optimization, this model achieves a high detection accuracy ([email protected] of 97.4%) while reducing the model size by 29.4% to 3.94 MB, significantly enhancing its deployment feasibility on mobile devices. Secondly, based on the detection results, an adaptive localization algorithm for picking points was developed. This algorithm achieves precise localization of picking points at the filament–flower head junction by integrating geometric analysis of filament growth posture, dynamic judgment of connection conditions, and intersection calculation of rotated bounding boxes. Experimental results demonstrate that this algorithm achieves an average localization success rate of 87.3% across various unstructured scenarios such as occlusion and backlighting, representing an improvement of approximately 10.7 percentage points over traditional methods. The estimation error for filament posture angle is merely 0.6°, and the localization success rate remains above 90% across the entire growth cycle. This study provides an efficient and robust visual solution for the automated harvesting of safflower filaments and offers valuable insights for advancing intelligent harvesting technologies for specialty cash crops.

Source: Horticulturae

DOI: 10.3390/horticulturae12020198

Authors: Bangbang Chen, Liqiang Wang, Jijing Lin, Baojian Ma, Lingfang Chen

Institution: Xinjiang Institute of Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,654