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                    U.S. DEPARTMENT OF AGRICULTURE
 Stored Product Insect and Engineering Research: Manhattan, KS
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                        Center for Grain and Animal Health Research: Manhattan, KS
                                    Arthropod-Borne Animal Diseases Research Unit
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ARS Home Plains Area Manhattan, Kansas Center for Grain and Animal Health Research Stored Product Insect and Engineering Research People





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- Dan Brabec
- James Campbell (CD)
- Alison Gerken
- Rob Morrison
- Brenda Oppert
- Lester Pordesimo
- Deanna Scheff
- Erin Scully (RL)
- Paul Armstrong
- Frank Arthur
- Mark Casada

Paul R Armstrong    Research Agricultural Engineer  **Dr. Paul ArmstrongCollaborator**
USDA-ARS-CGAHR-SPIERUATTN: Paul Armstrong1515 College AvenueManhattan, KS 66502www.ars.usda.gov/pa/cgahr/spieru/armstrong

Dr. Paul Armstrong**RESEARCH INTERESTS**Paul Armstrong is a scientist working on the development of sensors and instrumentation for non-destructive measurement of grain attributes and the monitoring of stored grain. Research includes development of rapid single-kernel near-infrared spectroscopic instrumentation to measure chemical composition and attributes of corn and soybeans (protein, lysine, tryptophan, oil, mold damage, etc), and development in situ moisture and quality monitoring systems for stored grain.**ALL PUBLICATIONS** via ARIS System     via Google Scholar**RECENT PUBLICATIONS**
• Developing a multi-spectral NIR LED-based instrument for detection of pesticide residues containing chlorpyrifos-methyl in rough, brown and milled rice - (Peer Reviewed Journal)Rodriguez, F.S., Armstrong, P.R., Maghirang, E.B., Yaptenco, K.F., Scully, E.D., Arthur, F.H., Brabec, D.L., Adviento-Borbe, A.A., Suministrado, D.C. 2020. Developing a multi-spectral NIR LED-based instrument for detection of pesticide residues containing chlorpyrifos-methyl in rough, brown and milled rice. Transactions of the ASABE. 36(6):983-993. https://doi.org/10.13031/aea.14001.• Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms - (Peer Reviewed Journal)Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms. Journal of Stored Products Research. 104. Article 102196. https://doi.org/10.1016/j.jspr.2023.102196.• [RA] NIR spectral imaging for animal feed quality and safety - (Peer Reviewed Journal)Dantes-Mendoza, P., Hurburgh, C.R., Maier, D.M., Armstrong, P.R. 2024. [RA] NIR spectral imaging for animal feed quality and safety. Applied Engineering in Agriculture. 39(6): 553-564. https://doi.org/10.13031/aea.15051.• Identifying common stored product insects using automated deep learning methods - (Peer Reviewed Journal)Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Identifying common stored product insects using automated deep learning methods. Journal of Stored Products Research. 103. Article 102166. https://doi.org/10.1016/j.jspr.2023.102166.• Assessment of oil quantification methods for high oil seeds - (Peer Reviewed Journal)Al-Bakri, A., Al-Amery, M., Su, K., Anderson, H., Geneve, R., Crocker, M., Teets, N., Armstrong, P.R., Kachroo, P., Hildebrand, D. 2023. Assessment of oil quantification methods for high oil seeds. Analytical Chemistry. 50. Article 102715. https://doi.org/10.1016/j.bcab.2023.102715.• Application of machine learning for insect monitoring in grain facilities - (Peer Reviewed Journal)Mendoza, Q.A., Pordesimo, L.O., Nielsen, M.L., Armstrong, P.R., Campbell, J.F. 2023. Application of machine learning for insect monitoring in grain facilities. Artificial Intelligence. 4:348-360. https://doi.org/10.3390/ai4010017.• Crop seed phenomics: Enabling nondestructive phenotyping approaches for characterization of functional and quality traits - (Peer Reviewed Journal)Hacisalihoglu, G., Armstrong, P.R. 2023. Crop seed phenomics: Enabling nondestructive phenotyping approaches for characterization of functional and quality traits. Plants. 12(5):1177. https://doi.org/10.3390/plants12051177.• Prediction of sorghum oil and kernel weight using near-infrared hyperspectral imaging - (Peer Reviewed Journal)Mendoza, P.D., Armstrong, P.R., Peiris, K.H., Siliveru, K., Bean, S.R., Pordesimo, L.O. 2023. Prediction of sorghum oil and kernel weight using near-infrared hyperspectral imaging. Cereal Chemistry. 100(3):775-783. https://doi.org/10.1002/cche.10656.• Compositional analysis in sorghum (Sorghum bicolor) NIR spectral techniques based on mean spectra from single seeds - (Peer Reviewed Journal)Gokhan, H., Armstrong, P.R., Mendoza, P.D., Seabourn, B.W. 2022. Compositional analysis in sorghum (Sorghum bicolor) NIR spectral techniques based on mean spectra from single seeds. Frontiers in Plant Science. 13:995328. https://doi.org/10.3389/fpls.2022.995328.• Predicting single kernel and bulk milled rice alkali spreading value and gelatinization temperature class using nir spectroscopy - (Peer Reviewed Journal)


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