Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 942-949 |
| Number of pages | 8 |
| Journal | Pest management science |
| Volume | 75 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2019 |
Bibliographical note
Funding Information:We thank Dr. Brian Aukema for the helpful discussions about forward model selection, linear regression models, and other statistical analyses. We also thank Wally Rich, Zach Marston, Anh Tran, Dr. Anthony Hanson, Daniela Pezzini, James Menger-Anderson, Kathryn Pawley, Annika Asp, and Celia Silverstein for help collecting data, and Randy Gettle for provision of the spectroradiometer. Thanks to Dr. Theresa Cira for reviewing the supplemental on-line material containing the R code. This work was partially supported by the Minnesota Soybean Research and Promotion Council, Minnesota’s Discovery, Research, and InnoVation Economy, partnership (MnDRIVE) and National Council for Scientific and Technological Development (CNPq/Brazil).
Funding Information:
We thank Dr. Brian Aukema for the helpful discussions about forward model selection, linear regression models, and other statistical analyses. We also thank Wally Rich, Zach Marston, Anh Tran, Dr. Anthony Hanson, Daniela Pezzini, James Menger-Anderson, Kathryn Pawley, Annika Asp, and Celia Silverstein for help collecting data, and Randy Gettle for provision of the spectroradiometer. Thanks to Dr. Theresa Cira for reviewing the supplemental on-line material containing the R code. This work was partially supported by the Minnesota Soybean Research and Promotion Council, Minnesota's Discovery, Research, and InnoVation Economy, partnership (MnDRIVE) and National Council for Scientific and Technological Development (CNPq/Brazil).
Publisher Copyright:
© 2018 Society of Chemical Industry
Keywords
- UAV advancements
- crop scouting
- data processing
- field-based classification
- sampling
