ArticlePlants (Basel, Switzerland)2023
Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models.
Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.Scientific reports · 2025Article
- A machine learning approach for classifying date fruit varieties at the Rutab stage.Frontiers in plant science · 2025Article
- GAE-YOLO: a lightweight multimodal detection framework for tomato smart agriculture with edge computing.Frontiers in plant science · 2025Article
- Identifying the Growth Status of Hydroponic Lettuce Based on YOLO-EfficientNet.Plants (Basel, Switzerland) · 2024Article
- An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network.Current research in food science · 2024Article
- Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model.Plants (Basel, Switzerland) · 2023Article
- High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.Plants (Basel, Switzerland) · 2023Article
- A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.Plants (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.
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Registered trials
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