Browsing by Author "Bagavathiannan, Muthukumar V."

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    Current outlook and future research needs for harvest weed seed control in North American cropping systems
    Shergill, Lovreet S.; Schwartz-Lazaro, Lauren M.; Leon, Ramon; Ackroyd, Victoria J.; Flessner, Michael L.; Bagavathiannan, Muthukumar V.; Everman, Wesley J.; Norsworthy, Jason K.; VanGessel, Mark J.; Mirsky, Steven B. (2020-12)
    Harvest weed seed control (HWSC) comprises a set of tools and tactics that prevents the addition of weed seed to the soil seed bank, attenuating weed infestations and providing a method to combat the development and spread of herbicide-resistant weed populations. Initial HWSC research efforts in North America are summarized and, combined with the vast area of crops suitable for HWSC, clearly indicate strong potential for this technology. However, potential limitations exist that are not present in Australian cropping systems where HWSC was developed. These include rotations with crops that are not currently amenable to HWSC (e.g. corn), high moisture content at harvest, untimely harvest, and others. Concerns about weeds becoming resistant to HWSC (i.e. adapting) exist, as do shifts in weed species composition, particularly with the diversity of weeds in North America. Currently the potential of HWSC vastly outweighs any drawbacks, necessitating further research. Such expanded efforts should foremost include chaff lining and impact mill commercial scale evaluation, as this will address potential limitations as well as economics. Growers must be integrated into large-scale, on-farm research and development activities aimed at alleviating the problems of using HWSC systems in North America and drive greater adoption subsequently. (c) 2020 Society of Chemical Industry
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    Detection of Italian Ryegrass in Wheat and Prediction of Competitive Interactions Using Remote-Sensing and Machine-Learning Techniques
    Sapkota, Bishwa; Singh, Vijay; Neely, Clark; Rajan, Nithya; Bagavathiannan, Muthukumar V. (MDPI, 2020-09-13)
    Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions.
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    Evaluation Of Current Policies on the use of Unmanned Aerial Vehicles in Indian Agriculture
    Singh, Vijay; Bagavathiannan, Muthukumar V.; Chauhan, Bhagirath Singh; Singh, Samar (Current Science Association, 2019-07-10)
    Unmanned aerial vehicles (UAVs), commonly called ‘drones’, have enormous potential for technological advances in many sectors including agriculture. The recent revision in UAV policy by the Directorate General of Civil Aviation (DGCA), India, can impact the pace of research and development in machine vision capabilities in the country. Several other countries that have framed UAV policy previously, are continuously bringing changes to the existing framework to make it more user friendly. India can learn from those changes and bring out a comprehensive update to foster a broader application of these tools in agriculture. This policy review provides suggestions and solutions for increasing licensing centres, limiting UAV speed and weight for safer flights and including aerial pesticide applications in UAV permits to revolutionize the multibillion-dollar agriculture industry. This article has also examines the current UAV regulations in four other countries.
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    Herbicide-Resistance in Turf Systems: Insights and Options for Managing Complexity
    Allen, Jennifer H.; Ervin, David E.; Frisvold, George B.; Brosnan, James T.; McCurdy, James D.; Bowling, Rebecca G.; Patton, Aaron J.; Elmore, Matthew T.; Gannon, Travis W.; McCarty, Lambert B.; McCullough, Patrick E.; Kaminski, John E.; Askew, Shawn D.; Kowalewski, Alec R.; Unruh, J. Bryan; McElroy, J. Scott; Bagavathiannan, Muthukumar V. (MDPI, 2022-10-18)
    Due to complex interactions between social and ecological systems, herbicide resistance has classic features of a “wicked problem”. Herbicide-resistant (HR) Poa annua poses a risk to sustainably managing U.S. turfgrass systems, but there is scant knowledge to guide its management. Six focus groups were conducted throughout the United States to gain understanding of socio-economic barriers to adopting herbicide-resistance management practices. Professionals from major turfgrass sectors (golf courses, sports fields, lawn care, and seed/sod production) were recruited as focus-group participants. Discussions emphasized challenges of the weed management of turfgrass systems as compared to agronomic crops. This included greater time constraints for managing weeds and more limited chemical control options. Lack of understanding about the proper use of compounds with different modes of action was identified as a threat to sustainable weed management. There were significant regional differences in perceptions of the existence, geographic scope, and social and ecological causes of HR in managing Poa annua. Effective resistance management will require tailoring chemical and non-chemical practices to the specific conditions of different turfgrass sectors and regions. Some participants thought it would be helpful to have multi-year resistance management programs that are both sector- and species-specific.
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    Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery
    Sapkota, Bishwa; Singh, Vijay; Cope, Dale; Valasek, John; Bagavathiannan, Muthukumar V. (MDPI, 2020-06-16)
    In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.
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    Morphophysiological diversity and its association with herbicide resistance in Echinochloa ecotypes
    Liu, Rui; Singh, Vijay; Abugho, Seth; Lin, Hao-Sheng; Zhou, Xin-Gen; Bagavathiannan, Muthukumar V. (Cambridge University Press, 2021-10-01)
    The genus Echinochloa constitutes some of the most prominent weed species found in rice (Oryza sativa L.) production worldwide. The taxonomy of Echinochloa is complex due to its morphological variations. The morphophysiological diversity and taxonomic characteristics of Echinochloa ecotypes infesting rice fields in Texas are unknown. A total of 54 Echinochloa ecotypes collected during late-season field surveys in 2015 and 2016 were characterized in a common garden in 2017. Plants were characterized for 14 morphophysiological traits, including stem angle; stem color; plant height; leaf color; leaf texture; flag leaf length, width, and angle; days to flowering; panicle length; plant biomass; seed shattering; seed yield; and seed dormancy. Principal component analysis indicated that 4 (plant height, flag leaf length, seed shattering, and seed germination) of the 14 phenological traits characterized here had significantly contributed to the overall morphological diversity of Echinochloa spp. Results showed wide interpopulation diversity for the measured traits among the E. colona ecotypes, as well as diverse intrapopulation variability in all three Echinochloa species studied, including barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.], junglerice [Echinochloa colona (L.) Link], and rough barnyardgrass [Echinochloa muricata (P. Beauv.) Fernald]. Taxonomical classification revealed that the collection consisted of three Echinochloa species, with E. colona being the most dominant (96%), followed by E. crus-galli (2%), and E. muricata (2%). Correlation analysis of morphophysiological traits and resistance status to commonly used preemergence (clomazone, quinclorac) and postemergence herbicides (propanil, quinclorac, imazethapyr, and fenoxaprop-ethyl) failed to show any significant association. Findings from this study provided novel insights into the morphophysiological characteristics of Echinochloa ecotypes in rice production in Texas. The morphological diversity currently present in Echinochloa ecotypes could contribute to their adaptation to selection pressure imposed by different management tools, emphasizing the need for a diversified management approach to effectively control this weed species.
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    Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
    Schwartz-Lazaro, Lauren M.; Shergill, Lovreet S.; Evans, Jeffrey A.; Bagavathiannan, Muthukumar V.; Beam, Shawn C.; Bish, Mandy D.; Bond, Jason A.; Bradley, Kevin W.; Curran, William S.; Davis, Adam S.; Everman, Wesley J.; Flessner, Michael L.; Haring, Steven C.; Jordan, Nicholas R.; Korres, Nicholas E.; Lindquist, John L.; Norsworthy, Jason K.; Sanders, Tameka L.; Steckel, Larry E.; VanGessel, Mark J.; Young, Blake; Mirsky, Steven B. (2021-01)
    Potential effectiveness of harvest weed seed control (HWSC) systems depends upon seed shatter of the target weed species at crop maturity, enabling its collection and processing at crop harvest. However, seed retention likely is influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed-shatter phenology in 13 economically important broadleaf weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after physiological maturity at multiple sites spread across 14 states in the southern, northern, and mid-Atlantic United States. Greater proportions of seeds were retained by weeds in southern latitudes and shatter rate increased at northern latitudes. Amaranthus spp. seed shatter was low (0% to 2%), whereas shatter varied widely in common ragweed (Ambrosia artemisiifolia L.) (2% to 90%) over the weeks following soybean physiological maturity. Overall, the broadleaf species studied shattered less than 10% of their seeds by soybean harvest. Our results suggest that some of the broadleaf species with greater seed retention rates in the weeks following soybean physiological maturity may be good candidates for HWSC.
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    Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
    Schwartz-Lazaro, Lauren M.; Shergill, Lovreet S.; Evans, Jeffrey A.; Bagavathiannan, Muthukumar V.; Beam, Shawn C.; Bish, Mandy D.; Bond, Jason A.; Bradley, Kevin W.; Curran, William S.; Davis, Adam S.; Everman, Wesley J.; Flessner, Michael L.; Haring, Steven C.; Jordan, Nicholas R.; Korres, Nicholas E.; Lindquist, John L.; Norsworthy, Jason K.; Sanders, Tameka L.; Steckel, Larry E.; VanGessel, Mark J.; Young, Blake; Mirsky, Steven B. (2021-01)
    Seed shatter is an important weediness trait on which the efficacy of harvest weed seed control (HWSC) depends. The level of seed shatter in a species is likely influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed shatter of eight economically important grass weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after maturity at multiple sites spread across 11 states in the southern, northern, and mid-Atlantic United States. From soybean maturity to 4 wk after maturity, cumulative percent seed shatter was lowest in the southern U.S. regions and increased moving north through the states. At soybean maturity, the percent of seed shatter ranged from 1% to 70%. That range had shifted to 5% to 100% (mean: 42%) by 25 d after soybean maturity. There were considerable differences in seed-shatter onset and rate of progression between sites and years in some species that could impact their susceptibility to HWSC. Our results suggest that many summer annual grass species are likely not ideal candidates for HWSC, although HWSC could substantially reduce their seed output during certain years.
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    Spray Deposition on Weeds (Palmer Amaranth and Morningglory) from a Remotely Piloted Aerial Application System and Backpack Sprayer
    Martin, Daniel; Singh, Vijay; Latheef, Mohamed A.; Bagavathiannan, Muthukumar V. (MDPI, 2020-09-19)
    This study was designed to determine whether a remotely piloted aerial application system (RPAAS) could be used in lieu of a backpack sprayer for post-emergence herbicide application. Consequent to this objective, a spray mixture of tap water and fluorescent dye was applied on Palmer amaranth and ivyleaf morningglory using an RPAAS at 18.7 and 37.4 L·ha−1 and a CO2-pressurized backpack sprayer at a 140 L·ha−1 spray application rate. Spray efficiency (the proportion of applied spray collected on an artificial sampler) for the RPAAS treatments was comparable to that for the backpack sprayer. Fluorescent spray droplet density was significantly higher on the adaxial surface for the backpack sprayer treatment than that for the RPAAS platforms. The percent of spray droplets on the abaxial surface for the RPAAS aircraft at 37.4 L·ha−1 was 4-fold greater than that for the backpack sprayer at 140 L·ha−1. The increased spray deposition on the abaxial leaf surfaces was likely caused by rotor downwash and wind turbulence generated by the RPAAS which caused leaf fluttering. This improved spray deposition may help increase the efficacy of contact herbicides. Test results indicated that RPAASs may be used for herbicide application in lieu of conventional backpack sprayers.
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    Technological Advances for Weed Management
    Burgos, Nilda; Rouse, Christopher; Singh, Vijay; Salas-Perez, Reiofeli; Bagavathiannan, Muthukumar V. (Asian-Pacific Weed Science Society (APWSS), Indian Society of Weed Science, The Weed Science Society of Japan,, 2017-12-06)