Plant trait estimation and classification studies in plant phenotyping using machine vision – A review

Machine vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

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Machine Vision System for 3D Plant Phenotyping

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2018.2824814 ◽

2019 ◽

Vol 16 (6) ◽

pp. 2009-2022 ◽

Cited By ~ 14

Author(s):

Ayan Chaudhury ◽

Christopher Ward ◽

Ali Talasaz ◽

Alexander G. Ivanov ◽

Mark Brophy ◽

...

Keyword(s):

Machine Vision ◽

Vision System ◽

Plant Phenotyping ◽

Machine Vision System

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Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping

Remote Sensing ◽

10.3390/rs13020276 ◽

2021 ◽

Vol 13 (2) ◽

pp. 276

Author(s):

Raja Manish ◽

Yi-Chun Lin ◽

Radhika Ravi ◽

Seyyed Meghdad Hasheminasab ◽

Tian Zhou ◽

...

Keyword(s):

Plant Traits ◽

Plant Phenotyping ◽

Unmanned Aerial Systems ◽

Mobile Mapping ◽

Ground Vehicle ◽

Plant Trait ◽

Mapping System ◽

Mobile Mapping System ◽

Using Data ◽

Aerial Systems

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved.

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3D machine vision system for robotic weeding and plant phenotyping

10.31274/etd-180810-201 ◽

2014 ◽

Cited By ~ 1

Author(s):

Ji Li

Keyword(s):

Machine Vision ◽

Vision System ◽

Plant Phenotyping ◽

Machine Vision System ◽

Robotic Weeding

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Automated plant phenotyping using 3D machine vision and robotics

10.31274/etd-180810-5944 ◽

2018 ◽

Author(s):

Yin Bao

Keyword(s):

Machine Vision ◽

Plant Phenotyping ◽

And Robotics

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Machine Vision

10.1017/cbo9781139168229 ◽

2009 ◽

Cited By ~ 15

Author(s):

Wesley E. Snyder ◽

Hairong Qi

Keyword(s):

Machine Vision

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Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

Light & Engineering ◽

10.33383/2018-135 ◽

2018 ◽

pp. 143-149 ◽

Cited By ~ 1

Author(s):

Ruijie CHENG

Keyword(s):

Control System ◽

Machine Vision ◽

Energy Saving ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Lighting Control ◽

Lighting Energy ◽

Neural Network Algorithm ◽

Energy Saving Control ◽

Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multifeedback model for energysaving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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