A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations.

Nutrient regulation in aquaponic environments has been a topic of research for many years. Most studies have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been conducted on commercial-scale applications. In our model, the input data were sourced on a weekly basis from three commercial aquaponic farms in Southeast Texas over the course of a year. Due to the limited number of data points, dimensionality reduction techniques such as pairwise correlation matrix were used to remove the highly correlated predictors. Feature selection techniques such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors, and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentration to be maintained in the aquaponic solution to sustain healthy growth of tilapia fish and lettuce plants in a coupled set-up. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop.

Selective fertilization with phosphite allows unhindered growth of cotton plants expressing the ptxD gene while suppressing weeds.

Weeds, which have been the bane of agriculture since the beginning of civilization, are managed manually, mechanically, and, more recently, by chemicals. However, chemical control options are rapidly shrinking due to the recent rise in the number of herbicide-resistant weeds in crop fields, with few alternatives on the horizon. Therefore, there is an urgent need for alternative weed suppression systems to sustain crop productivity while reducing our dependence on herbicides and tillage. Such a development will also allay some of the negative perceptions associated with the use of herbicide-resistance genes and heavy dependence on herbicides. Transgenic plants expressing the bacterial phosphite dehydrogenase (ptxD) gene gain an ability to convert phosphite (Phi) into orthophosphate [Pi, the metabolizable form of phosphorus (P)]. Such plants allow for a selective fertilization scheme, based on Phi as the sole source of P for the crop, while offering an effective alternative for suppressing weed growth. Here, we show that, when P is supplied in the form of Phi, ptxD-expressing cotton (Gossypium hirsutum L.) plants outcompete, in both artificial substrates and natural soils from agricultural fields, three different monocot and dicot weed species intentionally introduced in the experiments, as well as weeds naturally present in the tested soils. Importantly, the ptxD/Phi system proved highly efficacious in inhibiting the growth of glyphosate-resistant Palmer amaranth. With over 250 weed species resistant to currently available herbicides, ptxD-transgenic plants fertilized with Phi could provide an effective alternative to suppressing the growth of these weeds while providing adequate nutrition to the crop.

Elucidation of sex from mature Palmer amaranth (Amaranthus palmeri) leaves using a portable Raman spectrometer.

Palmer amaranth (Amaranthus palmeri) is a pervasive and troublesome weed species that poses significant challenges to agriculture in the United States. Identifying the sex of Palmer amaranth plants is crucial for developing tailored control measures due to the distinct characteristics and reproductive strategies exhibited by male and female plants. Traditional methods for sex determination are expensive and time-consuming, but recent advancements in spectroscopic techniques offer new possibilities. This study explores the potential of portable Raman spectroscopy for determining the sex of mature Palmer amaranth plants in-field. Raman analysis of the plant leaves reveals spectral differences associated with nitrate salts, lipids, carotenoids, and terpenoids, allowing for high accuracy and reliable identification of the plant's sex; male plants had higher concentrations of these compounds compared to females. It was also found that male plants had higher concentrations of these compounds compared to the females. Raman spectra were analyzed using a machine learning tool, partial least squares discriminant analysis (PLS-DA), to generate accuracies of no less than 83.7% when elucidating sex from acquired spectra. These findings provide insights into the sex-specific characteristics of Palmer amaranth and suggest that Raman analysis, combined with PLS-DA, can be a promising, non-destructive, and efficient method for sex determination in field settings. This approach has implications for developing sex-specific management strategies to monitor and control this invasive weed in real-world environments, benefiting farmers, agronomists, researchers, and master gardeners.

Current status of community resources and priorities for weed genomics research.

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

U.S. cereal rye winter cover crop growth database.

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001-2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha-1, a median of 2,458 kg ha-1, and a standard deviation of 3,163 kg ha-1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

Rate of crop-weed hybridization in Sorghum bicolor × Sorghum halepense is influenced by genetic background, pollen load, and the environment.

The potential for gene flow between cultivated species and their weedy relatives poses agronomic and environmental concerns, particularly when there are opportunities for the transfer of adaptive or agronomic traits such as herbicide resistance into the weedy forms. Grain sorghum (Sorghum bicolor) is an important crop capable of interspecific hybridization with its weedy relative johnsongrass (Sorghum halepense). Previous findings have shown that triploid progenies resulting from S. bicolor × S. halepense crosses typically collapse with only a few developing into mature seeds, whereas tetraploids often fully develop. The objective of this experiment was to determine the impact of S. bicolor genotype and pollen competition on the frequency of hybridization between S. bicolor and S. halepense. A total of 12 different cytoplasmic male sterile S. bicolor genotypes were compared with their respective male fertile lines across 2 years, to assess the frequency of hybridization and seed set when S. halepense served as the pollinator parent. Results indicate significant differences in the frequency of interspecific hybridization among the S. bicolor genotypes, and pollen fertility in S. bicolor reduced the rate of this interspecific hybridization by up to two orders of magnitude. Further, hybridization rates greatly varied across the two study environments. Results are helpful for developing appropriate gene flow mitigation strategies and indicate that gene flow could be reduced by the selection of appropriate seed parents for sorghum hybrids.

Adaptive constraints at the range edge of a widespread and expanding invasive plant.

Identifying the factors that facilitate and limit invasive species' range expansion has both practical and theoretical importance, especially at the range edges. Here, we used reciprocal common garden experiments spanning the North/South and East/West range that include the North American core, intermediate and range edges of the globally invasive plant, Johnsongrass (Sorghum halepense) to investigate the interplay of climate, biotic interactions (i.e. competition) and patterns of adaptation. Our results suggest that the rapid range expansion of Johnsongrass into diverse environments across wide geographies occurred largely without local adaptation, but that further range expansion may be restricted by a fitness trade-off that limits population growth at the range edge. Interestingly, plant competition strongly dampened Johnsongrass growth but did not change the rank order performance of populations within a garden, though this varied among gardens (climates). Our findings highlight the importance of including the range edge when studying the range dynamics of invasive species, especially as we try to understand how invasive species will respond to accelerating global changes.

Nitrogen use efficiency-a key to enhance crop productivity under a changing climate.

Nitrogen (N) is an essential element required for the growth and development of all plants. On a global scale, N is agriculture's most widely used fertilizer nutrient. Studies have shown that crops use only 50% of the applied N effectively, while the rest is lost through various pathways to the surrounding environment. Furthermore, lost N negatively impacts the farmer's return on investment and pollutes the water, soil, and air. Therefore, enhancing nitrogen use efficiency (NUE) is critical in crop improvement programs and agronomic management systems. The major processes responsible for low N use are the volatilization, surface runoff, leaching, and denitrification of N. Improving NUE through agronomic management practices and high-throughput technologies would reduce the need for intensive N application and minimize the negative impact of N on the environment. The harmonization of agronomic, genetic, and biotechnological tools will improve the efficiency of N assimilation in crops and align agricultural systems with global needs to protect environmental functions and resources. Therefore, this review summarizes the literature on nitrogen loss, factors affecting NUE, and agronomic and genetic approaches for improving NUE in various crops and proposes a pathway to bring together agronomic and environmental needs.

History of Herbicide-Resistant Traits in Cotton in the U.S. and the Importance of Integrated Weed Management for Technology Stewardship.

This paper reviews the history of herbicide-resistant (HR) traits in U.S. cotton since the beginning, highlighting the shortcomings of each trait over time that has led to the development of their successor and emphasizing the importance of integrated weed management (IWM) going forward to ensure their long-term sustainability. Introduction of glyphosate-resistant cropping systems has allowed for expansion of no-till systems more reliant on herbicides, favored less diverse crop rotations, and heavily relied on a single herbicide mode of action (MOA). With repeated applications of glyphosate over the years, biotypes of glyphosate-resistant (GR) A. palmeri and other weeds became economically damaging pests in cotton production systems throughout the U.S. Moreover, the reported cases of weeds resistant to different MOA across various parts of the United States has increased. The dicamba- (XtendFlex®) and 2,4-D-resistant (Enlist®) cotton traits (with stacks of glyphosate and glufosinate resistance) were introduced and have been highly adopted in the U.S. to manage HR weeds. Given the current rate of novel herbicide MOA discovery and increase in new HR weed cases, the future of sustainable weed management relies on an integrated approach that includes non-herbicidal methods with herbicides to ensure long-term success.

Pollen-mediated transfer of herbicide resistance between johnsongrass (Sorghum halepense) biotypes.

Johnsongrass (Sorghum halepense) is a troublesome weed in row crop production in the United States. Herbicide resistance is a growing concern in this species, with resistance to ACCase-, ALS-, and EPSPS-inhibitors already reported. Pollen-mediated gene flow (PMGF) is capable of spreading herbicide resistance, but the extent of PMGF has not yet been studied in johnsongrass. Field experiments were conducted in a Nelder-wheel design to quantify the distance and frequency of PMGF from ALS-inhibitor-resistant (AR) to -susceptible (AS) johnsongrass across three environments (summer 2018, fall 2018, and fall 2019). The AR biotype (pollen donor) was established at the center of the wheel (5-m diameter), and a naturally occurring johnsongrass (AS) infestation was utilized as the pollen recipient, in eight directions and at nine distances (5, 10, 15, 20, 25, 35, 40, 45, and 50 m) within each direction. Seeds collected from the AS plants in each distance and direction were screened for survival to the ALS-inhibitor herbicide nicosulfuron (Accent Q) at 95 g ai ha-1 under greenhouse conditions. The survivors (i.e. hybrids) were further confirmed based on the presence of the Trp574Leu mutation. At the closest distance of 5 m, PMGF was 9.6-16.2% across the directions and environments, which progressively declined to 0.8-1.2% at 50 m. The exponential decay model predicted 50% reduction in PMGF at 2.2 m and 90% reduction at 5.8 m from the pollen donor block. Results demonstrate that herbicide resistance can spread between adjacent field populations of johnsongrass through PMGF, which necessitates sound monitoring and management.

Evaluating Cross-Applicability of Weed Detection Models Across Different Crops in Similar Production Environments.

Convolutional neural networks (CNNs) have revolutionized the weed detection process with tremendous improvements in precision and accuracy. However, training these models is time-consuming and computationally demanding; thus, training weed detection models for every crop-weed environment may not be feasible. It is imperative to evaluate how a CNN-based weed detection model trained for a specific crop may perform in other crops. In this study, a CNN model was trained to detect morningglories and grasses in cotton. Assessments were made to gauge the potential of the very model in detecting the same weed species in soybean and corn under two levels of detection complexity (levels 1 and 2). Two popular object detection frameworks, YOLOv4 and Faster R-CNN, were trained to detect weeds under two schemes: Detect_Weed (detecting at weed/crop level) and Detect_Species (detecting at weed species level). In addition, the main cotton dataset was supplemented with different amounts of non-cotton crop images to see if cross-crop applicability can be improved. Both frameworks achieved reasonably high accuracy levels for the cotton test datasets under both schemes (Average Precision-AP: 0.83-0.88 and Mean Average Precision-mAP: 0.65-0.79). The same models performed differently over other crops under both frameworks (AP: 0.33-0.83 and mAP: 0.40-0.85). In particular, relatively higher accuracies were observed for soybean than for corn, and also for complexity level 1 than for level 2. Significant improvements in cross-crop applicability were further observed when additional corn and soybean images were added to the model training. These findings provide valuable insights into improving global applicability of weed detection models.

Deep learning for detecting herbicide weed control spectrum in turfgrass.

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 F1 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.

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of controlling nutrients in the aquaponic solution for optimal growth.[27-35].

A deep learning-based method for classification, detection, and localization of weeds in turfgrass.

BACKGROUND: Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi-classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two-classifier). RESULTS: The VGGNet multi-classifier exhibited an F1 score of 0.950 when used to detect common dandelion and achieved high F1 scores of ≥0.983 to detect and discriminate the subimages containing dallisgrass, purple nutsedge and white clover growing in bermudagrass turf. DenseNet, EfficientNetV2 and RegNet multi-classifiers exhibited high F1 scores of ≥0.984 for detecting dallisgrass and purple nutsedge. Among the evaluated neural networks, EfficientNetV2 two-classifier exhibited the highest F1 scores (≥0.981) for exclusively detecting and discriminating subimages containing weeds and turfgrass. CONCLUSION: The proposed method can accurately identify the grid cells containing weeds and thus precisely locate the weeds on the input images. Overall, we conclude that the proposed method can be used in the machine vision subsystem of smart sprayers to locate weeds and make the decision for precision spraying herbicides onto the individual map cells. © 2022 Society of Chemical Industry.

A novel deep learning-based method for detection of weeds in vegetables.

BACKGROUND: Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep-learning-based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS: The optimal confidence threshold values for YOLO-v3, CenterNet, and Faster R-CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep-learning models had average precision (AP) above 97% in the testing dataset. YOLO-v3 was the most accurate model for detection of vegetables and yielded the highest F 1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO-v3 was similar to CenterNet, but significantly shorter than that of Faster R-CNN. Overall, YOLO-v3 showed the highest accuracy and computational efficiency among the deep-learning architectures evaluated in this study. CONCLUSION: These results demonstrate that deep-learning-based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site-specific robotic weed control in vegetable crops.

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed management systems, but recent developments in neural networks have offered great prospects. However, a major limitation with the neural network models is the requirement of high volumes of data for training. The current study aims at exploring an alternative approach to the use of real images to address this issue. In this study, synthetic images were generated with various strategies using plant instances clipped from UAV-borne real images. In addition, the Generative Adversarial Networks (GAN) technique was used to generate fake plant instances which were used in generating synthetic images. These images were used to train a powerful convolutional neural network (CNN) known as "Mask R-CNN" for weed detection and segmentation in a transfer learning mode. The study was conducted on morningglories (MG) and grass weeds (Grass) infested in cotton. The biomass for individual weeds was also collected in the field for biomass modeling using detection and segmentation results derived from model inference. Results showed a comparable performance between the real plant-based synthetic image (mean average precision for mask-mAPm: 0.60; mean average precision for bounding box-mAPb: 0.64) and real image datasets (mAPm: 0.80; mAPb: 0.81). However, the mixed dataset (real image  + real plant instance-based synthetic image dataset) resulted in no performance gain for segmentation mask whereas a very small performance gain for bounding box (mAPm: 0.80; mAPb: 0.83). Around 40-50 plant instances were sufficient for generating synthetic images that resulted in optimal performance. Row orientation of cotton in the synthetic images was beneficial compared to random-orientation. Synthetic images generated with automatically-clipped plant instances performed similarly to the ones generated with manually-clipped instances. Generative Adversarial Networks-derived fake plant instances-based synthetic images did not perform as effectively as real plant instance-based synthetic images. The canopy mask area predicted weed biomass better than bounding box area with R2 values of 0.66 and 0.46 for MG and Grass, respectively. The findings of this study offer valuable insights for guiding future endeavors oriented towards using synthetic images for weed detection and segmentation, and biomass estimation in row crops.

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Occurrence of alfalfa (Medicago sativa L.) populations along roadsides in southern Manitoba, Canada and their potential role in intraspecific gene flow.

Alfalfa is a highly outcrossing perennial species that can be noticed in roadsides as feral populations. There remains little information available on the extent of feral alfalfa populations in western Canadian prairies and their role in gene flow. The main objectives of this study were (a) to document the occurrence of feral alfalfa populations, and (b) to estimate the levels of outcrossing facilitated by feral populations. A roadside survey confirmed widespread occurrence of feral alfalfa populations, particularly in alfalfa growing regions. The feral populations were dynamic and their frequency ranged from 0.2 to 1.7 populations km(-1). In many cases, the nearest feral alfalfa population from alfalfa production field was located within a distance sufficient for outcrossing in alfalfa. The gene flow study confirmed that genes can move back and forth between feral and cultivated alfalfa populations. In this study, the estimated outcrossing levels were 62% (seed fields to feral), 78% (feral to seed fields), 82% (hay fields to feral) and 85% (feral to feral). Overall, the results show that feral alfalfa plants are prevalent in alfalfa producing regions in western Canada and they can serve as bridges for gene flow at landscape level. Management of feral populations should be considered, if gene flow is a concern. Emphasis on preventing seed spill/escapes and intentional roadside planting of alfalfa cultivars will be particularly helpful. Further, realistic and pragmatic threshold levels should be established for markets sensitive to the presence of GE traits.

Seed traits correlate with herbicide resistance in Italian ryegrass (Lolium perenne ssp. multiflorum).

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 (< 40%). Percent survival status to multiple postemergence herbicides was positively correlated with 100-seed weight and fresh or initial seed dormancy. CONCLUSION: Early emerging cohorts are easily controlled by pre-plant tillage and preemergence herbicides, whereas late emerging cohorts (facilitated by seed dormancy) are exposed to postemergence herbicides wherein greater opportunities exist for resistance evolution, likely explaining the occurrence of high seed dormancy in Italian ryegrass populations resistant to postemergence herbicides. High seed weights can further allow seedling emergence from greater burial depth, thereby exposing more seedlings to postemergence herbicides and increasing the likelihood of resistance evolution. Results provide unique insights into the association between seed traits and herbicide resistance in this species. © 2021 Society of Chemical Industry.

Recurrent Selection with Sub-Lethal Doses of Mesotrione Reduces Sensitivity in Amaranthus palmeri.

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.