Training a new instrument to measure cotton fiber maturity using transfer learning

Chris Turner, Hamed Sari-Sarraf, Eric Hequet

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This paper presents a novel transfer learning regression method that utilizes data from an older instrument to train a new instrument to assess the same measurement. The method assumes that the instruments measure the same property but by different methodologies, and that samples presented to one apparatus are not available to the other. The algorithm makes use of a single feature common to both instruments to create a link with which to transfer information regarding the distribution of the resulting measurements, or labels. The goal is to generate a model in the domain of the new instrument that maps data from analyzed samples to an output measurement. This modeling process is accomplished through an iterative algorithm that supports many types of regression schemes. Results are shown using both synthetic and real world data sets, which demonstrate the effectiveness of the proposed method. Finally, we present how this technique is used to train a new instrument designed to measure cotton fiber maturity.

Original languageEnglish
Article number7893700
Pages (from-to)1668-1678
Number of pages11
JournalIEEE Transactions on Instrumentation and Measurement
Issue number7
StatePublished - Jul 2017


  • Cotton fiber maturity
  • Histogram specification
  • Machine learning
  • Regression
  • Transfer learning


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