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

Abstract

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

Plant phenotyping relevance

複数作物の葉を自動検出・セグメンテーションし、統合モデルと作物別モデルの性能を比較する画像解析手法が中心で、葉の抽出・計数という植物表現型取得に直接関係する。

Code and data availability

The supplied blocks describe a custom multi-crop leaf image dataset (robot and drone images with manual leaf annotations) and YOLOv11 training, but contain no public dataset deposit, no code availability statement, no repository URL, and no trained model release. No paper-specific public asset is identified.

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