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The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

Lawns provide valuable ecological, economic and social services. Both the quantity and quality of the turfgrass affect the level of the services delivered. Unwanted weed infestations can degrade lawn quality and decrease the value of these services for lawn users and others. For example, Brosnan et al. reported that athletic fields with weed cover not only decreased aesthetic quality but also resulted in increased surface hardness and a concomitant increase in potential athlete injuries and soil erosion. Contemporary research pertaining to pest influences on lawn quality and management responses is sparse. We hope to begin filling that gap with findings from a recent study of U.S. lawn care operators about the challenges and opportunities omanaging Poa annua, an emergent threat to sustainable lawn management (USDA-NIFA Specialty Crops Research Initiative (SCRI) Grants Program (award #: 2018-51181-28436). Estimates for residential- and commercial-lawn cover in the United States (U.S.) range from 58,000 km2 to 120,000 km2 with total turfgrass cover (inclusive of golf courses, parks, schools, roadsides) estimated at 163,800 km2. The Federal Highway Administration (FHWA) estimated that there are 20,436 km2 of grassed rights of way (roadsides) in the U.S. The total U.S. urban area in 2010 was estimated at 802,053 km2. Together, we can estimate turf cover between 7 and 18% of U.S urban areas, including lawns as the primary turf surface along with parks, athletic fields, golf courses, and roadsides. The professional lawn care industry employed 295,000 workers and represented an $18.5 billion market value in 2002. Despite its size, the U.S. lawn care industry defies simple characterization in part due to its heterogeneity. Lawns may provide valuable ecosystem services, including wildlife habitat, water infiltration, and moderation of the urban heat-island effect as well as aesthetics and open space for recreation. Apart from offering economic and ecological services, lawns and their management can be an important component of community social character and culture. Effective weed management in lawns assures sustainable delivery of all of these services, such that lawns are uniform and free from potentially troublesome weed species, such as Poa annua which ranks as the #1 most troublesome weed in turfgrass systems.

Seed retention, and ultimately seed shatter, are extremely important for the efficacy of harvest weed seed control (HWSC) and are likely influenced by various agroecological and environmental factors. Field studies investigated seed-shattering phenology of 22 weed species across three soybean [Glycine max (L.) Merr.]-producing regions in the United States. We further evaluated the potential drivers of seed shatter in terms of weather conditions, growing degree days, and plant biomass. Based on the results, weather conditions had no consistent impact on weed seed shatter. However, there was a positive correlation between individual weed plant biomass and delayed weed seed–shattering rates during harvest. This work demonstrates that HWSC can potentially reduce weed seedbank inputs of plants that have escaped early-season management practices and retained seed through harvest. However, smaller individuals of plants within the same population that shatter seed before harvest pose a risk of escaping early-season management and HWSC.

Wood vinegar, a product of pyrolysis, can induce phytotoxicity on plants when applied at an adequate rate and concentration. The objective of this research was to investigate wood vinegar obtained from the pyrolysis of apple tree branches for weed control in dormant zoysiagrass. In environment-controlled growth chambers, white clover visual injury and shoot mass reduction were evaluated and compared to the nontreated control after wood vinegar application at 1,000, 2,000, or 4,000 L ha−1 under 10 C or 30 C temperature conditions. Averaged across rates, wood vinegar rapidly desiccated white clover and caused 83% and 71% visual injury at 10 C and 30 C, respectively, at 1 d after treatment (DAT). Averaged across temperatures, wood vinegar at 1,000, 2,000, and 4,000 L ha−1 reduced white clover shoot mass by 56%, 81%, and 98% from the nontreated control at 10 DAT, respectively. In field experiments, weed control increased as wood vinegar rates increased from 1,000 to 5,000 L ha−1 in dormant zoysiagrass. The effective application dose of wood vinegar required to provide 90% control (ED90) of annual fleabane, Persian speedwell, and white clover was determined to be 2,450, 2,300, and 4,020 L ha−1, respectively, at 2 wk after treatment. Turf quality did not differ among the wood vinegar treatments and the nontreated control when zoysiagrass completely recovered from dormancy. Overall, results illustrate that wood vinegar resulting from the pyrolysis of apple tree branches can be used as a nonselective herbicide in dormant turfgrass, offering a new nonsynthetic herbicide option for weed control in managed turf.

The objective of this paper was to review the reproductive biology, herbicide-resistant (HR) biotypes, pollen-mediated gene flow (PMGF), and potential for transfer of alleles from HR to herbicide-susceptible grass weeds including barnyardgrass, creeping bentgrass, Italian ryegrass, johnsongrass, rigid (annual) ryegrass, and wild oats. The widespread occurrence of HR grass weeds is at least partly due to PMGF, particularly in obligate outcrossing species such as rigid ryegrass. Creeping bentgrass, a wind-pollinated turfgrass species, can efficiently disseminate herbicide resistance alleles via PMGF and movement of seeds and stolons. The genus Agrostis contains about 200 species, many of which are sexually compatible and produce naturally occurring hybrids and hybrids with species in the genus Polypogon. The self-incompatibility, extremely high outcrossing rate, and wind pollination in Italian ryegrass clearly point to PMGF as a major mechanism by which herbicide resistance alleles can spread across agricultural landscapes, resulting in abundant genetic variation within populations and low genetic differentiation among populations. Italian ryegrass can readily hybridize with perennial ryegrass and rigid ryegrass due to their similarity in chromosome numbers (2n = 14), resulting in interspecific gene exchange. Johnsongrass, barnyardgrass, and wild oats are self-pollinated species, so the potential for PMGF is relatively low and limited to short distances; however, seeds can easily shatter upon maturity before crop harvest, leading to wider dispersal. The occurrence of PMGF in reviewed grass weed species, even at a low rate, is greater than that of spontaneous mutations conferring herbicide resistance in weeds and thus can contribute to the spread of herbicide resistance alleles. This review indicates that the transfer of herbicide resistance alleles occurs under field conditions at varying levels depending on the grass weed species.

Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.

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. Published version

The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled late-season weed escapes. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using unmanned aerial vehicle–based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes before crop harvest: common waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately before ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 to 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assisting with management decision making.

Aims: Field studies were conducted to determine sesame response to the pre-emergence herbicides (acetochlor at 1.7 kg ai ha-1; S-metolachlor at 0.72, 1.43, and 2.86 kg ai ha-1; dimethenamid-P at 0.84 kg ai ha-1; pethoxamid at 0.22 kg ai ha-1; pyroxasulfone at 0.09 kg ai ha-1and bicyclopyrone at 0.12 and 0.24 kg ai ha-1) applied 3 or 6 days after 50% emergence. Study Design: Randomized complete block design with 3-4 reps depending on location. Place and Duration of Study: Sesame growing areas of Alabama, Mississippi, and Texas during the 2016 through 2018 growing seasons. Methodology: Treatments consisted of a factorial arrangement of herbicide treatments at two early POST application timings. A non-treated control was included for comparison. Crop oil concentrate (Agridex®, Helena, Collierville, TN 38017) at 1.0% v/v was added to all herbicide treatments. Plot size was either five rows (76 cm apart) by 9.1 m or four rows (101 cm apart) by 7.3 m depending on location. Only the two middle rows were sprayed and the other rows were untreated and served as buffers. Sesame cultivars were seeded approximately 1.0 to 2.0 cm deep at 9 kg/ha at all locations. Injury was evaluated early-season, 7 to 27 days after herbicide application (DAA), and later, 28 to 147 DAA, based on a scale of 0 (no sesame injury) to 100 (complete sesame death). Injury consisted of stuntingand leaf chlorosis and/or necrosis. Results: All herbicides tested resulted in significant injury to sesame at some location and application timing. None of the herbicides evaluated are safe to use early POST on sesame without causing significant injury. Conclusion: The ability of sesame to recover from significant injury and compensate for injury led to no yield loss in many instances. However, levels of injury observed are not acceptable by growers and will not allow the use of these herbicides soon after sesame emergence.

Ryegrass (Lolium spp.) is a troublesome weed in major wheat (Triticum aestivum L.) production regions in the United States. High diversity and adaptive potential are known to contribute to its success as a weed species and also create difficulties in correct species identification in fields. The objective of this research was to characterize diversity for 16 different morphological traits among 56 Lolium populations collected from wheat production fields across the Texas Blackland Prairies region and identify Lolium species based on taxonomic characteristics. Populations were highly diverse (both at inter- and intrapopulation levels) for the traits studied, and a taxonomic comparison with USDA-GRIN reference samples revealed that all the populations were variants of Italian ryegrass [Lolium perenne L. ssp. multiflorum (Lam.) Husnot] with a few offtypes of perennial ryegrass (Lolium perenne L.) or probable hybrids between the two species. Hierarchical clustering grouped the populations into six clusters based on their similarities for the morphological traits investigated. Principal component analysis showed that the variability for yield traits greatly contributed to the total diversity. Pre-flowering plant height (stage 10 on Feekes scale) was positively correlated with tiller count, shoot biomass, and spike count, but not with total seed count per plant, whereas plant height at maturity (stage 11.3 to 11.4 on Feekes scale) was highly correlated with total seeds per plant. Further, basal node color was positively correlated with plant growth habit, regrowth rate, and leaf color. Leaf blade width was positively correlated with survival to pinoxaden and multiple herbicides, whereas, spike count was negatively correlated with survival to mesosulfuron. The high levels of intra- as well as interpopulation variability documented in this study indicate the potential of this species to rapidly adapt to herbicides and emphasize the need for implementing diverse management tactics, including the integration of harvest weed seed control.

Background Annual bluegrass is a troublesome weed in managed turf systems. A survey was conducted to evaluate the prevalence of herbicide resistance in golf course populations of annual bluegrass in eastern Texas. Screenings were conducted for two photosystem II (PS II)-inhibitor herbicides [simazine preemergence (PRE), amicarbazone postemergence (POST)], two acetolactate synthase (ALS) inhibitors (foramsulfuron POST, trifloxysulfuron POST) and one microtubule assembly inhibitor (pronamide PRE/POST). Results Ninety percent of the populations were found to be resistant to at least one of the tested herbicides. The TX15-14 population was >490-, 178-, 10-, 26-, 4.3- and 3.8-fold, and the TX15-27 population was >490-, 16-, 28-, 84-, 5.2- and 4.1-fold less sensitive to simazine, amicarbazone, foramsulfuron, trifloxysulfuron, pronamide POST and pronamide PRE, respectively, compared to the susceptible standard TX15-SUS. Populations resistant to pronamide POST were completely controlled by pronamide PRE at the label recommended rate. The ALS and psbA gene sequence analysis indicated the presence of target site mutations (Ser-264-Gly in the psbA gene of TX15-14 and Trp-574-Leu in the ALS gene of TX15-27). However, given the absence of any target-site mutation in the ALS gene of TX15-14, the psbA gene of TX15-27 and α-tubulin of both populations, nontarget site mechanisms of resistance are suspected. Conclusion This is the first case of multiple herbicide resistance in annual bluegrass populations to three herbicide modes of action. Results show the widespread occurrence of multiple herbicide resistance in golf course annual bluegrass populations in eastern Texas and emphasize the need for the development and implementation of effective resistance management practices. © 2020 Society of Chemical Industry.

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. U.S. Department of Agriculture, Agricultural Research Service Areawide program; HATCH Program of the National Institute of Food and Agriculture; U.S. Department of AgricultureUnited States Department of Agriculture (USDA) Published version We would like to thank the staff and students at each university for helping conduct this research, specifically Kreshnik Bejleri, Sheri Heard, John Sanders, Barbara Scott, Annie Klodd, Zach Schaefer, Russ Garetson, Vitor Damiao, Matheus Martins, Camille Werner, Bruno Flaibam, and Camila Grassmann, and each institute's research and experimental stations. The authors would also like to thank the U.S. Department of Agriculture, Agricultural Research Service Areawide program for funding and the HATCH Program of the National Institute of Food and Agriculture and the U.S. Department of Agriculture for providing partial funding. No conflicts of interest have been declared.

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. U.S. Department of Agriculture, Agricultural Research Service Areawide program; HATCH Program of the National Institute of Food and Agriculture; U.S. Department of AgricultureUnited States Department of Agriculture (USDA) Published version We would like to thank the staff and students at each university for helping conduct this research, specifically Kreshnik Bejleri, Sheri Heard, John Sanders, Barbara Scott, Annie Klodd, Zach Schaefer, Russ Garetson, Vitor Damiao, Matheus Martins, Camille Werner, Bruno Flaibam, and Camila Grassmann, and each institute's research and experimental stations. The authors would also like to thank the U.S. Department of Agriculture, Agricultural Research Service Areawide program for funding and the HATCH Program of the National Institute of Food and Agriculture and the U.S. Department of Agriculture for providing partial funding for this work. No conflicts of interest have been declared.

A paper-based survey was conducted from 2015 to 2017 among stakeholders of the Texas rice industry on current weed management challenges and factors influencing management decisions. A total of 108 survey questionnaires were completed by stakeholders at the rice Cooperative Extension meetings conducted in the rice-growing counties of Texas. In addition, late-season field surveys were conducted prior to harvest in 2015 and 2016 across the rice-growing counties to understand dominant weed escapes occurring in rice fields. Results from the questionnaire survey revealed that rice–fallow–rice was the most common rotation practiced in Texas rice production. Echinochloa spp., Leptochloa spp., and Cyperus spp. were the top three problematic weed issues faced by the respondents. Among the Leptochloa species, Nealley’s sprangletop, a relatively new species in rice fields, was indicated as an emerging concern. Clomazone was the most frequently used PRE herbicide, whereas quinclorac, propanil, imazethapyr, and cyhalofop were the popular POST herbicides. Most respondents (72%) made weed-control decisions on the basis of economic thresholds, whereas 63% made decisions on the basis of weed problems from previous years. Most respondents (88%) expressed moderate to high concern for herbicide-resistant weeds in their operations. Strategies to manage herbicide-resistant weeds and economical weed management practices were among the top suggested research needs. The field survey revealed that jungle rice, Nealley’s sprangletop, and hemp sesbania were the top three late-season weed escapes in rice production in Texas, with frequencies of occurrence of 28%, 19%, and 13%, respectively. Furthermore, average field area infested by a species was the greatest for jungle rice (13%), followed by hemp sesbania (11%) and weedy rice (11%). Findings from the stakeholder and field surveys help direct future research and outreach efforts for sustainable weed management in Texas rice.

Tillage regimes can influence weed population dynamics and, consequently, the choice of appropriate weed management practices. Studies were conducted in 2016 and 2017 in a long-term (36-yr) grain sorghum [Sorghum bicolor (L.) Moench ssp. bicolor] experiment at Texas A&M University, College Station, to determine the impact of long-term no-till (NT) and conventional till (CT) systems on weed species dynamics. Higher densities of johnsongrass [Sorghum halepense (L.) Pers.], prostrate spurge [Chamaesyce humistrata (Engelm. ex A. Gray) Small], waterhemp [Amaranthus tuberculatus (Moq.) Sauer], and henbit (Lamium amplexicaule L.) were recorded in the NT system compared with the CT system. Further, the NT system showed greater weed diversity (Shannon-Wiener index, H = 0.8) and species richness (S = 6.2), compared with the CT system (H = 0.6, S = 4.2). Seedling emergence of some dominant weed species was also delayed in the NT system. In the CT system, 50% emergence of S. halepense (8.5 C base temperature) and waterhemp (10 C base temperature) occurred at 59 and 63 growing degree days (GDD), respectively, whereas 68 and 75 GDD, respectively, were required in the NT system. Further, a greater proportion (61%) of the viable seedbank was present at the top 5 cm of the soil in the NT system compared with the CT system (46%). Overall, findings from this 36-yr-long tillage experiment have revealed that the NT system had greater weed densities (especially of the perennial weed S. halepense) and a high proportion of weed seeds (particularly small-seeded annuals) on the topsoil layer, corroborating some earlier reports that were based on short-term investigations. Findings indicate that growers transitioning to NT systems should be mindful of potential shifts in weed species dominance and develop appropriate management tactics.

Herbicide-resistant weeds pose a severe threat to sustainable vegetation management in various production systems worldwide. The majority of the herbicide resistance cases reported thus far originate from agronomic production systems where herbicide use is intensive, especially in industrialized countries. Another notable sector with heavy reliance on herbicides for weed control is managed turfgrass systems, particularly golf courses and athletic fields. Intensive use of herbicides, coupled with a lack of tillage and other mechanical tools that are options in agronomic systems, increases the risk of herbicide-resistant weeds evolving in managed turfgrass systems. Among the notable weed species at high risk for evolving resistance under managed turf systems in the United States are annual bluegrass, goosegrass, and crabgrasses. The evolution and spread of multiple herbicide resistance, an emerging threat facing the turfgrass industry, should be addressed with the use of diversified management tools. Target-site resistance has been reported commonly as a mechanism of resistance for many herbicide groups, though non–target site resistance is an emerging concern. Despite the anecdotal evidence of the mounting weed resistance issues in managed turf systems, the lack of systematic and periodic surveys at regional and national scales means that confirmed reports are very limited and sparse. Furthermore, currently available information is widely scattered in the literature. This review provides a concise summary of the current status of herbicide-resistant weeds in managed turfgrass systems in the United States and highlights key emerging threats.Nomenclature: Annual bluegrass, Poa annua L.; crabgrass, Digitaria spp.; goosegrass, Eleusine indica (L.) Gaertn

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.

Abiotic stresses such as drought, extreme temperature, and salinity can negatively impact seed germination and plant growth and have become major limitations to crop production. Most crops are vulnerable to abiotic stress factors during their early growth phase, especially during seed germination and seedling emergence. Rapid crop seed germination and seedling establishment is known to provide competitive advantages over weeds and improve yields. Seed osmopriming is defined as a pre-sowing treatment in which seeds are soaked in osmotic solutions to undergo the first stage of germination, but radicle protrusion has not occurred. The process of osmopriming involves prior exposure of seeds in low-water-potential solutions. Osmopriming can generate a series of pre-germination metabolic activities, increase the antioxidant system activities, and prepare the seed for radicle protrusion. Polyethylene glycol (PEG) is a popular osmopriming agent that can alleviate the negative impacts of abiotic stresses. This review summarizes research findings on crop responses to seed priming with PEG under abiotic stresses. The challenges, limitations, and opportunities of using PEG for crop seed priming are discussed with the goal of providing insights into future research towards effective application of seed priming in crop production.

Field surveys were conducted across the Blacklands region of Texas during 2016 and 2017 to document the distribution of herbicide-resistant Lolium spp. infesting winter wheat production fields in the region. A total of 68 populations (64 Italian ryegrass, four perennial ryegrass) were evaluated in a greenhouse for sensitivity to herbicides of three different modes of action: an acetolactate synthase (ALS) inhibitor (mesosulfuron-methyl), two acetyl-coenzyme-A carboxylase (ACCase) inhibitors (diclofop-methyl and pinoxaden), and a 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor (glyphosate). Herbicides were applied at twice the label-recommended rates for mesosulfuron-methyl (29 g ai ha−1), diclofop-methyl (750 g ai ha−1), and pinoxaden (118 g ai ha−1); and at the recommended rate for glyphosate (868 g ae ha−1). The herbicide screenings were followed by dose-response assays of the most-resistant ryegrass population for each herbicide at eight rates (0.5, 1, 2, 4, 8, 16, 32, and 64×), compared with a susceptible population at six rates (0.0625, 0.125, 0.25, 0.5, 1, and 2×). The initial screening and dose-response experiments were conducted in a completely randomized design with three replications and two experimental runs. Survivors (

Experiments were conducted to evaluate the impact of 36 years of no-tillage (NT), compared to conventional-tillage (CT), on soil physical properties in a continuous grain sorghum experiment. Result...

Tetraploid johnsongrass [Sorghum halepense (L.) Pers.] is a sexually-compatible weedy relative of diploid sorghum [Sorghum bicolor (L.) Moench]. To determine the extent of interspecific hybridization between male sterile grain sorghum and johnsongrass and the ploidy of their progeny, cytoplasmic (CMS), genetic (GMS) and chemically induced male sterile lines of Tx623 and Tx631 were pollinated with johnsongrass pollen. At maturity 1% and 0.07% of the developing seeds of Tx623 and Tx631 respectively were recovered. Ninety-one percent of recovered hybrids were tetraploid and two percent were triploid, the tetraploids resulting from 2n gametes present in the sorghum female parent. Their formation appears to be genotype dependent as more tetraploids were recovered from Tx623 than Tx631. Because a tetraploid sorghum x johnsongrass hybrid has a balanced genome, they are male and female fertile providing opportunities for gene flow between the two species. Given the differences in 2n gamete formation among Tx623 and Tx631, seed parent selection may be one way of reducing the likelihood of gene flow. These studies were conducted in controlled and optimum conditions; the actual outcrossing rate in natural conditions is expected to be much lower. More studies are needed to assess the rates of hybridization, fitness, and fertility of the progeny under field conditions.

We conducted a survey in the major row-crop production regions of Texas to determine the response of waterhemp to glyphosate (5-enolpyruvylshikimate-3-phosphate synthase [EPSPS] inhibitor), atrazine (photosystem II [PSII] inhibitor), pyrithiobac (acetolactate synthase [ALS] inhibitor), tembotrione (hydroxyphenylpyruvate dioxygenase [HPPD] inhibitor), fomesafen (protoporphyrinogen oxidase [PPO] inhibitor), and dicamba (synthetic auxin). We evaluated 127 accessions for these herbicides. Resistance was confirmed on the basis of plant survival within an accession, and the injury ratings of surviving plants were used to categorize each accession as resistant (

A state-level survey was conducted across major row-crop production regions of Texas to document the level of sensitivity of Palmer amaranth to glyphosate, atrazine, pyrithiobac, tembotrione, fomesafen, and dicamba. Between 137 and 161 Palmer amaranth populations were evaluated for sensitivity to the labelled field rate (1X), and rated as resistant (≤49% injury), less sensitive (50% to 89% injury), or susceptible (90% to 100% injury). For glyphosate, 62%, 19%, 13%, and 13% of the populations from the High Plains, Central Texas, Rio Grande Valley, and Lower Gulf Coast, respectively, were resistant. Resistance to atrazine was more common in Palmer amaranth populations from the High Plains than in other regions, with 16% of the populations resistant and 22% less sensitive. Approximately 90% of the populations from the High Plains that exhibited resistance to atrazine POST also were resistant to atrazine PRE. Of the 160 populations tested for pyrithiobac, approximately 99% were resistant or less sensitive, regardless of the region. No resistance was found to fomesafen, tembotrione, or dicamba. However, 22% of the populations from the High Plains were less sensitive to 1X (93 g ai ha-1) tembotrione, but were killed at 2X, illustrating the background variability in sensitivity to this herbicide. For dicamba, three populations, all from the High Plains, exhibited less sensitivity at the 1X rate (controlled at the 2X rate; 1X = 560 g ae ha-1). One population exhibited multiple resistance to three herbicides with distinct sites of action (SOAs) involving acetolactate synthase, 5-enolpyruvylshikimate-3-phosphate synthase, and photosystem II inhibitors. Palmer amaranth populations exhibited less sensitivity to approximately 15 combinations of herbicides involving up to five SOAs. Dose-response assays conducted on the populations most resistant to glyphosate, pyrithiobac, or atrazine indicated they were 30-, 32-, or 49-fold or more resistant to these herbicides, respectively, compared with a susceptible standard.Nomenclature: Atrazine; dicamba; fomesafen; glyphosate; pyrithiobac; tembotrione; Palmer amaranth, Amaranthus palmeri S. Watson

In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat.The object detection neural networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels.CenterNet, Faster R-CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

To enable robotic weed control, we develop algorithms to detect nutsedge weed from bermudagrass turf. Due to the similarity between the weed and the background turf, manual data labeling is expensive and error-prone. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback-Leibler divergence which accommodates uncertainty in the labeling process. We implement the proposed algorithm and compare it with both Faster R-CNN and Mask R-CNN. The results show that our design can effectively overcome the impact of imprecise and insufficient training sample issues and significantly outperform the Faster R-CNN counterpart with a false negative rate of only 0.4%. In particular, our approach also reduces labeling time by 95% while achieving better performance if comparing with the original Mask R-CNN approach.

Seed shattering refers to the natural shedding of seeds when they ripe, a phenomenon typically observed in wild and weedy plant species. The timing and extent of this phenomenon varies considerably among plant species. Seed shattering is primarily a genetically controlled trait; however, it is significantly influenced by environmental conditions, management practices and their interactions, especially in agro-ecosystems. This trait is undesirable in domesticated crops where consistent efforts have been made to minimize it through conventional and molecular breeding approaches. However, this evolutionary trait serves as an important fitness and survival mechanism for most weeds that utilize it to ensure efficient dispersal of their seeds, paving the way for persistent soil seedbank development and sustained future populations. Weeds have continuously evolved variations in seed shattering as an adaptation under changing management regimes. High seed retention is common in many cropping weeds where weed maturity coincides with crop harvest, facilitating seed dispersal through harvesting operations, though some weeds have notoriously high seed shattering before crop harvest. However, high seed retention in some of the most problematic agricultural weed species such as annual ryegrass (Lolium rigidum), wild radish (Raphanus raphanistrum), and weedy amaranths (Amaranthus spp.) provides an opportunity to implement innovative weed management approaches such as harvest weed seed control, which aims at capturing and destroying weed seeds retained at crop harvest. The integration of such management options with other practices is important to avoid the rapid evolution of high seed shattering in target weed species. Advances in genetics and molecular biology have shown promise for reducing seed shattering in important crops, which could be exploited for manipulating seed shattering in weed species. Future research should focus on developing a better understanding of various seed shattering mechanisms in plants in relation to changing climatic and management regimes.

Amaranthus palmeri, ranked as the most prolific and troublesome weed in North America, has evolved resistance to several herbicide sites of action. Repeated use of any one herbicide, especially at lower than recommended doses, can lead to evolution of weed resistance, and, therefore, a better understanding of the process of resistance evolution is essential for the management of A. palmeri and other difficult-to-control weed species. Amaranthus palmeri rapidly developed resistance to 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors such as mesotrione. The objective of this study was to test the potential for low-dose applications of mesotrione to select for reduced susceptibility over multiple generations in an A. palmeri population collected from an agricultural field in 2001. F0 plants from the population were initially treated with sub-lethal mesotrione rates and evaluated for survival three weeks after treatment. All F0 plants were controlled at the 1× rate (x = 105 g ai ha−1). However, 2.5% of the F0 plants survived the 0.5× treatment. The recurrent selection process using plants surviving various mesotrione rates was continued until the F4 generation was reached. Based on the GR50 values, the sensitivity index was determined to be 1.7 for the F4 generation. Compared to F0, HPPD gene expression level in the F3 population increased. Results indicate that after several rounds of recurrent selection, the successive generations of A. palmeri became less responsive to mesotrione, which may explain the reduced sensitivity of this weed to HPPD-inhibiting herbicides. The results have significance in light of the recently released soybean and soon to be released cotton varieties with resistance to HPPD inhibitors.

Recent computer vision techniques based on convolutional neural networks (CNNs) are considered state-of-the-art tools in weed mapping. However, their performance has been shown to be sensitive to image quality degradation. Variation in lighting conditions adds another level of complexity to weed mapping. We focus on determining the influence of image quality and light consistency on the performance of CNNs in weed mapping by simulating the image formation pipeline. Faster Region-based CNN (R-CNN) and Mask R-CNN were used as CNN examples for object detection and instance segmentation, respectively, while semantic segmentation was represented by Deeplab-v3. The degradations simulated in this study included resolution reduction, overexposure, Gaussian blur, motion blur, and noise. The results showed that the CNN performance was most impacted by resolution, regardless of plant size. When the training and testing images had the same quality, Faster R-CNN and Mask R-CNN were moderately tolerant to low levels of overexposure, Gaussian blur, motion blur, and noise. Deeplab-v3, on the other hand, tolerated overexposure, motion blur, and noise at all tested levels. In most cases, quality inconsistency between the training and testing images reduced CNN performance. However, CNN models trained on low-quality images were more tolerant against quality inconsistency than those trained by high-quality images. Light inconsistency also reduced CNN performance. Increasing the diversity of lighting conditions in the training images may alleviate the performance reduction but does not provide the same benefit from the number increase of images with the same lighting condition. These results provide insights into the impact of image quality and light consistency on CNN performance. The quality threshold established in this study can be used to guide the selection of camera parameters in future weed mapping applications.

BACKGROUND Italian ryegrass (Lolium perenne ssp. multiflorum) is one of the major winter annual weeds worldwide. In this research, diversity for seed morpho-physiological traits such as seed weight, seed size, awnedness, dormancy, speed of germination, and seed vigor among Italian ryegrass populations collected from the Texas Blacklands region were assessed, and potential association with herbicide resistance was investigated. RESULTS A high degree of diversity was observed among the populations for 100-seed weight (125-256 mg), seed length (4.8-6.6 mm), awn length (0-6 mm), and total seedling length (9-14 cm at 21 days after seed germination). Inter-population range for seed dormancy was higher in the freshly harvested seed (31-85%), which reduced to 18 to 62% at 9 months after harvest. Populations with high initial seed dormancy (> 70% dormancy) released dormancy at a faster rate than the low dormancy group (

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 Public domain – authored by a U.S. government employee

This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems., In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making., Muthukumar V. Bagavathiannan’s research program is supported by state and federal funds appropriated to Texas A&M AgriLife Research. Gayle J. Somerville was supported by the UKRI project of evaluating people-environment trade-offs through low-tech intensification of livestock management in communal grazing systems in South Africa (BB/S014683/1) whilst working at Rothamsted. Brian J. Schutte is supported by state and federal funds appropriated to the New Mexico Agricultural Experiment Station. JLG-A was funded by FEDER (European Regional Development Funds) and the Spanish Ministry of Economy and Competitiveness grants (projects AGL2015-64130-R). Guillermo R. Chantre received funding from Agencia Nacional de Promoción Científica y Tecnológica MINCyT (PICT-2016-1575) and Universidad Nacional del Sur of Argentina (PGI 24/A225).

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&middot, ha&minus, 1 and a CO2-pressurized backpack sprayer at a 140 L&middot, 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&middot, 1 was 4-fold greater than that for the backpack sprayer at 140 L&middot, 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.

Vegetable producers are often concerned about potential herbicide carryover and injury to vegetables following the application of livestock manure/compost from animals fed with treated hay. A greenhouse study was conducted to evaluate the effect of activated charcoal on soil residual activity of the herbicides aminopyralid, aminocyclopyrachlor, and picloram, applied at recommended field rates of 120 g ae, 70 g ai, and 303 g ai ha−1 respectively, on tomato, okra, and cantaloupe. A commercial source of activated charcoal was used at four rates [0, 0.5X, 1X, and 2X (1X = 336 kg ha−1)]. When no activated charcoal was applied, aminopyralid caused the greatest injury to tomato and okra, leading to plant death at 4 weeks after emergence/transplanting (WAE/T). At the highest rate (2X) of activated charcoal, crop injuries due to aminopyralid were reduced by 72 and 78% for okra and cantaloupe, respectively at 4 WAE, while tomato did not respond to the 2X rate of activated charcoal. For aminocyclopyrachlor, even a much lower rate (0.5X) of activated charcoal was sufficient to reduce about 70% of the injury caused on cantaloupe and okra. For picloram, activated charcoal application at the 2X rate reduced injury by 78–94% across the three vegetable species investigated. Under low herbicide residue levels that usually occur under carryover situations, a much lower rate of activated charcoal (

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 &times, 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.

Despite the best weed control efforts, weed escapes are often present in large production fields prior to harvest, contributing to seed rain and species persistence. Late-season surveys were conducted in cotton (Gossypium hirsutum L.) fields in Texas in 2016 and 2017 to identify common weed species present as escapes and estimate seed rain potential of Palmer amaranth (Amaranthus palmeri S. Watson) and waterhemp [A. tuberculatus (Moq.) J.D. Sauer], two troublesome weed species with high fecundity. A total of 400 cotton fields across four major cotton-producing regions in Texas [High Plains (HP), Gulf Coast (GC), Central Texas, and Blacklands] were surveyed. Results have revealed that A. palmeri, Texas millet [Urochloa texana (Buckley) R. Webster], A. tuberculatus, ragweed parthenium (Parthenium hysterophorus L.), and barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] were the top five weed escapes present in cotton fields. Amaranthus palmeri was the most prevalent weed in the HP and Lower GC regions, whereas A. tuberculatus escapes were predominantly observed in the Upper GC and Blacklands regions. On average, 9.4% of an individual field was infested with A. palmeri escapes in the Lower GC region; however, it ranged between 5.1 and 8.1% in the HP region. Average A. palmeri density ranged from 405 (Central Texas) to 3,543 plants ha−1 (Lower GC). The greatest seed rain potential by A. palmeri escapes was observed in the upper HP region (13.9 million seeds ha−1), whereas the seed rain potential of A. tuberculatus escapes was the greatest in the Blacklands (12.9 million seeds ha−1) and the upper GC regions (9.8 million seeds ha−1). Results indicated that seed rain from late-season A. palmeri and A. tuberculatus escapes are significant in Texas cotton, and effective management of these escapes is imperative for minimizing seedbank inputs and impacting species persistence.