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Annual bluegrass is one of the most problematic weeds in the turfgrass industry, exhibiting both cross-resistance and multiple-herbicide resistance. Prodiamine, pronamide, and indaziflam are commonly used preemergence herbicides for the control of this species on golf courses in the southern United States. There have been increasing anecdotal reports of annual bluegrass populations escaping control with these herbicides, but resistance has yet to be confirmed. To evaluate the response of annual bluegrass to three herbicides, populations were collected from golf courses, athletic fields, and landscape areas in Texas and Florida, and a dose-response assay was conducted on populations that were suspected to be resistant to and known to be susceptible to prodiamine, pronamide, and indaziflam. The suspected-resistant populations showed survival to prodiamine at 32 times the recommended field rate (both populations from Florida and Texas) of 736 g ai ha–1, and to pronamide at 32 times (the Florida populations) or 16 times (the Texas populations) the recommended field rate of 1,156 g ha–1. In contrast, the known susceptible populations attained 100% mortality at rates as low as 46 and 578 g ha–1, respectively, from applications of prodiamine and pronamide. For indaziflam, the suspected-resistant populations showed reduced sensitivity up to the recommended field rate of 55 g ha–1, but they were controlled when treated with a rate twice that of the field rate. Overall, annual bluegrass populations with resistance to prodiamine and pronamide, and reduced sensitivity to indaziflam (at the recommended field rate) were confirmed from golf courses in Florida and Texas. In the presence of herbicide-resistant annual bluegrass populations, especially to commonly used herbicides such as prodiamine and pronamide, turfgrass managers should adopt integrated management strategies and frequently rotate herbicide sites of action, rather than relying solely on microtubule-assembly inhibitors or cellulose biosynthesis inhibitors, to control this species. Nomenclature: Indaziflam; prodiamine; pronamide; annual bluegrass, Poa annua L.

This chapter explores how sound turf management practices can be used to help control pests and reduce the need for pesticides. Pest management in the turfgrass industry varies by region. Weeds may be divided by their life cycles: into annuals, biennials, and perennials. The weed life cycle is important because it informs the turf manager about the best time of year to control the weed. Mowing is one of the cultural practices that has the greatest effect on weed infestation. Fertilization timing can affect weed infestation. Fertilizers high in phosphorus (P) are used at the time of establishment, whereas P becomes less important once the mature plants have a well‐developed root system. Proper irrigation can also be part of a weed control program. Many herbicides are specific to weed types. Each herbicide can be classified by three names: trade name, common name, and chemical name.

Tropical signalgrass (TSG) causes serious problems for sod production and turf maintenance in Florida. Other grasses such as large crabgrass (CG), smutgrass (SG), thin paspalum (TP), and torpedograss (TG) can be problematic as well. Several emulsion formulations composed of mycelium or mycelium-free culture filtrate (or both) of the fungal pathogen Drechslera gigantea (DG) and Sunspray 6E oil were tested with or without ammonium sulfate or pelargonic acid (n-nonanoic acid; a natural product registered as a biorational herbicide) in greenhouse and field trials. A 30% Sunspray 6E oil formulation containing DG mycelium (10 g), DG culture filtrate (70 ml), and 4.5 g of ammonium sulfate caused 88 to 100% injury on TSG, CG, SG, and TG in greenhouse trials. The injury resulted from disease as well as phytotoxicity of the culture filtrate, oil, and ammonium sulfate. An emulsion formulation composed of 30% Sunspray 6E oil and 70% DG culture filtrate amended with 2% (v/v) pelargonic acid killed SG 2 wk after application. DG formulations containing ammonium sulfate or pelargonic acid produced lower levels of injury when treated grasses were exposed to a 24-h dew period compared with those treated and not exposed to dew. Formulations containing DG mycelium, DG culture filtrate, and ammonium sulfate or pelargonic acid are effective and promising for control of weedy grasses. Further evaluations of these formulations under field conditions are justified.

Background Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F 1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

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. Nomenclature: Annual fleabane; Erigeron annuus L. Pers.; Persian speedwell; Veronica persica Poir.; white clover; Trifolium repens L.; apple; Malus domestica L.; zoysiagrass; Zoysia japonica Steud.

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

Targeted spraying application technologies have the capacity to drastically reduce herbicide inputs, but to be successful, the performance of both machine vision–based weed detection and actuator efficiency needs to be optimized. This study assessed (1) the performance of spotted spurge recognition in ‘Latitude 36’ bermudagrass turf canopy using the You Only Look Once (YOLOv3) real-time multiobject detection algorithm and (2) the impact of various nozzle densities on model efficiency and projected herbicide reduction under simulated conditions. The YOLOv3 model was trained and validated with a data set of 1,191 images. The simulation design consisted of four grid matrix regimes (3 × 3, 6 × 6, 12 × 12, and 24 × 24), which would then correspond to 3, 6, 12, and 24 nonoverlapping nozzles, respectively, covering a 50-cm-wide band. Simulated efficiency testing was conducted using 50 images containing predictions (labels) generated with the trained YOLO model and by applying each of the grid matrixes to individual images. The model resulted in prediction accuracy of an F1 score of 0.62, precision of 0.65, and a recall value of 0.60. Increased nozzle density (from 3 to 12) improved actuator precision and predicted herbicide-use efficiency with a reduction in the false hits ratio from ∼30% to 5%. The area required to ensure herbicide deposition to all spotted spurge detected within images was reduced to 18%, resulting in ∼80% herbicide savings compared to broadcast application. Slightly greater precision was predicted with 24 nozzles but was not statistically different from the 12-nozzle scenario. Using this turf/weed model as a basis, optimal actuator efficacy and herbicide savings would occur by increasing nozzle density from 1 to 12 nozzles within the context of a single band. Nomenclature: Spotted spurge, Chamaesyce maculata (L.) Small; bermudagrass, Cynodon spp.

Spot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed [Murdannia nudiflora (L.) Brenan], dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass [Urochloa distachya (L.) T.Q. Nguyen] in bermudagrass [Cynodon dactylon (L.) Pers.]. VGGNet generally outperformed AlexNet and GoogLeNet in detecting selected grassy weeds. For detection of P. dilatatum, VGGNet achieved high F1 scores (≥0.97) and recall values (≥0.99). A single VGGNet model exhibited high F1 scores (≥0.93) and recall values (1.00) that reliably detected Digitaria spp., M. nudiflora, P. dilatatum, and U. distachya. Low weed density reduced the recall values of AlexNet at detecting all weed species and GoogLeNet at detecting Digitaria spp. In comparison, VGGNet achieved excellent performances (overall accuracy = 1.00) at detecting all weed species in both high and low weed-density scenarios. These results demonstrate the feasibility of using DCNN for detection of grass or grass-like weeds in turfgrass systems.

Herbicide restrictions require alternative strategies for turfgrass weed control. This growth chamber study investigated the interference of 27 Festuca cultivars selected from five Festuca species using white clover (Trifolium repens L.), lawn daisy (Bellis perennis L.), and yarrow (Achillea millefolium L.) as indicator species. At 13 days after sowing (DAS), 20 weed seeds were placed in between 60 grass seeds. Lawn daisy was highly sensitive to the presence of all grasses, and results are not presented. Festuca species or individual cultivars did not affect the germination percentage and mean germination period of white clover and yarrow. The presence of tall fescue [Schedonorus arundinaceus (Schreb.) Dumort., nom. cons.]) species reduced white clover root length by 71.6% and slender creeping red fescue [Festuca rubra L. ssp. littoralis (G.Mey.) Auquier] by 44.5% at 30 DAS. Within cultivars, reductions of white clover roots ranged from 81.7% (Regenerate) to 24.8% (Cathrine). Root length for yarrow was reduced by an average of 75% with no difference among Festuca species. Cultivar effects ranged from 91.8% for Barcesar to 62.9% for Samanta. For both white clover and yarrow, negative correlations were determined between Festuca biomass and the root length of both weeds: −0.241*** (white clover) and yarrow −0.168* (yarrow). Such a relationship suggests that part of the inhibiting effect can be directly attributed to Festuca biomass. We conclude that differences in interference potential between cultivars within species are as important as differences between species. White clover appeared to be the most discriminative species for growth interference studies with Festuca. Core Ideas Pesticide restrictions require alternative strategies for turfgrass weed control. Daisy (Bellis perennis L.) was highly sensitive to the presence of 27 cultivars from five Festuca species. Festuca cultivars did not affect the germination percentage and mean germination period of clover and yarrow. Root length for yarrow was reduced by an average of 75% with no difference between Festuca species. Root length of white clover was reduced in the presence of Festuca cultivars.

Auxinic herbicides are widely used for control of broadleaf weeds in cereal crops and turfgrass. These herbicides are structurally similar to the natural plant hormone auxin, and induce several of the same physiological and biochemical responses at low concentrations. After several decades of research to understand the auxin signal transduction pathway, the receptors for auxin binding and resultant biochemical and physiological responses have recently been discovered in plants. However, the precise mode of action for the auxinic herbicides is not completely understood despite their extensive use in agriculture for over six decades. Auxinic herbicide-resistant weed biotypes offer excellent model species for uncovering the mode of action as well as resistance to these compounds. Compared with other herbicide families, the incidence of resistance to auxinic herbicides is relatively low, with only 29 auxinic herbicide-resistant weed species discovered to date. The relatively low incidence of resistance to auxinic herbicides has been attributed to the presence of rare alleles imparting resistance in natural weed populations, the potential for fitness penalties due to mutations conferring resistance in weeds, and the complex mode of action of auxinic herbicides in sensitive dicot plants. This review discusses recent advances in the auxin signal transduction pathway and its relation to auxinic herbicide mode of action. Furthermore, comprehensive information about the genetics and inheritance of auxinic herbicide resistance and case studies examining mechanisms of resistance in auxinic herbicide-resistant broadleaf weed biotypes are provided. Within the context of recent findings pertaining to auxin biology and mechanisms of resistance to auxinic herbicides, agronomic implications of the evolution of resistance to these herbicides are discussed in light of new auxinic herbicide-resistant crops that will be commercialized in the near future.