Faster R–CNN–based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting-Reference-Cited by-同舟云学术

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Faster R–CNN–based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting

Published:2020-09 Issue: Volume:197 Page:245-256
ISSN:1537-5110
Container-title:Biosystems Engineering
language:en
Short-container-title:Biosystems Engineering

Author:

Fu Longsheng^ORCID,Majeed Yaqoob^ORCID,Zhang Xin,Karkee Manoj,Zhang Qin^ORCID

Funder

China Postdoctoral Science Foundation

Key Research and Development Program in Shaanxi Province of China

National Natural Science Foundation of China

International Scientific and Technological Cooperation Foundation of Northwest A&F University

Publisher

Elsevier BV

Subject

Soil Science,Agronomy and Crop Science,Food Science,Control and Systems Engineering

Reference52 articles.

1. Harvesting robots for high-value crops: State-of-the-art review and challenges ahead;Bac;Journal of Field Robotics,2014

2. Image segmentation for fruit detection and yield estimation in apple orchards;Bargoti;Journal of Field Robotics,2017

3. Agricultural robots for field operations: Concepts and components;Bechar;Biosystems Engineering,2016

4. Kiwifruit detection in field images using Faster R-CNN with ZFNet;Fu;IFAC-PapersOnLine,2018

5. A novel image processing algorithm to separate linearly clustered kiwifruits;Fu;Biosystems Engineering,2019

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