Knowledge of weed seeds present in the soil seedbank is important for understanding population dynamics and forecasting future weed infestations. Quantification of the weed seedbank has historically been laborious, and few studies have attempted to quantify seedbanks on the scale required to make management decisions. An accurate, efficient, and ideally automated method to identify weed seeds in field samples is needed. To achieve sufficient precision, we leveraged YOLOv8, a machine learning object detection to accurately identify and count weed seeds obtained from the soil seedbank and weed seed collection. The YOLOv8 model, trained and evaluated using high-quality images captured with a digital microscope, achieved an overall accuracy and precision exceeding 80% confidence in distinguishing various weed seed species in both images and real-time videos. Despite the challenges associated with species having similar seed morphology, the application of YOLOv8 will facilitate rapid and accurate identification of weed seeds for the assessment of future weed management strategies.

Opportunistic use of limited resources is often attributed to invasive species, and as a mature vine, old man’s beard (Clematis vitalba L.) is known to have devastating negative impacts on the trees it colonizes. No previous experimental studies have been published on how easily C. vitalba seedlings can colonize ground covered by other established vegetation. This species has had an increasing presence in forestry blocks and riparian zones in New Zealand, both of which usually maintain some grass cover. To determine the importance of vegetative ground cover for preventing ingress of new C. vitalba plants, this study looked at seedling emergence through the soil and establishment of C. vitalba within four different levels of grassy cover at three sites: (1) ground kept bare after vegetation removal; (2) ground bare at C. vitalba seed sowing, but thereafter allowed to recover; (3) vegetative cover trimmed to 4 cm high at C. vitalba sowing, and then allowed to recover; and (4) unmanaged vegetation. At the highest level of vegetation density (unmanaged vegetation), no C. vitalba seedlings were ever detected throughout a 1-yr monitoring period. At lower ground cover densities, poor seedling emergence was observed, with a maximum of 36% of seeds sown in bare plots producing a seedling. Also, seedlings did not survive past 1 yr, except in bare plots or in plots where vegetation grew sparsely. However, seedlings that did survive began producing multiple stems within 6 mo of emergence. These results indicate that obstacles to seedling emergence and poor development at the young seedling stage when vegetative cover is dense severely limit C. vitalba’s chances to invade new sites via seed. Yet some successful seedling recruitment does occur due to the magnitude of the propagule pressure on the landscape and the difficulty of maintaining high-density ground cover across large areas throughout the year.

Automatic detection and removal of weeds is a challenging task that requires precise sensors. While crops and weeds possess similar features in terms of appearance, they can be discriminated based on spectral information. This can be done because any object has its own specific spectral signature based on its physical structure and chemical contents. This study examined the use of wavelet transform and deep learning for discrimination of weeds from crops. A total of 626 spectral reflectances in the range of 380 to 1,000 nm were obtained for three crops (cucumber [Cucumis sativus L.], tomato [Solanum lycopersicum L.], and bell pepper [Capsicum annuum L.]) and five different weeds (bindweed [Convolvulus spp.], purple nutsedge [Cyperus rotundus L.], narrowleaf plantain [Plantago lanceolata L.], common cinquefoil [Potentilla simplex Michx.], and garden sorrel [Rumex acetosa L.]). Morse wavelet was employed to decompose the spectra and extract the scalograms, which are the RGB representations of the spectral data. Two deep convolutional neural networks (i.e., GoogLeNet and SqueezNet) were employed for the recognition of crops and weeds. In addition, six common classifiers, including linear discriminant analysis, quadratic discriminant analysis, linear support vector machine, quadratic support vector machine, artificial neural networks, and k-nearest neighbors classifier (KNN), were used for the task of crop/weed discrimination to build the comparison with the proposed method. The error of prediction gradually decreased, and a 100% correct classification was achieved after 258 iterations. Analysis showed that SqueezNet provided classification of 100% accuracy, while GoogLeNet’s accuracy was 97.8% for the test set. Among the common classifiers, KNN provided the highest accuracy (i.e., 100%). This study showed that the proposed method can be successfully utilized for classification of crops and weeds.

Weedy rice (Oryza sativa f. spontanea Auct. ex Backer) is a troublesome annual weed from the Gramineae family and infests rice (Oryza sativa L.) fields globally, with a notable presence in China. However, limited information is available regarding the effects of diverse environmental factors on its germination and emergence. A better understanding of the seed biology and ecology of weedy rice is crucial for developing effective weed management strategies. Experiments were conducted to evaluate the effects of temperature, light, soil burial depth, wheat (Triticum aestivum L.) crop residue amount, salt stress, osmotic stress, and radiant heat on the germination and seedling emergence of weedy rice. Weedy rice exhibited robust germination (>98%) when exposed to varying day/night temperatures (20/15 to 35/30 C) and remained unaffected by light conditions. Seedling emergence was not influenced within the top 5-cm soil layer, where 100% of the seedlings emerged. However, emergence decreased as the soil burial depth increased, eventually resulting in no emergence from a burial depth of 11 cm. The soil burial depth required for 50% of the maximum emergence was 8.3 cm. Seedling emergence ranged from 97% to 100% across different amounts of the wheat straw residue cover (0 to 10,000 kg ha−1). The sodium chloride concentration and osmotic potential required for 50% were 230.8 mM and −0.5 MPa, respectively. No germination was observed when weedy rice seeds were exposed to >110 C pretreatment (radiant heat for 5 min), indicating that residue burning could reduce infestation of weedy rice. The insights gained from this study contribute valuable knowledge to enhance the integrated management of weedy rice in China.

The Amaranthus genus contains numerous agronomic weedy species that are widely distributed across the United States. The seeds of many Amaranthus species are morphologically indistinguishable. The Minnesota Department of Agriculture declared Palmer amaranth (Amaranthus palmeri S. Watson) a prohibited noxious weed seed in 2016. Any Amaranthus spp. seeds that are identified as contaminants during routine seed testing require genetic testing to determine whether A. palmeri is present. This research aimed to validate and optimize the molecular detection of A. palmeri in seed. We refined the DNA extraction from pools of Amaranthus spp. seeds ranging in size from 1 to 100 seeds to improve sample testing throughput. Real-time polymerase chain reaction (qPCR) using primers developed by Murphy and colleagues correctly identified the presence of A. palmeri genetics in 84 samples containing 0, 1, 2, 25, or 50 A. palmeri seeds in samples containing up to 100 seeds. The method specificity was examined using 17 Amaranthus species and 4 hybrids of unknown genetics. The Murphy regular qPCR cycle amplified Watson’s amaranth (Amaranthus watsonii Standl.), spleen amaranth (Amaranthus dubius Mart. ex Thell.), and spiny amaranth (Amaranthus spinosus L.), which would result in false-positive calls; however, the fast cycle only identified A. watsonii as a potential false positive. Examination of the Murphy primer binding site revealed an identical sequence for A. palmeri and A. watsonii. Additional markers were evaluated and optimized for use in qPCR to eliminate the risk of a false positive. The additional markers did eliminate the amplification of A. dubius and A. spinosus but did not eliminate the amplification for A. watsonii. Currently, A. watsonii is not known to be distributed outside of its limited native range and is not expected to be encountered in samples.

Pollinators risk exposure to insecticide residue when visiting weedy flowers in urban landscapes. Previous research shows that pollinators are routinely exposed to a variety of pesticides, but herbicides have exhibited minimal toxicity and did not contribute to the modeled risk quotients. Herbicides from different modes of action may deter pollinators from visiting turfgrass weeds, but their temporal influence on floral quality and pollinator foraging is unaddressed. Research experiments were conducted at Blacksburg, VA, in 2023 to assess the effect of four herbicides on floral morphology and ultraviolet (UV) reflectance of three different UV floral classes of weeds and associated pollinator foraging visits. Among 1,080 assessments per weed species, honeybees (Apis mellifera), bumble bees (Bombus spp.), solitary bees (Chelostoma florisomne), and flies (Diptera spp.) accounted for 94%, 2%, 3%, and 1%, respectively, of the total pollinator visitations on white clover (Trifolium repens L.) inflorescences; 71%, 2%, 0%, and 27%, respectively, on dandelion (Taraxacum officinale F.H. Wigg.) flowers; and 0%, 0%, 78%, and 22%, respectively, on bulbous buttercup (Ranunculus bulbosus L.) flowers. Pollinator visitation and floral quality were temporarily affected by herbicide application, with some herbicides eliminating food resources, while others transiently impacted floral quality and density. The combination of 2,4-D + dicamba + MCPP and topramezone eliminated pollinator foraging visits, but on differing temporal scales of 3 d for auxins and 14 d for topramezone. Halosulfuron and sulfentrazone transiently suppressed floral quality and density, with varying degrees of deterrence on pollinators depending on the weed species. All evaluated herbicides reduced radiometric UV reflectance of T. officinale petal apices, but only synthetic auxin and topramezone reduced digitally assessed floral UV-reflecting area. Petal UV reflectance appears to contribute but not solely influence pollinator foraging behavior. UV-absorbing and UV-reflecting flowers differed in UV-reflectance response to herbicides, but pollinators were similarly deterred. Results suggest that herbicides may offer a variety of management solutions to pollinator deterrence in areas slated for insecticide treatment, including long-term or transient deterrence with potential food-resource preservation.

Effective pesticide application is dependent on precise and sufficient delivery of active ingredients to targeted pests. Water-sensitive papers (WSPs) have been used to estimate the stain coverage, droplet density, droplet size, total spray volume, and other spray-quality metrics by analyzing deposit stains using image analysis software. However, because WSPs are expensive, they are typically distributed along unidimensional transects at intervals of 0.5 m or more, which comprises 0.5% or less of the total treated area. This might limit the ability to accurately represent the deposition of agricultural sprayers with irregular patterns, such as agricultural drone sprayers in the early developmental stage. This study introduces a novel approach utilizing white Kraft paper and a blue colorant proxy for assessing spray deposition. A custom Python-based image analysis tool, SprayDAT (Spray Droplet Analysis Tool), was developed and compared with traditional image analysis software, DepositScan. Both models showed increased accuracy in detecting larger objects, with SprayDAT generally performing better for smaller droplets. DepositScan underestimated the total deposited spray volume by up to 2.7 times less compared with the colorant extraction assessed via spectrophotometry and the predicted output based on flow rate, coverage, and speed. Accuracy of software-estimated spray volume declined with increasing total stain coverage, likely due to overlapping stain objects. Droplet density exhibited a Gaussian trend, with peak density at approximately 22% stain cover, offering evidence for overlapped stains for both DepositScan and SprayDAT as stain cover increased. Both models showed exponential growth in volumetric median diameter (VMD) with increasing stain cover. SprayDAT is freely accessible through an online repository. It features a user-friendly interface for batch processing large sets of scanned images and offers versatility for customization to meet individual needs, such as adjusting spread factor, updating the standard curve for spray volume estimation, or modifying the stain detection threshold.

Agricultural spray drone (ASD) use in managed turfgrass has been given limited attention in the scientific literature. Further, deposition patterns of ASD spray have been obscured in previous research by ambient wind, crop canopy interference, and limited sampling resolution. Using a continuous sampling method involving blue colorant and water sprayed over white Kraft paper that was assessed via digital image analysis of stain objects and referenced spectrophotometric analysis of extractant, deposition metrics were estimated across a 29.3-m transect perpendicular to an ASD or ground-sprayer spray swath. The ASD applies very fine droplets that are highly concentrated with herbicide, similar to ultra–low volume treatments, that improved smooth crabgrass [Digitaria ischaemum (Schreb.) Schreb. ex Muhl.] control compared with a ground sprayer when the ASD was operated 2 m above the turf. Unfortunately, these very fine droplets also drift, leading to four times greater droplet density at distance of almost 12 m away from the targeted spray swath following an operational height of 10 m compared with 2 m. As ASD operational height increases, drift and effective swath width at 30% coefficient of variation uniformity increases, while effective application rate, total deposition, and D. ischaemum control by quinclorac herbicide decreased. Total deposition decreased 6% for each meter increase in ASD operational height, likely due to evaporation. The potential losses due to evaporation are a serious consideration for ASD use that has received little attention in the scientific literature. Our data suggest that ASD operational height should be as low as possible, but modification of spray systems may be needed to improve homogeneity of spray pattern.

Herbicide drift to sensitive crops can result in significant injury, yield loss, and even crop destruction. When pesticide drift is reported to the Georgia Department of Agriculture (GDA), tissue samples are collected and analyzed for residues. Seven field studies were conducted in 2020 and 2021 in cooperation with the GDA to evaluate the effect of (1) time interval between simulated drift event and sampling, (2) low-dose herbicide rates, and (3) the sample collection methods on detecting herbicide residues in cotton (Gossypium hirsutum L.) foliage. Simulated drift rates of 2,4-D, dicamba, and imazapyr were applied to non-tolerant cotton in the 8- to 9-leaf stage with plant samples collected at 7 or 21 d after treatment (DAT). During collection, plant sampling consisted of removing entire plants or removing new growth occurring after the 7-leaf stage. Visual cotton injury from 2,4-D reached 43% to 75% at 0.001 and 0.004 kg ae ha−1, respectively; for dicamba, it was 9% to 41% at 0.003 or 0.014 kg ae ha−1, respectively; and for imazapyr, it was 1% to 74% with 0.004 and 0.03 kg ae ha−1 rates, respectively. Yield loss was observed with both rates of 2,4-D (11% to 51%) and with the high rate of imazapyr (52%); dicamba did not influence yield. Herbicide residues were detected in 88%, 88%, and 69% of samples collected from plants treated with 2,4-D, dicamba, and imazapyr, respectively, at 7 DAT compared with 25%, 16%, and 22% when samples were collected at 21 DAT, highlighting the importance of sampling quickly after a drift event. Although the interval between drift event and sampling, drift rate, and sampling method can all influence residue detection for 2,4-D, dicamba, and imazapyr, the factor with the greatest influence is the amount of time between drift and sample collection.

Palmer amaranth (Amaranthus palmeri S. Watson, AMAPA) is one of the most troublesome weeds in North America due to its rapid growth rate, substantial seed production, competitiveness and the evolution of herbicide-resistant populations. Though frequently encountered in the South, Midwest, and Mid-Atlantic regions of the United States, A. palmeri was recently identified in soybean [Glycine max (L.) Merr.] fields in Genesee, Orange, and Steuben counties, NY, where glyphosate was the primary herbicide for in-crop weed control. This research, conducted in 2023, aimed to (1) describe the dose response of three putative resistant NY A. palmeri populations to glyphosate, (2) determine their mechanisms of resistance, and (3) assess their sensitivity to other postemergence herbicides commonly used in NY crop production systems. Based on the effective dose necessary to reduce aboveground biomass by 50% (ED50), the NY populations were 42 to 67 times more resistant to glyphosate compared with a glyphosate-susceptible population. Additionally, the NY populations had elevated EPSPS gene copy numbers ranging from 25 to 135 located within extrachromosomal circular DNA (eccDNA). Label rate applications of Weed Science Society of America (WSSA) Group 2 herbicides killed up to 42% of the NY populations of A. palmeri. Some variability was observed among populations in response to WSSA Group 5 and 27 herbicides. All populations were effectively controlled by labeled rates of herbicides belonging to WSSA Groups 4, 10, 14, and 22. Additional research is warranted to confirm whether NY populations have evolved multiple resistance to herbicides within other WSSA groups and to develop effective A. palmeri management strategies suitable for NY crop production.

Following the application of MCPA/MCPB at 1.7 kg ae ha−1 at a field site near Dresden, ON, Canada, poor control (<50% visible control) of green pigweed (Amaranthus powellii S. Watson) was observed. Amaranthus powellii is a common weed in Ontario crop production, and its evolution of resistance to synthetic auxin herbicides (SAHs) could pose a risk to crop yields. The suspected resistant A. powellii population (R) was used in dose–response and field experiments to determine resistance to SAHs. The objective of these studies was to determine whether this population of A. powellii is resistant to MCPA and cross-resistant to other SAHs. The GR50 (herbicide dose that causes a 50% reduction in plant aboveground biomass) values were determined by fitting plant dry weight data, obtained following application with seven SAHs, to a four-parameter log-logistic equation and were compared between the suspected-resistant (R) population and a known susceptible (S) population of A. powellii. The field trial was conducted in 2017, 2018, 2019, and 2021 in corn (Zea mays L.) and consisted of 11 postemergence SAH treatments. The GR50 values differed between the R and S populations following application with MCPA, aminocyclopyrachlor, dichlorprop-p, and mecoprop, resulting in resistance factors of 4.4, 3.0, 2.5, and 2.4, respectively. In the field study, dicamba and MCPA ester controlled A. powellii 84% and 30%, respectively, at 8 wk after treatment application (WAA). The control of Amaranthus powellii with all SAHs applied POST in corn was poor (<90% visible control) at 8 WAA. Both studies confirmed resistance to SAHs in this population of A. powellii, which will create limitations for farmers aiming to control this weed.

Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

New agronomic practices are emerging in the green cane system to utilize sugarcane (Saccharum officinarum L.) straw for energy cogeneration, which necessitates its removal from the soil surface. This study has three main objectives: (1) evaluate the population dynamics and composition of Jamaican crabgrass (Digitaria horizontalis Willd.) and large crabgrass [Digitaria sanguinalis (L.) Scop.] under different sugarcane straw amounts, with and without herbicide treatment; (2) assess the development of sugarcane under different straw amounts; and (3) determine the amount of sugarcane straw that should be kept on the soil surface after harvest to ensure that it does not compromise the chemical control for Digitaria spp. in ratoon cane in a green cane system. We conducted this research at two experimental sites, one at the beginning and the other during the middle of the harvest season, over a span of 2 yr. Our primary treatments consisted of different amounts of sugarcane straw after harvest on the soil surface (0, 5, 10, and 15 Mg ha−1), while secondary treatments included the herbicide application (sulfentrazone + tebuthiuron for the beginning of harvest season and isoxaflutole + tebuthiuron for the middle of harvest season). The Digitaria spp. exhibited higher density (four times more) and dry matter (two times more) in scenarios with a lower sugarcane straw amount (5 Mg ha−1) on the soil surface and no herbicide application. However, a higher straw amount (15 Mg ha−1) contributed to reduced Digitaria spp. infestation and to improved sugarcane yield. According to this research, it is essential to maintain at least 10 Mg ha−1 of sugarcane straw on the soil surface and remove only 5 Mg ha−1 for energy cogeneration.

The Northern Great Plains of the United States is a major production region for organic pulse crops that are prone to yield losses due to weeds. Weed management in organic systems relies on the integration of several tactics to stack additive effects and for redundancy to deal with variable efficacy of individual weed management practices. To address the need for effective, integrated weed management, we conducted a 2-yr trial that evaluated the effects of planting date, seeding rate, and preemergent weed control practices (shallow tillage and flame weeding) on weed biomass, crop density, and yield in organically managed chickpea (Cicer arietinum L.) in Montana. Stacking weed management practices increased yields. Early planting had the largest effects on yields, increasing them by 1.8- to 3.6-fold compared with planting 10 to 14 d later. Increasing the seeding rate from the standard rate (43 viable seeds m−2) by 50% increased yields by 47% from 889 to 1,304 kg ha−1. Both preemergent weed control practices increased yields by 40% to 50 % relative to the non-weeded controls. By integrating all three practices, yields of organic chickpea increased greater than 6-fold from 318 kg ha−1 in the controls to 2,006 kg ha−1. The effects of weed control treatments on midseason weed biomass were complex and variable. Although efficacy of the cultural (seeding rate and planting date) and physical (preemergent) treatments on weed biomass varied between years and when combined with other treatments, their full integration, that is, early planted, at higher seeding rate, and preemergent weed control, produced consistently lower weed biomass (84% reduction on average) compared with the standard grower practice (later planting, standard seeding rate, no preemergent weed control). The results lend support to the concept of integrating multiple weed management practices to achieve weed control and high yields in organically managed crops.

Sourgrass [Digitaria insularis (L.) Mez ex Ekman] is considered the most troublesome weed in agronomic crops in South America. Overreliance on glyphosate has selected for resistant populations, although the resistance mechanisms remain unknown. Recently, populations were identified that exhibited multiple resistance to 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) and acetyl-CoA carboxylase (ACCase) inhibitors, posing a significant challenge due to the lack of alternative control options. This project aimed to identify the resistance patterns and levels to glyphosate and ACCase inhibitors of three suspected resistant populations (P1, P2, and P3), and elucidate the resistance mechanisms. We performed dose–response experiments with clethodim, fluazifop-P-butyl, glyphosate, and pinoxaden to identify the possibility of cross- and multiple resistance and to quantify the resistance levels. We sequenced the ACCase and EPSPS genes to test the hypothesis that target-site mutations were involved in the resistance mechanisms, given the resistance patterns observed. Our results indicated that two of the tested populations, P1 and P2, were multiple resistant to glyphosate and all ACCase-inhibitor classes, while P3 was resistant to glyphosate only. Resistance levels varied by herbicide, with resistance indices ranging from 2.7- to nearly 2,000-fold. We identified an amino acid substitution in ACCase at position 2078 (Asp-2078-Gly), homozygous for both P1 and P2, corroborating the resistance patterns observed. Interestingly, EPSPS sequencing identified multiple heterozygous DNA polymorphisms that resulted in amino acid substitutions at positions 106 (P1 and P2) or at both 102 and 106 (P3), indicating multiple evolutionary origins of glyphosate-resistance evolution. We show for the first time the genetic mechanisms of multiple resistance to glyphosate and ACCase in D. insularis, and provide a thorough discussion of the evolutionary and management implications of our work.

Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

Information regarding the prevalence and distribution of herbicide-resistant waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in Minnesota is limited. Whole-plant bioassays were conducted in the greenhouse on 90 A. tuberculatus populations collected from 47 counties in Minnesota. Eight postemergence herbicides, 2,4-D, atrazine, dicamba, fomesafen, glufosinate, glyphosate, imazamox, and mesotrione, were applied at 1× and 3× the labeled doses. Based on their responses, populations were classified into highly resistant (≥40 % survival at 3× the labeled dose), moderately resistant (<40% survival at 3× the labeled dose but ≥40% survival at 1× the labeled dose), less sensitive (10% to 39% survival at 1× the labeled dose), and susceptible (<10% survival at 1× the labeled dose) categories. All 90 populations were resistant to imazamox, while 89% were resistant to glyphosate. Atrazine, fomesafen, and mesotrione resistance was observed in 47%, 31%, and 22% of all populations, respectively. Ten percent of the populations were resistant to 2,4-D, and 2 of 90 populations exhibited >40% survival following dicamba application at the labeled dose. No population was confirmed to be resistant to glufosinate. However, 22% of all populations were classified as less sensitive to glufosinate. Eighty-two populations were found to be multiple-herbicide resistant. Among these, 15 populations exhibited resistance to four different herbicide sites of action (SOAs); 7 and 4 populations were resistant to five and six SOAs, respectively. All six-way-resistant populations were from southwest Minnesota. Two populations, one from Lincoln County and the other from Lyon County, were resistant to 2,4-D, atrazine, dicamba, fomesafen, glyphosate, imazamox, and mesotrione, leaving only glufosinate as a postemergence control option for these populations in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.]. Diversified management tactics, including nonchemical control measures along with herbicide applications from effective SOAs, should be implemented to slow down the evolution and spread of herbicide-resistant A. tuberculatus populations.

Slender Russian thistle (Salsola collina Pall.) is a troublesome weed distributed mainly in the cropping regions of northern China that produces heteromorphic seeds in the same plant. However, limited information is available on the germination ecology of heteromorphic seeds in S. collina. Thus, the present study was conducted to verify the effect of alternating temperature conditions, light conditions, winged perianth, salt concentrations, water stress, and burial depths on the seed germination or seedling emergence of S. collina. The results showed that S. collina produced two different types of fruits/seeds that significantly differed in seed size, seed color, external structure, and germination/dormancy behavior. The type A seeds (green seeds) were nondormant, and the germination percentage was >96% at all alternating day/night temperatures and light conditions; whereas type B seeds (yellow seeds) exhibited dormancy characteristics and poor germination (≤1%). Moreover, the winged perianth did not inhibit the germination of S. collina green seeds. The germination of green seeds declined rapidly when NaCl concentration exceeded 100 mM, and only 2.22% germination was observed at 600 mM NaCl. About 62.00% of green seeds germinated at −0.6 MPa, and 8.00% germination was obtained at −1.2 MPa. The seedling emergence declined with an increase of burial depth, and decreased sharply when the burial depth exceeded 1.0 cm. Only 8.33% seedling emergence occurred at a burial depth of 4.0 cm. The results gathered from present study will help to illustrate the ecological adaptation strategy of S. collina and indicate that shallow tillage can effectively minimize the seedling emergence of S. collina.

Isoproturon phytotoxicity to wheat (Triticum aestivum L.) is a worry for many farmers in chemical control of weeds in wheat fields, especially in subzero weather conditions. Iron chlorin e6 (ICe6), a new plant growth regulator, has been reported to enhance crop stress resistance to alleviate damage caused by stress; however, it is not clear whether ICe6 has an alleviative effect on isoproturon phytotoxicity to wheat. We determined the alleviative effect of ICe6 on isoproturon phytotoxicity to wheat, and 0.018 g ai ha−1 was the optimal dose. Meanwhile, we also studied the photosynthetic pigment content, photosynthetic parameters, oxidative stress indicators, and antioxidant enzyme activity of wheat treated with the three different treatments. We found that the photosynthetic pigment content, antioxidant enzyme activity, and photosynthesis of wheat damaged by isoproturon were significantly lower than those of the control, and the hydrogen peroxide (H2O2) and malondialdehyde (MDA) content increased. These results indicate that isoproturon stress significantly weakened the photosynthetic and antioxidant capacity of wheat. The photosynthetic pigment content, photosynthetic parameters (excluding intercellular CO2 concentration), and antioxidant enzyme activity of isoproturon+ICe6– treated wheat were significantly higher than those of isoproturon-treated wheat. The H2O2 and MDA content was significantly lower than that of isoproturon-treated wheat. These results indicate that ICe6 treatment maintained the photosynthetic pigment content of wheat and relatively improved photosynthetic capacity, allowing photosynthesis to proceed normally. ICe6 treatment also limits the decrease in antioxidant enzyme activity, effectively clearing excess reactive oxygen species and ultimately alleviating membrane lipid peroxidation damage. In summary, ICe6 not only enhances stress resistance and increases yield in crops such as soybean [Glycine max (L.) Merr.] and canola (Brassica napus L.), but also has an alleviating effect on the isoproturon phytotoxicity to wheat, which is manifested by the improvement of photosynthetic and antioxidant abilities, ultimately leading to an increase in wheat shoot height and shoot fresh weight.

Using convolutional neural networks (CNNs) for image recognition is effective for early weed detection. However, the impact of training data curation, specifically concerning morphological changes during the early growth phases of weeds, on recognition robustness remains unclear. We focused on four weed species (giant ragweed [Ambrosia trifida L.], red morningglory [Ipomoea coccinea L.], pitted morningglory [Ipomoea lacunosa L.], and burcucumber [Sicyos angulatus L.]) with varying cotyledon and true leaf shapes. Creating 16 models in total, we employed four dataset patterns with different growth stage combinations, two image recognition algorithms (object detection: You Look Only Once [YOLO] v5 and image classification: Visual Geometry Group [VGG] 19), and two conditions regarding the number of species treated (four and two species). We evaluated the effects of growth stage training on weed recognition success using two datasets. One evaluation revealed superior results with a single class/species training dataset, achieving >90% average precision for detection and classification accuracy under most conditions. The other dataset revealed that merging different growth stages with different shapes as a class effectively prevented misrecognition among different species when using YOLOv5. Both results suggest that integrating different shapes in a plant species as a single class is effective for maintaining robust recognition success amid temporal morphological changes during the early growth stage. This finding not only enhances early detection of weed seedlings but also bolsters the robustness of general plant species identification.