Prediction of Early Vigor from Overhead Images of Carinata Plants
Date
2019-03-20
Authors
Journal Title
Journal ISSN
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Publisher
ORCID
Type
Thesis
Degree Level
Masters
Abstract
Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security.
Early season vigor is signi cantly correlated with yields and is often used as an early indicator of tness in
breeding programs. Early vigor can be a useful indicator of the health and strength of plants with bene ts
such as improved light interception, reduced surface evaporation, and increased biological yield. However,
vigor is challenging to measure analytically and is often rated using subjective visual scoring. This traditional
method of breeder scoring becomes cumbersome as the size of breeding programs increase. In this study, we
used hand-held cameras tted on gimbals to capture images which were then used as the source for automated
vigor scoring. We have employed a novel image metric, the extent of plant growth from the row centerline,
as an indicator of vigor. Along with this feature, additional features were used for training a random forest
model and a support vector machine, both of which were able to predict expert vigor ratings with an 88:9%
and 88% accuracies respectively, providing the potential for more reliable, higher throughput vigor estimates.
Description
Keywords
machine learning, computer vision, carinata, agriculture
Citation
Degree
Master of Science (M.Sc.)
Department
Computer Science
Program
Computer Science