Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models
Quoc-Hung Phan1, Van-Tung Nguyen1, Chi-Hsiang Lien1
1Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan.
Plants (Basel, Switzerland)
|February 25, 2023
Summary
Deep learning models, including Yolov5m with ResNet-101, achieved 100% accuracy classifying ripe and immature tomatoes. These advanced frameworks show promise for automated tomato harvesting systems.
Area of Science:
- Agricultural technology
- Computer vision
- Machine learning
Background:
- Accurate classification of tomato fruit ripeness and damage is crucial for efficient harvesting.
- Automated systems require robust image recognition capabilities.
Purpose of the Study:
- To develop and evaluate deep learning frameworks for classifying tomato fruit on the vine.
- To compare the performance of Yolov5m, ResNet50, ResNet-101, and EfficientNet-B0 for tomato classification.
Main Methods:
- Four deep learning frameworks were proposed: Yolov5m, and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0.
- A dataset of 4500 tomato images was used for training over 200 epochs with a batch size of 128 and image size of 224x224 pixels.
Main Results:
- Yolo5m combined with ResNet-101 achieved 100% accuracy for ripe and immature tomatoes.
- Yolo5m with EfficientNet-B0 reached 94% accuracy for damaged tomatoes.
- Testing accuracies for ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 were 98%, 98%, 97%, and 97%, respectively.
Conclusions:
- All four proposed deep learning frameworks demonstrate high potential for tomato fruit classification.
- These models can support the development of automated tomato fruit harvesting applications in agriculture.
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