Emergency obstacle avoidance system of sugarcane basecutter based on improved YOLOv5s

Author:

Huo PengORCID,Ma ShaochunORCID,Su Chenyang,Ding Zhengliang

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference28 articles.

1. Vision-based obstacle detection and navigation for an agricultural robot;Ball;J. Field Rob.,2016

2. Real-time detection methodology for obstacles in orchards using improved YOLOv4;Cai;Trans. Chinese Soc. Agri. Eng.,2021

3. Canziani, A., Paszke, A., Culurciello, E., 2016. An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678. 10.48550/arXiv.1605.07678.

4. Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots;Champ;Appl. Plant Sci.,2020

5. Apple inflorescence recognition of phenology stage in complex background based on improved YOLOv7;Chen;Comput. Electron. Agric.,2023

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