Leguminous seeds detection based on convolutional neural networks: Comparison of Faster R-CNN and YOLOv4 on a small custom dataset-Reference-Cited by-同舟云学术

Leguminous seeds detection based on convolutional neural networks: Comparison of Faster R-CNN and YOLOv4 on a small custom dataset

Published:2023-06 Issue: Volume:8 Page:30-45
ISSN:2589-7217
Container-title:Artificial Intelligence in Agriculture
language:en
Short-container-title:Artificial Intelligence in Agriculture

Author:

Ouf Noran S.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Agricultural and Biological Sciences,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference20 articles.

1. YOLOv4: Optimal Speed and Accuracy of Object Detection;Bochkovskiy,2020

2. Engineering applications of neural networks,2017

3. DLBench: a comprehensive experimental evaluation of deep learning frameworks;Elshawi;Clust. Comput.,2021

4. Fast R-CNN;Girshick,2015

5. Rich feature hierarchies for accurate object detection and semantic segmentation;Girshick;Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.,2013

Cited by 6 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. Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI;Scientific Reports;2024-07-06

2. Grow-light smart monitoring system leveraging lightweight deep learning for plant disease classification;Artificial Intelligence in Agriculture;2024-06

3. Fruit recognition, task plan, and control for apple harvesting robots;Revista Brasileira de Engenharia Agrícola e Ambiental;2024

4. Detection of Broken Hongshan Buckwheat Seeds Based on Improved YOLOv5s Model;Agronomy;2023-12-22

5. Integrating blockchain and deep learning for intelligent greenhouse control and traceability;Alexandria Engineering Journal;2023-09