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Related Experiment Video

Updated: May 21, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

Tomato ripeness detection and fruit segmentation based on instance segmentation.

Jinfan Wei¹, Yu Sun^1,2, Lan Luo¹

¹College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in Plant Science

|May 19, 2025

Summary

This summary is machine-generated.

This study introduces ACP-Tomato-Seg, an improved YOLOv8s-seg model for precise tomato instance segmentation, enhancing robot picking in complex environments. The novel method significantly boosts accuracy in detecting and segmenting tomatoes, even with occlusion.

Keywords:

ACP-tomato-seg adaptive feature extraction complex field environments multi-scale features self-attention mechanism tomato instance segmentation

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Area of Science:

Computer Vision
Robotics
Agricultural Technology

Background:

Precision agriculture demands accurate fruit contour information for automated tasks like robotic picking.
Complex field conditions (lighting, occlusion, overlap) challenge existing fruit segmentation methods.

Purpose of the Study:

To develop an enhanced instance segmentation model for tomatoes to support precise robotic picking.
To improve the model's robustness in challenging environmental conditions.

Main Methods:

Proposed ACP-Tomato-Seg, an improved YOLOv8s-seg model incorporating Adaptive and Oriented Feature Refinement Module (AOFRM) and Custom Multi-scale Pooling module (CMPRD).
Integrated a partial self-attention module (PSA) for enhanced global context and detail extraction.
Utilized deformable and multi-directional asymmetric convolutions in AOFRM for shape and orientation feature extraction.
Employed self-defined pooling kernels in CMPRD for multi-scale feature extraction to differentiate tomato sizes and maturity levels.

Main Results:

ACP-Tomato-Seg achieved significant improvements over the original YOLOv8s-seg: mAP50 increased by 5.6% (bounding box) and 5.8% (mask), mAP50-95 by 8.3% (bounding box) and 8.5% (mask).
The model demonstrated superior performance and generalization ability on a public strawberry dataset (StrawDI_Db1), outperforming comparative methods.
Validation on a custom dataset of 1061 tomato images covering six ripeness categories confirmed the method's effectiveness.

Conclusions:

ACP-Tomato-Seg offers a robust and accurate solution for tomato instance segmentation, addressing challenges like occlusion and varying maturity.
The proposed method provides an effective approach for precise fruit detection and segmentation, crucial for advancing robotic picking in agriculture.
The model's strong performance and generalization capabilities highlight its potential for real-world agricultural applications.