Optimizing herbicide selection for pre-emergence control of itchgrass and cypressvine morningglory in sugarcane.

The aim of this study was to assess the efficacy of herbicides in association to control Rottboellia exaltata and Ipomoea quamoclit during pre-emergence while also to evaluate the potential impact on the sugarcane. The experimental design employed a randomized block with seven treatments and four replications. The treatments were: 1 - no herbicide application; 2 - indaziflam + sulfentrazone; 3 - indaziflam + diclosulam; 4 - indaziflam + tebuthiuron; 5 - flumioxazin + diclosulam, 6 - flumioxazin + pyroxasulfone and 7 - clomazone + sulfentrazone. The evaluated parameters were: percentage of weeds control, green coverage percentage (Canopeo® system), weed biomass (g m-2), itchgrass height, and sugarcane tiller. Several herbicide associations have been proven effective alternatives for managing itchgrass and cypressvine morningglory. The most successful treatments for itchgrass control were indaziflam + tebuthiuron (100%) and indaziflam + diclosulam (97%), whereas for cypressvine morningglory, the betters were indaziflam + sulfentrazone (97%), indaziflam + diclosulam (98%), indaziflam + tebuthiuron (97%), flumioxazin + diclosulam (94%), and clomazone + sulfentrazone (96%). All treatments reduced the weed biomass, with indaziflam + tebuthiuron being the safest option for protecting sugarcane.

Laser weeding: opportunities and challenges for couch grass (Elymus repens (L.) Gould) control.

Laser weeding may contribute to less dependency on herbicides and soil tillage. Several research and commercial projects are underway to develop robots equipped with lasers to control weeds. Artificial intelligence can be used to locate and identify weed plants, and mirrors can be used to direct a laser beam towards the target to kill it with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a tiny part of the field for treatment. Laser weeding leaves behind only ashes from the burned plants and does not disturb the soil. Therefore, it is an eco-friendly method to control weed seedlings. However, perennial weeds regrow from the belowground parts after the laser destroys the aerial shoots. Depletion of the belowground parts for resources might be possible if the laser continuously kills new shoots, but it may require many laser treatments. We studied how laser could be used to destroy the widespread and aggressive perennial weed Elymus repens after the rhizomes were cut into fragments. Plants were killed with even small dosages of laser energy and stopped regrowing. Generally, the highest efficacy was achieved when the plants from small rhizomes were treated at the 3-leaf stage.

Ecological weed management and square planting influenced the weed management, and crop productivity in direct-seeded rice.

Herbicide use may pose a risk of environmental pollution or evolution of resistant weeds. As a result, an experiment was carried out to assess the influence of different non-chemical weed management tactics (one hoeing (HH) at 12 DAS followed by (fb) one hand weeding at 30 DAS, one HH at 12 DAS fb Sesbania co-culture and its mulching, one HH at 12 DAS fb rice straw mulching @ 4t ha-1, one HH at 12 DAS fb rice straw mulching @ 6 t ha-1) on weed control, crop growth and yield, and economic returns in direct-seeded rice (DSR). Experiment was conducted during kharif season in a split-plot design and replicated thrice. Zero-till seed drill-sown crop (PN) had the lowest weed density at 25 days after sowing (DAS), while square planting geometry (PS) had the lowest weed density at 60 DAS. PS also resulted in a lower weed management index (WMI), agronomic management index (AMI), and integrated weed management index (IWMI), as well as higher growth attributes, grain yield (4.19 t ha-1), and net return (620.98 US$ ha-1). The cultivar Arize 6444 significantly reduced weed density and recorded higher growth attributes, yield, and economic return. In the case of weed management treatments, one HH at 12 DAS fb Sesbania co-culture and its mulching had the lowest weed density, Shannon-weinner index and eveness at 25 DAS. However, one hoeing at 12 DAS fb one hand weeding at 30 DAS (HH + WH) achieved the highest grain yield (4.85 t ha-1) and net returns (851.03 US$ ha-1) as well as the lowest weed density at 60 DAS. PS × HH + WH treatment combination had the lowest weed persistent index (WPI), WMI, AMI, and IWMI, and the highest growth attributes, production efficiency, and economic return.

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.

Trade-offs constrain the success of glyphosate-free farming.

Glyphosate, the most widely used herbicide, is linked with environmental harm and there is a drive to replace it in agricultural systems. We model the impacts of discontinuing glyphosate use and replacing it with cultural control methods. We simulate winter wheat arable systems reliant on glyphosate and typical in northwest Europe. Removing glyphosate was projected to increase weed abundance, herbicide risk to the environment, and arable plant diversity and decrease food production. Weed communities with evolved resistance to non-glyphosate herbicides were not projected to be disproportionately affected by removing glyphosate, despite the lack of alternative herbicidal control options. Crop rotations with more spring cereals or grass leys for weed control increased arable plant diversity. Stale seedbed techniques such as delayed drilling and choosing ploughing instead of minimum tillage had varying effects on weed abundance, food production, and profitability. Ploughing was the most effective alternative to glyphosate for long-term weed control while maintaining production and profit. Our findings emphasize the need for careful consideration of trade-offs arising in scenarios where glyphosate is removed. Integrated Weed Management (IWM) with more use of cultural control methods offers the potential to reduce chemical use but is sensitive to seasonal variability and can incur negative environmental and economic impacts.

Isolation and chemical characterization of lignocellulosic fiber from Pueraria montana using Box-Behnken design for weed management.

The huge demand for natural fibers necessitates the search for non-traditional bioresources including invasive species which are deteriorating the ecosystem and biodiversity. The study aims to utilize Pueraria montana weed for the extraction of lignocellulosic fiber using both traditional (water retting) and chemical extraction methods to determine the better extraction method. Chemically extracted fiber showed 17.09 g/tex bundle strength whereas water-extracted fiber showed 11.7 g/tex bundle strength. Therefore, chemical extraction method was chosen for fiber isolation by optimization of reaction conditions using Box Behnken Design. Based on the design, optimal conditions obtained were 1 % w/v NaOH, 0.75 % v/v H2O2, and 3 days retting time. Solid-state NMR illustrated the breakdown of hemicellulose linkages at 25.89 ppm. FTIR revealed the disappearance of C=O groups of hemicellulose at 1742 cm-1. TGA demonstrated thermal stability of chemically treated fiber up to 220 °C and activation energy of 60.122 KJ/mol. XRD evidenced that chemically extracted fiber has a crystallinity index of 71.1 % and a crystal size of 2 nm. Thus P. montana weed holds potential for the isolation of natural fiber as its chemical composition and properties are comparable to commercial lignocellulosic fibers. The study exemplifies the transformation of weed to a bioresource of natural fibers.

Acting pre-emptively reduces the long-term costs of managing herbicide resistance.

Globally, pesticides improve crop yields but at great environmental cost, and their overuse has caused resistance. This incurs large financial and production losses but, despite this, very diversified farm management that might delay or prevent resistance is uncommon in intensive farming. We asked farmers to design more diversified cropping strategies aimed at controlling herbicide resistance, and estimated resulting weed densities, profits, and yields compared to prevailing practice. Where resistance is low, it is financially viable to diversify pre-emptively; however, once resistance is high, there are financial and production disincentives to adopting diverse rotations. It is therefore as important to manage resistance before it becomes widespread as it is to control it once present. The diverse rotations targeting high resistance used increased herbicide application frequency and volume, contributing to these rotations' lack of financial viability, and raising concerns about glyphosate resistance. Governments should encourage adoption of diverse rotations in areas without resistance. Where resistance is present, governments may wish to incentivise crop diversification despite the drop in wheat production as it is likely to bring environmental co-benefits. Our research suggests we need long-term, proactive, food security planning and more integrated policy-making across farming, environment, and health arenas.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed-detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k-fold cross-validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable-rate herbicide applications. The proposed method can be employed in a machine vision-based autonomous site-specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

Controlled release herbicide formulation for effective weed control efficacy.

Controlled release formulation (CRF) of herbicide is an effective weed management technique with less eco-toxicity than other available commercial formulations. To maximise the effectiveness of CRFs however, it is crucial to understand the herbicide-releasing behaviour at play, which predominately depends on the interaction mechanisms between active ingredients and carrier materials during adsorption. In this study, we investigated and modelled the adsorption characteristics of model herbicide 2,4-D onto two organo-montmorillonites (octadecylamine- and aminopropyltriethoxysilane-modified) to synthesise polymer-based CRFs. Herbicide-releasing behaviour of the synthesised CRF microbeads was then analysed under various experimental conditions, and weed control efficacy determined under glasshouse conditions. Results revealed that adsorption of 2,4-D onto both organo-montmorillonites follows the pseudo-second-order kinetics model and is predominately controlled by the chemisorption process. However, multi-step mechanisms were detected in the adsorption on both organoclays, hence intra-particle diffusion is not the sole rate-limiting step for the adsorption process. Both organoclays followed the Elovich model, suggesting they have energetically heterogeneous surfaces. Herbicide-releasing behaviours of synthesised beads were investigated at various pH temperatures and ionic strengths under laboratory and glasshouse conditions. Furthermore, weed control efficacy of synthesised beads were investigated using pot studies under glasshouse condition. Desorption studies revealed that both synthesised microbeads have slow releasing behaviour at a wide range of pHs (5-9), temperatures (25-45 °C), and ionic strengths. The results also revealed that synthesised microbeads have excellent weed control efficacy on different broad-leaf weed species under glasshouse conditions.

Early silique-shedding wild radish (Raphanus raphanistrum L.) phenotypes persist in a long-term harvest weed seed control managed field in Western Australia.

BACKGROUND: This study introduces a wild radish population collected from Yelbeni in the Western Australian grainbelt that evolved an early silique abscission (shedding) trait to persist despite long-term harvest weed seed control (HWSC) use. In 2017, field-collected seed (known herein as Yelbeni) was compared to surrounding ruderal and field-collected populations in a fully randomized common garden study. RESULTS: The Yelbeni population exhibited a higher rate of silique abscission when compared to the ruderal populations collected from the site before wheat (Triticum aestivum L.) harvest (assessed at soft dough stage, Zadoks 83). A similar common garden study was conducted in the subsequent season (2018) using progeny reproduced on a single site without stress. The HWSC-selected progeny (Yelbeni P) shed 1048 (±288) siliques before wheat maturity at the soft dough stage (Zadoks 83) compared to 25 (±7) siliques from the pooled control populations. The Yelbeni P population only flowered 6 days earlier (FT50 as determined by log-logistic analysis) than pooled control populations, which is unlikely to fully account for the increased rate of silique abscission. The Yelbeni P population also located its lowest siliques below the lowest height for harvest interception (10 cm), which is likely to increase HWSC evasion. The mechanism inducing early silique-shedding is yet to be determined; however, wild radish is known for its significant genetic variability and has demonstrated its capacity to adapt to environmental and management stresses. CONCLUSION: This study demonstrates that the repeated use of HWSC can lead to the selection of HWSC-avoidance traits including early silique-shedding before harvest and/or locating siliques below the harvest cutting height for interception. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa.

BACKGROUND: Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa. RESULTS: A novel neural network, ResNet-101-segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet-101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet-101), ResNet-101-segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets. CONCLUSION: ResNet-101-segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet-101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. RESULTS: The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection. CONCLUSION: The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Weed biology and management in the multi-omics era: Progress and perspectives.

Weeds pose a significant threat to crop production, resulting in substantial yield reduction. In addition, they possess robust weedy traits that enable them to survive in extreme environments and evade human control. In recent years, the application of multi-omics biotechnologies has helped to reveal the molecular mechanisms underlying these weedy traits. In this review, we systematically describe diverse applications of multi-omics platforms for characterizing key aspects of weed biology, including the origins of weed species, weed classification, and the underlying genetic and molecular bases of important weedy traits such as crop-weed interactions, adaptability to different environments, photoperiodic flowering responses, and herbicide resistance. In addition, we discuss limitations to the application of multi-omics techniques in weed science, particularly compared with their extensive use in model plants and crops. In this regard, we provide a forward-looking perspective on the future application of multi-omics technologies to weed science research. These powerful tools hold great promise for comprehensively and efficiently unraveling the intricate molecular genetic mechanisms that underlie weedy traits. The resulting advances will facilitate the development of sustainable and highly effective weed management strategies, promoting greener practices in agriculture.

Unveiling the resistance of native weed communities: insights for managing invasive weed species in disturbed environments.

Weed communities influence the dynamics of ecosystems, particularly in disturbed environments where anthropogenic activities often result in higher pollution. Understanding the dynamics existing between native weed communities and invasive species in disturbed environments is crucial for effective management and normal ecosystem functioning. Recognising the potential resistance of native weed communities to invasion in disturbed environments can help identify suitable native plants for restoration operations. This review aims to investigate the adaptations exhibited by native and non-native weeds that may affect invasions within disturbed environments. Factors such as ecological characteristics, altered soil conditions, and adaptations of native weed communities that potentially confer a competitive advantage relative to non-native or invasive weeds in disturbed environments are analysed. Moreover, the roles of biotic interactions such as competition, mutualistic relationships, and allelopathy in shaping the invasion resistance of native weed communities are described. Emphasis is given to the consideration of the resistance of native weeds as a key factor in invasion dynamics that provides insights for conservation and restoration efforts in disturbed environments. Additionally, this review underscores the need for further research to unravel the underlying mechanisms and to devise targeted management strategies. These strategies aim to promote the resistance of native weed communities and mitigate the negative effects of invasive weed species in disturbed environments. By delving deeper into these insights, we can gain an understanding of the ecological dynamics within disturbed ecosystems and develop valuable insights for the management of invasive species, and to restore long-term ecosystem sustainability.

Semi-supervised learning methods for weed detection in turf.

BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass. RESULTS: Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated. CONCLUSION: These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.

Effects of varying tillage practices and weed control methods on the efficacy of infiltration models.

Agricultural land preparation and weed control techniques are essential farm management tools that affect the dynamics of soil water infiltration and the estimation accuracy of infiltration models. To analyse the interaction effect of tillage and weed control methods on the changes in soil physical properties and the efficacy of infiltration models, an experiment was conducted on a sandy clay loam forest ochrosol at Hodzo near Ho in Ghana. Four tillage systems (No Tillage [NT], Reduced Tillage [RT], Plough + Harrow + Ridging [PHR], and Deep Tillage + Plough + Harrow + Ridging [DPHR]) and three weed control methods (Hoeing [H], Machete [MAT] and No Weeding [NW]) were employed. The study also tested the reliability of the models (Kostiakov, Philip, and Horton) using the goodness of fit statistical criteria: Root mean squared error (RMSE), Mean absolute error (MAE), Coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE). The results show that conservation tillage systems (CsT) and conventional tillage systems (CT) with MAT weeding treatments recorded the highest moisture content across the studied soil profile, especially for NT x MAT (11.189%) which was significant (p < 0.05) in the 15-30 cm layer; the lowest were observed in the CsT and CT with H weeding interactions, especially for the DPHR x H (8.086%). Comparing the interaction effect on the soil infiltration, the highest mean infiltration rate was significant (p < 0.05) under the NT X H treatment combination whilst the lowest infiltration rate was recorded in the DPHR X H and PHR X NW treatment combinations. The efficiency of the fitting models (Kostiakov > Horton > Philip) highly prioritised the soil tillage operations and weed management under the treatments DPHR x MAT > DPHR x NW > DPHR x H > RT x MAT > PHR x NW > PHR x MAT > NT x NW > RT x MAT > PHR x H > RT x H > NT x MAT > RT x NW > NT x H in that order. The trend shows that the increase in tillage intensity and the decrease in weed management intensity induce the quality of the estimation process and vice versa. The study, therefore, identified the use of machete (MAT) with DPHR under the Kostiakov model as the efficient land management for modelling the cumulative infiltration characteristics of the sandy clay loam ochrosols of the study area.

PROTAC for agriculture: learning from human medicine to generate new biotechnological weed control solutions.

Weed control has relied on the use of organic and inorganic molecules that interfere with druggable targets, especially enzymes, for almost a century. This approach, although effective, has resulted in multiple cases of herbicide resistance. Furthermore, the rate of discovery of new druggable targets that are selective and with favorable environmental profiles has slowed down, highlighting the need for innovative control tools. The arrival of the biotechnology and genomics era gave hope to many that all sorts of new control tools would be developed. However, the reality is that most efforts have been limited to the development of transgenic crops with resistance to a few existing herbicides, which in fact is just another form of selectivity. Proteolysis-targeting chimera (PROTAC) is a new technology developed to treat human diseases but that has potential for multiple applications in agriculture. This technology uses a small bait molecule linked to an E3 ligand. The 3-dimensional structure of the bait favors physical interaction with a binding site in the target protein in a manner that allows E3 recruitment, ubiquitination and then proteasome-mediated degradation. This system makes it possible to circumvent the need to find druggable targets because it can degrade structural proteins, transporters, transcription factors, and enzymes without the need to interact with the active site. PROTAC can help control herbicide-resistant weeds as well as expand the number of biochemical targets that can be used for weed control. In the present article, we provide an overview of how PROTAC works and describe the possible applications for weed control as well as the challenges that this technology might face during development and implementation for field uses. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network.

BACKGROUND: Centaurea diluta Aiton (North African knapweed) is a major weed concern in Spain as a result of the limited herbicides capable of controlling it, and the limited knowledge of its biology hinders the development of integrated weed management strategies. RESULTS: The current study presents results from two experiments that aimed to: (i) determine the effect of seed burial on seedling emergence; and (ii) model its phenology progression using sigmoidal (SRM) and artificial neural network models (ANN) based on different cohort emergence times. In the first experiment, burial at 2 cm and 5 cm decreased C. diluta emergence by 54% and 90%, respectively, compared to the emergence at 0 cm. In the second experiment, without crop-weed competition conditions, the emergence delay led to reductions in leaf number, rosette diameter, plant height and dry biomass by 63%, 50%, 59% and 93%, respectively. Seed production per plant exceeded 21 469. According to the growth model, leaf number was the most consistent morphological trait and critical for timing weed control actions, so it was used to compare SRMs and ANNs. On average, ANNs increased the precision in 5.72% (± 2.4 leaves) compared to SRMs. This slight performance of ANNs may be valuable for controlling C. diluta because control methods must be applied at the 4-leaf stage to achieve good efficacy. CONCLUSION: Seed burial at 5 cm depth is an effective method reducing C. diluta emergence. ANNs accurately predicted the leaf number employing environmental variables can help increase the efficiency of C. diluta control actions and reduce the risk of escapes. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Recent technologies for glyphosate removal from aqueous environment: A critical review.

The growing demand for food has led to an increase in the use of herbicides and pesticides over the years. One of the most widely used herbicides is glyphosate (GLY). It has been used extensively since 1974 for weed control and is currently classified by the World Health Organization (WHO) as a Group 2A substance, probably carcinogenic to humans. The industry and academia have some disagreements regarding GLY toxicity in humans and its effects on the environment. Even though this herbicide is not mentioned in the WHO water guidelines, some countries have decided to set maximum acceptable concentrations in tap water, while others have decided to ban its use in crop production completely. Researchers around the world have employed different technologies to remove or degrade GLY, mostly at the laboratory scale. Water treatment plants combine different technologies to remove it alongside other water pollutants, in some cases achieving acceptable removal efficiencies. Certainly, there are many challenges in upscaling purification technologies due to the costs and lack of factual information about their adverse effects. This review presents different technologies that have been used to remove GLY from water since 2012 to date, its detection and removal methods, challenges, and future perspectives.

Hyperspectral Analysis for Discriminating Herbicide Site of Action: A Novel Approach for Accelerating Herbicide Research.

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)-the general mechanisms affecting plant processes-has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.