Research on segmentation method for Lycium barbarum garden images during non-fruiting period
Year: 2026
Type: article
Abstract: Introduction Accurate segmentation of plant-column and path regions is essential for visual navigation and baseline extraction of autonomous agricultural platforms in Lycium barbarum gardens. During the non-fruiting period, sparse foliage, exposed branches, fallen leaves, weeds, similar soil backgrounds, and variable illumination weaken the visual contrast between plant-column and path regions, rendering conventional segmentation methods based on color or texture unreliable. Methods We propose a deep learning-based semantic segmentation method tailored to non-fruiting L. barbarum garden images. A region-based annotation strategy was used to label continuous plant-column and path regions following the requirements for navigation baseline extraction. The encoder–decoder model incorporates depthwise separable convolutions, skip connections, and multi-scale contextual feature extraction to enhance feature representation of complex field scenes. Image data were collected from multiple gardens across different years, growth stages, lighting conditions, irrigation regimes, and soil types; data augmentation was implemented during model training. Model performance was validated using an independent internal test set containing 230 images, and cross-garden generalization was assessed on two unseen field sites with limited fine-tuning. Results On the internal test set, the model achieved precision of 95.24 ± 2.66%, recall of 94.68 ± 3.15%, mean intersection over union (mIoU) of 90.10 ± 5.43%, Dice coefficient of 94.68 ± 3.15%, and mean pixel accuracy (mPA) of 94.64 ± 3.14%. For the two unseen gardens, the raw mIoU reached 86.84% and 84.60%, and increased to 92.43% and 90.23% after fine-tuning with a small set of newly annotated images. Discussion The proposed method yields reliable region-level segmentation for non-fruiting * L. barbarum * garden scenes, alleviating domain shifts while preserving strong pixel-level and region-overlap performance. Minor boundary discrepancies may arise from annotation ambiguity. This approach offers solid technical support for navigation baseline extraction in agricultural robotic systems.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7225336171