Optimizing band selection for spectral detection of Aphis glycines Matsumura in soybean

Tavvs M. Alves, Roger D. Moon, Ian V. MacRae, Robert L. Koch

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BACKGROUND: Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean in North America. Accurate estimation of A. glycines densities requires costly, time-intensive weekly counts of adults and nymphs on plants. Field studies were conducted in 2013 and 2014 to assess the potential for spectral-based remote sensing to more efficiently quantify cumulative aphid-days (CADs) using soybean canopy reflectance. RESULTS: Narrow-band wavelengths in the near-infrared spectral range were associated with CAD, but those in the visible spectral range were not associated with CAD. Simple linear regression models of CAD on reflectance were generally better than quadratic and cubic regression models. Simulated wide-band sensors centered at 740–1100 nm yielded better regression models than ones centered at 600–740 nm, regardless of bandwidth. Among the simulated wide-band sensors, increasing sensor bandwidth worsened CAD estimation or required more simulated sensors to optimize CAD estimation. Optimal combinations of spectral bands explained 83–96% of the experimentally manipulated variation in CAD. CONCLUSION: Near-infrared wavelengths at 780 ± 50 nm can effectively estimate A. glycines abundance on soybean. Our approach of simulating wide-band multispectral sensors from ground-based hyperspectral data helped to refine spectral sensors and holds potential to reduce the cost and complexity of treat/no-treat classification tasks. This study will contribute to future research aiming to quantify insect injury using customized commercial-grade sensors for detection, quantification, and differentiation of A. glycines from other stressors.

Original languageEnglish (US)
Pages (from-to)942-949
Number of pages8
JournalPest management science
Issue number4
StatePublished - Apr 2019



  • UAV advancements
  • crop scouting
  • data processing
  • field-based classification
  • sampling

PubMed: MeSH publication types

  • Journal Article

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