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338 The Alabama Genomic Health Initiative: Integrating Genomic Medicine into Primary Care
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- Nita A Limdi, Devin Absher, Irf Asif, Lori Bateman, Greg Barsh, Kevin M. Bowling, Gregory M. Cooper, Brittney H. Davis, Kelly M. East, Candice R. Finnila, Blake Goff, Susan Hiatt, Melissa Kelly, Whitley V. Kelley, Bruce R. Korf, Donald R. Latner, James Lawlor, Thomas May, Matt Might, Irene P. Moss, Mariko Nakano-Okuno, Tiffany Osborne, Stephen Sodeke, Adriana Stout, Michelle L. Thompson
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- Journal:
- Journal of Clinical and Translational Science / Volume 7 / Issue s1 / April 2023
- Published online by Cambridge University Press:
- 24 April 2023, pp. 100-101
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OBJECTIVES/GOALS: Supported by the State of Alabama, the Alabama Genomic Health Initiative (AGHI) is aimed at preventing and treating common conditions with a genetic basis. This joint UAB Medicine-HudsonAlpha Institute for Biotechnology effort provides genomic testing, interpretation, and counseling free of charge to residents in each of Alabama’s 67 counties. METHODS/STUDY POPULATION: Launched in 2017, as a state-wide population cohort, AGHI (1.0) enrolled 6,331 Alabamians and returned individual risk of disease(s) related to the ACMG SF v2.0 medically actionable genes. In 2021, the cohort was expanded to include a primary care cohort. AGHI (2.0) has enrolled 750 primary care patients, returning individual risk of disease(s) related to the ACMG SF v3.1 gene list and pre-emptive pharmacogenetics (PGx) to guide medication therapy. Genotyping is done on the Illumina Global Diversity Array with Sanger sequencing to confirm likely pathogenic / pathogenic variants in medically actionable genes and CYP2D6 copy number variants using Taqman assays, resulting in a CLIA-grade report. Disease risk results are returned by genetic counselors and Pharmacogenetics results are returned by Pharmacists. RESULTS/ANTICIPATED RESULTS: We have engaged a statewide community (>7000 participants), returning 94 disease risk genetic reports and 500 PGx reports. Disease risk reports include increased predisposition to cancers (n=38), cardiac diseases (n=33), metabolic (n=12), other (n=11). 100% of participants harbor an actionable PGx variant, 70% are on medication with PGx guidance, 48% harbor PGx variants and are taking medications affected. In 10% of participants, pharmacists sent an active alert to the provider to consider/ recommend alternative medication. Most commonly impacted medications included antidepressants, NSAIDS, proton-pump inhibitors and tramadol. To enable the EMR integration of genomic information, we have developed an automated transfer of reports into the EMR with Genetics Reports and PGx reports viewable in Cerner. DISCUSSION/SIGNIFICANCE: We share our experience on pre-emptive implementation of genetic risk and pharmacogenetic actionability at a population and clinic level. Both patients and providers are actively engaged, providing feedback to refine the return of results. Real time alerts with guidance at the time of prescription are needed to ensure future actionability and value.
Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis – ERRATUM
- Yin Wu, Brooke Levis, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P.A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Yeates Conwell, Janneke M. de Manvan Ginkel, Jesse R. Fann, Felix H. Fischer, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, Patricia A. Harrison, Martin Härter, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Yunxin Kwan, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Anthony McGuire, Sherina Mohd-Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Katrin Reuter, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Henk C. van Weert, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Andrea Benedetti, Brett D. Thombs
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- Journal:
- Psychological Medicine / Volume 50 / Issue 16 / December 2020
- Published online by Cambridge University Press:
- 19 August 2019, p. 2816
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Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis
- Yin Wu, Brooke Levis, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P.A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Yeates Conwell, Janneke M. de Man-van Ginkel, Jesse R. Fann, Felix H. Fischer, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, Patricia A. Harrison, Martin Härter, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Yunxin Kwan, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Anthony McGuire, Sherina Mohd-Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Katrin Reuter, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Henk C. van Weert, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Andrea Benedetti, Brett D. Thombs
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- Journal:
- Psychological Medicine / Volume 50 / Issue 8 / June 2020
- Published online by Cambridge University Press:
- 12 July 2019, pp. 1368-1380
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Background
Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.
MethodsWe conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.
Results16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).
ConclusionsPHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
Probability of major depression diagnostic classification using semi-structured versus fully structured diagnostic interviews
- Brooke Levis, Andrea Benedetti, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Matthew J. Chiovitti, Tatiana A. Sanchez, Pim Cuijpers, Simon Gilbody, John P. A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Russell J. Steele, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Anna Beraldi, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Neerja Chowdhary, Kerrie Clover, Yeates Conwell, Janneke M. de Man-van Ginkel, Jaime Delgadillo, Jesse R. Fann, Felix H. Fischer, Benjamin Fischler, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, John Hambridge, Patricia A. Harrison, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Khalida Ismail, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Laura Marsh, Anthony McGuire, Sherina Mohd Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Christina M. van der Feltz-Cornelis, Henk C. van Weert, Paul A. Vöhringer, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Yuying Zhang, Brett D. Thombs
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- Journal:
- The British Journal of Psychiatry / Volume 212 / Issue 6 / June 2018
- Published online by Cambridge University Press:
- 02 May 2018, pp. 377-385
- Print publication:
- June 2018
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Background
Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.
AimsTo evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.
MethodData collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
ResultsA total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).
ConclusionsThe MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
Declaration of interestDrs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Utility of Hyperspectral Reflectance for Differentiating Soybean (Glycine max) and Six Weed Species
- Cody J. Gray, David R. Shaw, Lori M. Bruce
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- Weed Technology / Volume 23 / Issue 1 / March 2009
- Published online by Cambridge University Press:
- 20 January 2017, pp. 108-119
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Reflectance data were subjected to a variety of analysis methods to determine the utility of hyperspectral reflectance for differentiating soybean, soil, and six weed species commonly found in Mississippi agricultural fields. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Hyperspectral reflectance data were collected from mature plant leaves three times in 2002 and two times in 2003. Vegetation indices were calculated and subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA, using vegetation indices, produced the poorest classification accuracies for the plant species studied, generally less than 50%, whereas LDA resulted in classification accuracies greater than those from PCA. Best spectral band combination (BSBC) provided the greatest classification accuracies, with all better than 80% for all data sets. The BSBC indicated three wavelength bands of interest for species discrimination in the short wavelength infrared portion of the electromagnetic spectrum, which are not commonly used in current vegetation indices for species differentiation. These areas of interest were located from 1,445 to 1,475 nm, 2,030 to 2,090 nm, and 2,115 to 2,135 nm. The top 10 wavelengths determined by BSBC were then added to the vegetation indices and reanalyzed using PCA and LDA. Classification accuracies increased for all species when these wavelengths were added rather than using vegetation indices alone, suggesting greater crop and weed species differentiation can be obtained when using sensors that include these wavelength regions of the short wavelength infrared portion of the electromagnetic spectrum.
Detection of pitted morningglory (Ipomoea lacunosa) by hyperspectral remote sensing. I. Effects of tillage and cover crop residue
- Clifford H. Koger, David R. Shaw, Krishna N. Reddy, Lori M. Bruce
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- Weed Science / Volume 52 / Issue 2 / April 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 222-229
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Field experiments were conducted to evaluate the potential of hyperspectral reflectance data collected with a hand-held spectroradiometer to discriminate soybean intermixed with pitted morningglory and weed-free soybean in conventional till and no-till plots containing rye, hairy vetch, or no cover crop residue. Pitted morningglory was in the cotyledon to six-leaf growth stage. Seven 50-nm spectral bands (one ultraviolet, two visible, four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables. Pitted morningglory plant size had more influence on discriminant capabilities than tillage or cover crop residue systems. Across all tillage and residue systems, discrimination accuracy was 71 to 95%, depending on the size of pitted morningglory plants at the time of data acquisition. The versatility of the seven 50-nm bands was tested by using a discriminant model developed for one experiment location to test discriminant capabilities for the other experiment, with discrimination accuracy across all tillage and residue systems of 55 to 73%, depending on pitted morningglory plant size.
Detection of pitted morningglory (Ipomoea lacunosa) with hyperspectral remote sensing. II. Effects of vegetation ground cover and reflectance properties
- Clifford H. Koger, David R. Shaw, Krishna N. Reddy, Lori M. Bruce
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- Weed Science / Volume 52 / Issue 2 / April 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 230-235
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Field research was conducted to determine the potential of hyperspectral remote sensing for discriminating plots of soybean intermixed with pitted morningglory and weed-free soybean with similar and different proportions of vegetation ground cover. Hyperspectral data were collected using a handheld spectroradiometer when pitted morningglory was in the cotyledon to two-leaf, two- to four-leaf, and four- to six-leaf growth stages. Synthesized reflectance measurements containing equal and unequal proportions of reflectance from vegetation were obtained, and seven 50-nm spectral bands (one ultraviolet, two visible, and four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables to differentiate weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 93 to 100% regardless of pitted morningglory growth stage and whether equal or unequal proportions of reflectance from vegetation existed in weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 88 to 98% when using the discriminant model developed for one experiment to discriminate soybean intermixed with pitted morningglory and weed-free soybean plots of the other experiment. Reflectance in the near-infrared spectrum was higher for weed-free soybean compared with soybean intermixed with pitted morningglory, and this difference affected the ability to discriminate weed-free soybean from soybean intermixed with pitted morningglory.
Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture
- W. Brien Henry, David R. Shaw, Kambham R. Reddy, Lori M. Bruce, Hrishikesh D. Tamhankar
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- Weed Science / Volume 52 / Issue 5 / October 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 788-796
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Experiments were conducted to examine the use of spectral reflectance curves for discriminating between plant species across moisture levels. Weed species and soybean were grown at three moisture levels, and spectral reflectance data and leaf water potential were collected every other day after the imposition of moisture stress at 8 wk after planting. Moisture stress did not reduce the ability to discriminate between species. As moisture stress increased, it became easier to distinguish between species, regardless of analysis technique. Signature amplitudes of the top five bands, discrete wavelet transforms, and multiple indices were promising analysis techniques. Discriminant models created from data set of 1 yr and validated on additional data sets provided, on average, approximately 80% accurate classification among weeds and crop. This suggests that these models are relatively robust and could potentially be used across environmental conditions in field scenarios.
Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean
- Chris T. Leon, David R. Shaw, Lori M. Bruce, Clarence Watson
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- Weed Science / Volume 51 / Issue 4 / August 2003
- Published online by Cambridge University Press:
- 20 January 2017, pp. 557-564
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Because of interest in monitoring crop response to weed interference, greenhouse experiments were conducted to evaluate interference of purple and yellow nutsedge on the growth, development, and spectral response of cotton and soybean. Cotton fresh weight was reduced 9 to 42% compared with the control when grown with yellow and purple nutsedge. Fresh weight of soybean was reduced 27 to 60% when it emerged simultaneously with yellow nutsedge and 45 to 63% when it emerged 7 d after yellow nutsedge. Soybean fresh weight was reduced 30 to 35% when it emerged simultaneously with purple nutsedge and 44 to 72% when it emerged 7 d after purple nutsedge. Reflectance data were analyzed using wavelet transformation techniques with the HAAR mother wavelet. Nine extracted features from the cotton and soybean leaf reflectance measurements were used to classify single-leaf cotton and soybean reflectance measurements to predict whether cotton or soybean was growing in the presence or absence of purple and yellow nutsedge. After training the system, the ability to separate leaf reflectance measurements of crops growing weed free from those growing in the presence of purple and yellow nutsedge was tested using cross-validation with the nearest mean classifier. Cross-validation accuracy results for cotton were 62 to 70%. Cross-validation accuracy for soybean and yellow nutsedge was similar, regardless of emergence, and ranged from 60 to 71%. Features extracted from the soybean reflectance measurements were not as effective in classifying soybean leaf reflectance measurements based on the presence or absence of purple nutsedge. A decrease in accuracy was observed for both simultaneous and delayed soybean emergence in purple nutsedge fresh weight categories from more than 2,560 g to more than 3,420 g. Overall, the system correctly classified soybean emerging simultaneously with purple nutsedge 58 to 74% and soybean emerging 7 d after purple nutsedge 53 to 67%. These results indicate the potential of differentiating crops under stress using spectral reflectance, although refinements to the system must be made before it is field ready.
Remote Sensing to Detect Herbicide Drift on Crops
- W. Brien Henry, David R. Shaw, Kambham R. Reddy, Lori M. Bruce, Hrishikesh D. Tamhankar
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- Journal:
- Weed Technology / Volume 18 / Issue 2 / June 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 358-368
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Glyphosate and paraquat herbicide drift injury to crops may substantially reduce growth or yield. Determining the type and degree of injury is of importance to a producer. This research was conducted to determine whether remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in 3.8-L pots to the five- to seven-leaf stage, at which time, applications of nonselective herbicides were made. Visual injury estimates were made, and hyperspectral reflectance data were recorded 1, 4, and 7 d after application (DAA). Several analysis techniques including multiple indices, signature amplitude (SA) with spectral bands as features, and wavelet analysis were used to distinguish between herbicide-treated and nontreated plants. Classification accuracy using SA analysis of paraquat injury on soybean was better than 75% for both 1/2- and 1/8× rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2× rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. Applications of a 1/2× rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using SA analysis, the moderately injured plants were indistinguishable from the uninjured controls, as represented by the low classification accuracies at the 1/8-, 1/32-, and 1/64× rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the 1/8- or the 1/2× rates of paraquat compared with the nontreated corn plants better than 92% 1, 4, and 7 DAA. These analysis techniques, once tested and validated on field scale data, may help determine the extent and the degree of herbicide drift for making appropriate and, more importantly, timely management decisions.
Remote Sensing to Distinguish Soybean from Weeds After Herbicide Application
- W. Brien Henry, David R. Shaw, Kambham R. Reddy, Lori M. Bruce, Hrishikesh D. Tamhankar
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- Weed Technology / Volume 18 / Issue 3 / September 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 594-604
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Two experiments, one focusing on preemergence (PRE) herbicides and the other on postemergence (POST) herbicides, were conducted and repeated in time to examine the utility of hyperspectral remote sensing data for discriminating common cocklebur, hemp sesbania, pitted morningglory, sicklepod, and soybean after PRE and POST herbicide application. Discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. These data suggest that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the data set pooled across time and experiment types, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate, or species. Signature amplitudes, an additional classification technique, produced variable results with respect to discriminating soybean from weeds after herbicide application and discriminating between controls and plants to which herbicides were applied; thus, this was not an adequate classification technique.
Utility of Multispectral Imagery for Soybean and Weed Species Differentiation
- Cody J. Gray, David R. Shaw, Patrick D. Gerard, Lori M. Bruce
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- Journal:
- Weed Technology / Volume 22 / Issue 4 / December 2008
- Published online by Cambridge University Press:
- 20 January 2017, pp. 713-718
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An experiment was conducted to determine the utility of multispectral imagery for identifying soybean, bare soil, and six weed species commonly found in Mississippi. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Multispectral imagery was analyzed using supervised classification techniques based upon 2-class, 3-class, and 8-class systems. The 2-class system was designed to differentiate bare soil and vegetation. The 3-class system was used to differentiate bare soil, soybean, and weed species. Finally, the 8-class system was designed to differentiate bare soil, soybean, and all weed species independently. Soybean classification accuracies classified as vegetation for the 2-class system were greater than 95%, and bare soil classification accuracies were greater than 90%. In the 3-class system, soybean classification accuracies were 70% or greater. Classification of soybean decreased slightly in the 3-class system when compared to the 2-class system because of the 3-class system separating soybean plots from the weed plots, which was not done in the 2-class system. Weed classification accuracies increased as weed density or weeks after emergence (WAE) increased. The greatest weed classification accuracies were obtained once weed species were allowed to grow for 10 wk. Palmleaf morningglory and pitted morningglory classification accuracies were greater than 90% for 10 WAE using the 3-class system. Palmleaf morningglory and pitted morningglory at the highest densities of 6 plants/m2 produced the highest classification accuracies for the 8-class system once allowed to grow for 10 wk. All other weed species generally produced classification accuracies less than 50%, regardless of planting density. Thus, multispectral imagery has the potential for weed detection, especially when being used in a management system when individual weed species differentiation is not essential, as in the 2-class or 3-class system. However, weed detection was not obtained until 8 to 10 WAE, which is unacceptable in production agriculture. Therefore, more refined imagery acquisition with higher spatial and/or spectral resolution and more sophisticated analyses need to be further explored for this technology to be used early-season when it would be most valuable.