Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field

Author:

Smith Lyndon Neal,Zhang Wenhao,Hansen Mark F.ORCID,Hales Ian JohnORCID,Smith Melvyn Lionel

Funder

Innovate UK

Biotechnology and Biological Sciences Research Council

Branston Limited

UWE Bristol

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference21 articles.

1. 3-D imaging systems for agricultural applications—a review;Vázquez-Arellano;Sensors,2016

2. Applied machine vision of plants: a review with implications for field deployment in automated farming operations;McCarthy;Intell. Serv. Robot.,2010

3. Sensors and systems for fruit detection and localization: a review;Gongal;Comput. Electron. Agric.,2015

4. Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland;Binch;Comput. Electron. Agric.,2017

5. Potato grading method of weight and shape based on imaging characteristics parameters in machine vision system;Wang;Trans. Chin. Soc. Agric. Eng.,2016

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