Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models.

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.

Plant phenotyping relevance

トマト果実の成熟度・損傷状態という植物器官の状態を、深層学習画像分類で推定する手法が研究の中心であり、収穫ロボット用途でも単なる位置検出を超えた表現型推定に該当する。

Code and data availability

The paper's tomato image dataset (1508 images collected from farms in Taiwan) is paper-specific and qualifies as a phenotyping asset, but the Data Availability Statement says it is only available on request, with no public deposit or URL. No author code, models, or other public assets are mentioned.

No evidence-backed public reproduction asset is currently recorded.