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 trai...

Descripción completa

Detalles Bibliográficos
Autores principales: Phan, Quoc-Hung, Nguyen, Van-Tung, Lien, Chi-Hsiang, Duong, The-Phong, Hou, Max Ti-Kuang, Le, Ngoc-Bich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959894/
https://www.ncbi.nlm.nih.gov/pubmed/36840138
http://dx.doi.org/10.3390/plants12040790

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959894/
https://www.ncbi.nlm.nih.gov/pubmed/36840138
http://dx.doi.org/10.3390/plants12040790

Ejemplares similares