AFBF-YOLO: An Improved YOLO11n Algorithm for Detecting Bunch and Maturity of Cherry Tomatoes in Greenhouse Environments.

Abstract

Accurate detection of cherry tomato clusters and their ripeness stages is critical for the development of intelligent harvesting systems in modern agriculture. In response to the challenges posed by occlusion, overlapping clusters, and subtle ripeness variations under complex greenhouse environments, an improved YOLO11-based deep convolutional neural network detection model, called AFBF-YOLO, is proposed in this paper. First, a dataset comprising 486 RGB images and over 150,000 annotated instances was constructed and augmented, covering four ripeness stages and fruit clusters. Then, based on YOLO11, the ACmix attention mechanism was incorporated to strengthen feature representation under occluded and cluttered conditions. Additionally, a novel neck structure, FreqFusion-BiFPN, was designed to improve multi-scale feature fusion through frequency-aware filtering. Finally, a refined loss function, Inner-Focaler-IoU, was applied to enhance bounding box localization by emphasizing inner-region overlap and focusing on difficult samples. Experimental results show that AFBF-YOLO achieves a precision of 81.2%, a recall of 81.3%, and an mAP@0.5 of 85.6%, outperforming multiple mainstream YOLO series. High accuracy across ripeness stages and low computational complexity indicate it excels in simultaneous detection of cherry tomato fruit bunches and fruit maturity, supporting automated maturity assessment and robotic harvesting in precision agriculture.

Plant phenotyping relevance

画像ベースの深層学習手法を開発し、トマト果実の成熟段階という植物状態を検出・評価しているため、収穫対象の単なる定位を超えた中心的な表現型計測研究である。

Code and data availability

The paper describes a custom cherry tomato dataset (486 RGB images, 150,000+ annotations) and the AFBF-YOLO model, but no block contains any data or code availability statement, public repository deposit, or authors' URL for the dataset, images, annotations, or trained model. The only repository-like URL (https://zenod

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