@inbook{a5152e9471e84c42853281b49c9a82ea,
title = "Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields",
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.",
keywords = "Crops, Drone, Feature extraction, Field, Image analysis, Imaging sensors, Machine learning, Plant phenotyping, Potato, UAV/UAS, Crops, Drone, Feature extraction, Field, Image analysis, Imaging sensors, Machine learning, Plant phenotyping, Potato, UAV/UAS",
author = "J. Gao and J.C. Westergaard and E. Alexandersson",
year = "2021",
doi = "10.1007/978-1-0716-1609-3\_13",
language = "English",
isbn = "978-1-0716-1608-6",
series = "Methods in Molecular Biology",
publisher = "Springer",
number = "2354",
pages = "273--299",
booktitle = "Solanum tuberosum : Methods and Protocols",
address = "Germany",
}