Intensive cropping influences the success of seed dormancy breaking methods in Australian collected Hordeum, Avena, and Bromus sp.

Background: Seed dormancy is a critical evolutionary trait that enhances the persistence of plant populations under both natural and managed conditions. It is influenced by genetic and environmental factors, with crop management practices like tillage and herbicide use reportedly selecting for increased seed dormancy in weeds. This study aimed to compare the success of seed dormancy breaking methods between weed populations collected from intensively managed crop fields and unmanaged ruderal locations.

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

Palmer amaranth () 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.

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 () to investigate the interplay of climate, biotic interactions (i.e.

Rate of crop-weed hybridization in × 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 () is an important crop capable of interspecific hybridization with its weedy relative johnsongrass (. Previous findings have shown that triploid progenies resulting from  ×  crosses typically collapse with only a few developing into mature seeds, whereas tetraploids often fully develop.

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.

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.

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).

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).

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.

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.

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.

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.

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.

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.

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

, 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 and other difficult-to-control weed species. rapidly developed resistance to 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors such as mesotrione.

Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth ().

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations.

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.

Cross and multiple herbicide resistance in annual bluegrass (Poa annua) populations from eastern Texas golf courses.

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).

Current outlook and future research needs for harvest weed seed control in North American cropping systems.

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.

Late-season surveys to document seed rain potential of Palmer amaranth (Amaranthus palmeri) and waterhemp (Amaranthus tuberculatus) in Texas cotton.

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.

First case of glyphosate resistance in weedy sunflower (Helianthus annuus).

Background: Weedy sunflower (Helianthus annuus L.) is a troublesome weed in row-crop production fields in South Texas. Populations with suspected resistance to glyphosate were evaluated with 1X and 4X rates (X = 868 g ae ha ) of the herbicide, followed by a dose-response assay of the most resistant population.