Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.

Abstract

Tomato growing points and flower buds serve as vital physiological indicators influencing yield quality, yet their detection remains challenging in complex facility environments. This study develops an improved YOLOv8 model for robust flower bud detection by first constructing a comprehensive multi-environment dataset covering 10 typical growing conditions with enhanced annotations. Three key innovations address YOLOv8's limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. Ablation experiments confirm these modifications synergistically improve adaptability to scale and environmental variations, achieving 97.8% mAP@0.5 (+ 0.5%) and 85.1% mAP@0.5:0.95 (+ 5.1%) with 11% fewer parameters. Practical deployment on agricultural robots in operational greenhouses demonstrated 93.6% detection accuracy, validating the model's effectiveness for precision agriculture applications. The proposed system achieves an optimal balance of accuracy, speed, and lightweight design while providing immediately applicable solutions for automated tomato monitoring.

Plant phenotyping relevance

トマトの生長点・花蕾という植物器官を対象に、複雑な環境での画像検出モデルを開発し、データセット構築、アブレーション、実環境ロボットでの検証まで行っており、植物フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a homemade multi-environment tomato growth point bud dataset and an improved YOLOv8-SEGW model, but contain no data availability statement, no public dataset deposit, and no author code/model release with a URL. The only URL present is the article's own DOI, which is excluded as the article

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