Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models

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 x 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.

Automated summary

In this study, four deep learning frameworks combining Yolov5m with ResNet50, ResNet-101, and EfficientNet-B0 were proposed for classifying tomato fruit on the vine into ripe, immature, and damaged categories. The prediction accuracy for ripe and immature tomatoes was found to be 100% when combining Yolov5m with ResNet-101, while the accuracy for damaged tomatoes was 94% using Yolov5m with the Efficient-B0 model. The testing accuracies of ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks were 98%, 98%, 97%, and 97% respectively. Thus, all four frameworks have the potential for automated tomato fruit classification in agriculture.