Tomatoes Maturity Detection Approach Based on YOLOv5 and Attention Mechanisms
Author:
Affiliation:
1. Shanxi Agricultural University,School of Software,Jinzhong,China,030801
Funder
Shanxi Agricultural University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9986511/9986516/09986640.pdf?arnumber=9986640
Reference16 articles.
1. Deep Learning Based Improved Classification System for Designing Tomato Harvesting Robot[J];li;IEEE Access,2018
2. Using Fuzzy Mask R-CNN Model to Automatically Identify Tomato Ripeness
3. Improved DenseNet convolutional neural network for tomato leaf disease detection[J];niu;Journal of Jiangsu Agriculture Science,2022
4. Tomato crop disease classification using pre-trained deep learning algorithm
5. A YOLO v4+HSV-based method for tomato identification at maturity[J];li;Journal of Agricultural Engineering,2021
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Tomato maturity stage prediction based on vision transformer and deep convolution neural networks;International Journal of Hybrid Intelligent Systems;2024-06-25
2. Multi-task Tomato Fruit and Bunch Maturity Detection Approach Based on Improved YOLOv7;2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS);2023-08-12
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