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

Gao, Junfeng, Westergaard, Jesper Cairo and Alexandersson, Erik (2021) Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields. In: Solanum tuberosum. Methods in Molecular Biology (2354). Springer, New York, pp. 273-299. ISBN 9781071616086

Full content URL: https://doi.org/10.1007/978-1-0716-1609-3_13

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Item Type:Book Section
Item Status:Live Archive

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 above-ground 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, pre-processing, 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.

Keywords:plant phenotyping, image analysis, fields, Potatoes, crops, image sensors, Drones, UAV/UAS, feature extraction, machine learning
Subjects:D Veterinary Sciences, Agriculture and related subjects > D415 Crop Production
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
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ID Code:46316
Deposited On:08 Oct 2021 14:53

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