Literature DB >> 34448165

Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields.

Junfeng Gao1, Jesper Cairo Westergaard2, Erik Alexandersson3.   

Abstract

Field phenotyping of crops has recently gained considerable attention leading to the development of new protocols for recording plant traits of interest. Phenotyping in field conditions can be performed by various cameras, sensors, and imaging platforms. In this chapter, practical aspects as well as advantages and disadvantages of aboveground phenotyping platforms are highlighted with a focus on drone-based imaging and relevant image analysis for field conditions. It includes useful planning tips for experimental design as well as protocols, sources, and tools for image acquisition, preprocessing, feature extraction, and machine learning highlighting the possibilities with computer vision. Several open and free resources are given to speed up data analysis for biologists.This chapter targets professionals and researchers with limited computational background performing or wishing to perform phenotyping of field crops, especially with a drone-based platform. The advice and methods described focus on potato but can mostly be used for field phenotyping of any crops.
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Crops; Drone; Feature extraction; Field; Image analysis; Imaging sensors; Machine learning; Plant phenotyping; Potato; UAV/UAS

Mesh:

Year:  2021        PMID: 34448165     DOI: 10.1007/978-1-0716-1609-3_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  3 in total

Review 1.  Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.

Authors:  Noah Fahlgren; Malia A Gehan; Ivan Baxter
Journal:  Curr Opin Plant Biol       Date:  2015-02-27       Impact factor: 7.834

2.  A computational image analysis glossary for biologists.

Authors:  Adrienne H K Roeder; Alexandre Cunha; Michael C Burl; Elliot M Meyerowitz
Journal:  Development       Date:  2012-09       Impact factor: 6.868

3.  Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

Authors:  Ziyi Liu; Junfeng Gao; Guoguo Yang; Huan Zhang; Yong He
Journal:  Sci Rep       Date:  2016-02-11       Impact factor: 4.379

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.