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FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and

F M Javed Mehedi Shamrat¹, Rashiduzzaman Shakil², Mohd Yamani Idna Idris¹

¹Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

|September 10, 2024

Summary

A new dataset, FruitSeg30, aids deep learning in fruit segmentation and classification. Models trained on this dataset show high accuracy, improving agricultural technology and food industry applications.

Keywords:

Agriculture automation Computer vision Data annotation Dataset diversity Deep learning Fruit image Fruit segmentation Image classification

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Area of Science:

Computer Vision
Agricultural Technology
Deep Learning

Background:

Fruits are vital for human nutrition, necessitating efficient agricultural processes.
Accurate fruit classification and segmentation are critical for automated sorting and quality control, reducing costs and enhancing consistency.
Existing datasets may lack the diversity and quality required for robust deep learning models in fruit analysis.

Purpose of the Study:

To introduce the FruitSeg30_Segmentation Dataset & Mask Annotations, a novel dataset for fruit segmentation and classification.
To provide a diverse and high-quality image collection for training advanced deep learning models.
To establish new benchmarks in dataset quality and diversity for agricultural applications.

Main Methods:

Development of the FruitSeg30 dataset with 1969 images across 30 fruit classes.
Utilizing a U-Net architecture for deep learning model training.
Performance evaluation using metrics such as accuracy, precision, recall, F1-score, IoU, and Dice score.

Main Results:

The U-Net model achieved high performance metrics: 94.72% training accuracy, 92.57% validation accuracy, 94% precision, 91% recall, 92.5% F1-score, 86% IoU, and a 0.9472 Dice score.
The dataset demonstrated superior performance in segmentation tasks.
The results indicate the effectiveness of the FruitSeg30 dataset in advancing fruit segmentation capabilities.

Conclusions:

The FruitSeg30 dataset addresses a critical gap in resources for fruit image analysis.
The dataset enhances the potential of deep learning models in agricultural technology and the food industry.
This work sets new standards for dataset quality and diversity in fruit-related AI applications.