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      • HARVEST
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      Prediction of Early Vigor from Overhead Images of Carinata Plants

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      DAS-THESIS-2019.pdf (18.98Mb)
      Date
      2019-03-20
      Author
      Das, Anupama
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      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.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      stanley, kevin; stavness, ian
      Committee
      keil, mark; eramian, mark; bett, kirstin
      Copyright Date
      February 2019
      URI
      http://hdl.handle.net/10388/11925
      Subject
      machine learning, computer vision, carinata, agriculture
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