LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 196 - 196

Published: Jan. 17, 2025

Accurate leaf segmentation and counting are critical for advancing crop phenotyping improving breeding programs in agriculture. This study evaluates YOLOv11-based models automated detection across spring barley, wheat, winter rye, triticale. The key focus is assessing whether a unified model trained on combined multi-crop dataset can outperform crop-specific models. Results show that the achieves superior performance bounding box tasks, with mAP@50 exceeding 0.85 crops 0.7 crops. Segmentation however, reveal mixed results, individual occasionally excelling recall These findings highlight benefits of diversity generalization, while emphasizing need larger annotated datasets to address variability real-world conditions. While improves unique characteristics may still benefit from specialized training.

Language: Английский

Capturing plant functional traits in coastal dunes using close-range remote sensing

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103159 - 103159

Published: April 1, 2025

Language: Английский

Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102951 - 102951

Published: Dec. 1, 2024

Language: Английский