Weed Management In Agriculture Research Articles

Characterization of the Phytotoxic Potential of Seven Copaifera spp. Essential Oils: Analyzing Active Compounds through Gas Chromatography-Mass Spectrometry Molecular Networking.

In recent years, there has been a need for environmentally friendly compounds for weed management in agriculture. This study is aimed to assess the phytotoxic constituents of oils obtained from oleoresins of seven Copaifera species (known as copaiba oils). Copaiba oils were separated from the resins by hydro-distillation, and the distillates were analyzed using gas chromatography-mass spectrometry (GC-MS) to characterize their chemical compositions. Multivariate analyses and molecular networking of GC-MS data were conducted to discern patterns in the chemical composition and phytotoxic activity of the oils, with the aim of identifying key compounds associated with phytotoxic activity. Seed germination bioassay revealed strong or complete germination inhibition against the monocot, Agrostis stolonifera but not the dicot Lactuca sativa. GC-MS analysis showed variations in composition among Copaifera species with some common compounds identified across multiple species. Caryophyllene oxide and junenol were associated with the observed phytotoxic effects. Automated flash chromatography was used to isolate the major compounds of the oils. Isolated compounds exhibited differing levels of phytotoxicity compared to the oils, suggesting the importance of interactions or synergism among oil components. These findings highlight the potential of copaiba oils as natural herbicidal agents and underscore the importance of considering species-specific responses in weed management strategies.

New segmentation approach for effective weed management in agriculture

Accurate weed detection in agricultural images is a crucial challenge for improving crop management practices and reducing chemical usage. In this study, we propose an innovative segmentation model called DWUNet, inspired by popular architectures and incorporating the latest advances in the state of the art. Our model delivers remarkable accuracy, with a Jaccard index reaching 0.825, while ensuring fast inference speed of only 8 ms per image, thus providing an optimal solution for real-time applications. By comparing DWUNet to several state-of-the-art models, we demonstrate its superiority in terms of accuracy and efficiency. Furthermore, a qualitative analysis of the visual results confirms DWUNet's ability to accurately detect weeds and generalize results beyond the training data. This study represents a significant advancement in the field of precision agriculture, providing a powerful tool for sustainable crop management and reducing environmental impact.

Allelopathic potential of Oldenlandia corymbosa and identification of its allelopathic substances

The use of allelopathy developed as an alternative practice for weed management in agriculture. Oldenlandia corymbosa is an annual herb and notable for being applied in folk medicine. Its diverse range of pharmacological properties and several bioactive substances have been well reported. Nevertheless, the allelopathic potential of O. corymbosa has not yet been documented. Hence, we aim to explore the allelopathy of O. corymbosa for weed control purposes. The extracts of O. corymbosa were tested to determine their effects on the seedling growth of six tested species. Significant reductions in the growth of seedling were exhibited as the plant extract concentrations increased. The plant extracts of O. corymbosa were separated in several chromatography steps; two active substances isolated and characterized as hedyotiscone A and B. Hedyotiscone A and B significantly restricted the growth of cress seedling at concentrations higher than 0.3 mM. The I 50 values of hedyotiscone A and B for the cress seedlings ranged from 0.023 to 0.131 mM and 0.167 to 0.810 mM, respectively. The inhibitory activity against cress seedlings indicates that hedyotiscone A and B contribute to the allelopathic effects and may be responsible for the allelopathic potential of O. corymbosa.

Evaluation of the Allelopathic Activity of Aqueous and Methanol Extracts of Heliotropium indicum Leaves and Roots on Eight Cucurbit Crops

Allelopathy, a biological phenomenon where plants release chemicals that influence the growth and development of neighboring flora, offers potential natural alternatives for weed management in agriculture. This study investigated the allelopathic effects of Heliotropium indicum leaf and root extracts on the germination, growth, and biochemical parameters of eight cucurbit crops. Results demonstrated that H. indicum extracts generally inhibited seed germination across all cucurbit species, with aqueous extracts showing a stronger effect than methanol extracts in Lagenaria siceraria at 10.66 ± 0.46% (p < 0.001). The treatment also variably affected shoot and root growth, indicating both inhibitory and stimulatory actions, depending on the crop species and extract type. For instance, in methanol extract treatments, Benincasa hispida’s shoot length was significantly reduced, compared to the control, at 2.1 ± 0.14 cm (p < 0.001). Notably, aqueous leaf extracts enhanced chlorophyll content more effectively than root extracts, suggesting a potential application as a biostimulant, to improve photosynthetic efficiency in certain species, for example, Momordica charantia (p < 0.05). The study further explored the antioxidative potential of the extracts by measuring MDA levels, a biomarker for oxidative stress. MDA levels increased significantly across all species in response to both types of extracts, indicating oxidative stress (L. siceraria p < 0.05). These findings suggest the potential of H. indicum extracts as natural herbicides and biostimulants, though their effects are species-specific and dependent on concentration. This research contributes to understanding the complex interactions in plant allelopathy, and highlights the potential of plant-derived extracts in sustainable agriculture.

Weed Remover Using Machine Learning

Weed Remover using Machine Learning is an innovative solution using machine learning techniques to address the persistent challenge of weed management in agriculture. The system integrates advanced machine learning algorithms with robotics to accurately find and cut weeds in real-time. A convolutional neural network (CNN) is trained on a diverse dataset comprising images of various crops and weed species, allowing the model to develop a robust understanding of visual cues associated with both. This trained model is deployed on a robotic platform equipped with cameras and actuators for real-time decision-making and precise weed removal

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.

Genetic Diversity of Weedy Rice and Its Potential Application as a Novel Source of Disease Resistance.

Weeds that infest crops are a primary factor limiting agricultural productivity worldwide. Weedy rice, also called red rice, has experienced independent evolutionary events through gene flow from wild rice relatives and de-domestication from cultivated rice. Each evolutionary event supplied/equipped weedy rice with competitive abilities that allowed it to thrive with cultivated rice and severely reduce yields in rice fields. Understanding how competitiveness evolves is important not only for noxious agricultural weed management but also for the transfer of weedy rice traits to cultivated rice. Molecular studies of weedy rice using simple sequence repeat (SSR), restriction fragment length polymorphism (RFLP), and whole-genome sequence have shown great genetic variations in weedy rice populations globally. These variations are evident both at the whole-genome and at the single-allele level, including Sh4 (shattering), Hd1 (heading and flowering), and Rc (pericarp pigmentation). The goal of this review is to describe the genetic diversity of current weedy rice germplasm and the significance of weedy rice germplasm as a novel source of disease resistance. Understanding these variations, especially at an allelic level, is also crucial as individual loci that control important traits can be of great target to rice breeders.

Herbicide Physiology and Environmental Fate

Herbicides are crucial tools for weed management in agriculture [...]

A LuALS Mutation with High Sulfonylurea Herbicide Resistance in Linum usitatissimum L.

The cultivation of herbicide-resistant crops is an effective tool for weed management in agriculture. Weed control in flax (Linum usitatissimum L.) remains challenging due to the lack of available herbicide-resistant cultivars. In this study, a mutant resistant to acetolactate synthase (ALS)-inhibiting herbicides was obtained by ethyl methanesulphonate (EMS) mutagenesis using an elite cultivar, Longya10. Whole-plant dose-response assays revealed that, compared to Longya10, the mutant was 11.57-fold more resistant to tribenuron-methyl (TBM) and slightly resistant to imazethapyr (resistance index (mutant/Longya10) < 3). In vitro acetolactate synthase assays showed that the relative resistance of the mutant was 12.63 times more than that of Longya10. A biochemical analysis indicated that there was a Pro197Ser (relative to the Arabidopsis thaliana ALS sequence) substitution within the LuALS1, conferring high resistance to sulfonylurea herbicides in the mutant. Additionally, two cleaved amplified polymorphic sequence (CAPS) markers, BsaI-LuALS1 and EcoO109I-LuALS1, were developed based on the mutation site for marker assistant selection in breeding. Moreover, the mutant did not cause losses in natural field conditions. We find a mutant with ALS-inhibiting herbicide resistance chemically induced by EMS mutagenesis, providing a valuable germplasm for breeding herbicide-resistant flax varieties.

Effects of Piper betle L. Extract and Allelochemical Eugenol on Rice and Associated Weeds Germination and Seedling Growth

Natural herbicide is considered as a sustainable approach for weed management in agriculture. Here, allelopathic activities of Piper betle L. extract (BE) and known allelochemical eugenol (EU) were studied against rice and associated weeds in terms of germination and seedling growth. Five plant species including a rice crop (Oryza sativa L.); a dicot weed, false daisy (Eclipta prostrata (L.) L.); and three monocot weeds, barnyard grass (Echinochloa crus-galli (L.) P. Beauv.), swollen fingergrass (Chloris barbata Sw.), and weedy rice (Oryza sativa f. spontanea Roshev.) were studied. The paper-based results demonstrated that BE and EU had inhibitory effects on seed germination and seedling growth. The IC50 values of BE and EU for seed germination were ranked from swollen fingergrass, to false daisy, barnyard grass, rice, and weedy rice, respectively. The ratio of root to shoot length of the seedlings indicated that the roots were more affected by the treatments than the shoots. In addition, the gel-based results showed the reduction of the rice seedling root system, especially on lateral root length and the numbers upon the treatments. Taken together, BE had an allelopathic activity similar to that of EU. Interestingly, the major paddy weed, barnyard grass, was more sensitive to BE than rice, underlining BE as a natural herbicide in rice agriculture.

The Foliar Fungal Pathogenic Metabolites as Promising Alternatives to Chemical Herbicides: Recent Developments, Future Perspective and Commercialization

Weeds harbour wide variety of microorganisms having beneficial, neutral and phytopathogenic effects. Weed microbiome discoveries could fuel progress in sustainable agriculture, such as the development of microbial herbicide products. Weed infecting phytopathogenic living fungal cells (mycelia or spores) and their natural products have been studied as producers of mycoherbicides. The application of biological and biochemical (natural or biorational) herbicides based on specific weed pathogens and natural products, respectively, is believed to assist the decreasing harmful impact of the chemicals. Cell free broth of several plant pathogenic fungi have been enthusiastically investigated for substitutes of synthetic agrochemicals against weeds. However, all such studies conducted on pure compound with high purity which have limitations due to high costs. It was found that herbicide in cell-free culture broth of fungi were largely composed of various nature of different metabolites with the ratio varying with culture time. Crude broth in a form of cell-free culture broth showed high herbicidal activity against weeds. So, cell-free culture broth as a crude product could be serve as a potential cost-effective and environmental-friendly herbicide in agriculture. The application of mycometabolites in agricultural weed management are safer to the user and the environment. They were formulated and applied in the same manner as chemical herbicides. This review aims at summarizing the studies on the application of mycometabolites as a lucrative, novel source of secondary herbicidal compounds for management of weeds. More effort should be expended in this area of research in the future, despite the obstacles that exist.

Biotechnological Road Map for Innovative Weed Management.

In most agriculture farmlands, weed management is predominantly reliant on integrated weed management (IWM) strategies, such as herbicide application. However, the overuse and misuse of herbicides, coupled with the lack of novel active ingredients, has resulted in the uptrend of herbicide-resistant weeds globally. Moreover, weedy traits that contribute to weed seed bank persistence further exacerbate the challenges in weed management. Despite ongoing efforts in identifying and improving current weed management processes, the pressing need for novel control techniques in agricultural weed management should not be overlooked. The advent of CRISPR/Cas9 gene-editing systems, coupled with the recent advances in “omics” and cheaper sequencing technologies, has brought into focus the potential of managing weeds in farmlands through direct genetic control approaches, but could be achieved stably or transiently. These approaches encompass a range of technologies that could potentially manipulate expression of key genes in weeds to reduce its fitness and competitiveness, or, by altering the crop to improve its competitiveness or herbicide tolerance. The push for reducing or circumventing the use of chemicals in farmlands has provided an added incentive to develop practical and feasible molecular approaches for weed management, although there are significant technical, practical, and regulatory challenges for utilizing these prospective molecular technologies in weed management.

Advances in site‐specific weed management in agriculture—A review

AbstractThe developments of information and automation technologies have opened a new era for weed management to fit physical and chemical control treatments to the spatial and temporal heterogeneity of weed distributions in agricultural fields. This review describes the technologies of site‐specific weed management (SSWM) systems, evaluates their ecological and economic benefits and gives a perspective for the implementation in practical farming. Sensor technologies including 3D cameras, multispectral imaging and Artificial Intelligence (AI) for weed classification and computer‐based decision algorithms are described in combination with precise spraying and hoeing operations. Those treatments are targeted for patches of weeds or individual weed plants. Cameras can also guide inter‐row hoes precisely in the centre between two crop rows at much higher driving speed. Camera‐guided hoeing increased selectivity and weed control efficacy compared with manual steered hoeing. Robots combine those guiding systems with in‐row hoeing or spot spraying systems that can selectively control individual weeds within crop rows. Results with patch spraying show at least 50% saving of herbicides in various crops without causing additional costs for weed control in the following years. A challenge with these technologies is the interoperability of sensing and controllers. Most of the current SSWM technologies use their own IT protocols that do not allow connecting different sensors and implements. Plug &amp; play standards for linking detection, decision making and weeding would improve the adoption of new SSWM technologies and reduce operational costs. An important impact of SSWM is the potential contribution to the EU‐Green Deal targets to reduce pesticide use and increase biodiversity. However, further on‐farm research is needed for integrating those technologies into agricultural practice.

Plant growth ability, rather than phylogenetic relatedness, predicts the effect of soil biota from an abandoned field on native and exotic plants

Abstract Soil biota, as legacy effects of previous species in natural ecosystems, profoundly affects plant performance in new habitats and, in turn, plant community. However, how soil biota, as legacy effects of agricultural crops, affects the likelihood of establishment of exotic and native plants in newly abandoned farmland remains poorly understood, which may hinder effective management of agricultural weeds. Here, we grew 58 plant species (28 exotic species and 30 native species) common in Central China in sterilized vs. nonsterilized soils collected from a newly abandoned maize field. We (i) estimated the effects of soil biota on plant shoot, root and total mass, (ii) explored the dependence of soil effects on the plants’ phylogenetic distance to maize, origin (native vs. exotic) and life history (annual vs. perennial) and (iii) tested which plant traits could predict soil effects. Soil biota, in general, decreased plant mass, suggesting a dominant role of enemies. The effect of the soil biota on plant total mass was unrelated to the phylogenetic distance of the plants to maize and decreased linearly with increasing plant intrinsic growth ability. Moreover, the soil biota on average had greater negative impacts on the total mass of exotic plants, particularly perennial species, than on that of native plants. Our results suggest that plant intrinsic growth ability, rather than phylogenetic relatedness, is a reliable predictor of soil effects. Additionally, native plants can benefit from plant–soil interactions in competing with exotic perennial plants when initially established in newly abandoned farmlands.

Plant community responses to integrating livestock into a reduced‐till organic cropping system

AbstractThe problems with herbicide‐ and tillage‐based weed management in agriculture are well documented and have precipitated research into finding alternatives. Integrating livestock grazing into organic agroecosystems has benefits and is a viable method for terminating cover crops, yet its impacts on weed communities are largely unknown. This lack of knowledge is particularly true in semi‐arid environments, including the Northern Great Plains, where we conducted our research. We compared weed community responses (biomass, species richness, Simpson’s diversity, composition) of a sheep‐grazed organic cropping system with those of two contrasting cropping systems (high input conventional no‐till, tilled organic) across a five‐year crop rotation (safflower, sweet clover, winter wheat, lentils, winter wheat). We found that the conventional no‐till and tilled organic systems suppressed weed biomass and reduced species richness and diversity, while the grazed organic resulted in higher weed biomass, species richness, and diversity. During the first two years of the study, the composition of the two organic communities were distinct from the conventional no‐till communities but were indistinguishable from one another. Over the final three years of the study, grazed organic communities were tightly grouped and became distinct from both the tilled and conventional communities. We found that weed biomass and diversity were highest in the sweet clover and lowest in the winter wheat. The spring annual crops, safflower and lentil, demonstrated similar weed biomass, species richness, and composition. Our findings indicate that integrating livestock into cropping systems alters plant communities and increases the agroecosystem plant biodiversity of semi‐arid organic farming and that specific crops interact with cropping systems to alter agroecosystem plant communities. However, the increase in weed biomass associated with our grazing treatment makes this approach impractical as the sole weed management strategy and necessitates that integrating livestock into semi‐arid organic cropping systems must be part of a larger integrated weed management program.

Trp548Met mutation of acetolactate synthase in rice confers resistance to a broad spectrum of ALS-inhibiting herbicides

Herbicide resistance in crop plants is valuable for integrated weed management in agriculture. Herbicide resistant rice, in particular, is important to management of weedy rice, a close relative of cultivated rice and a noxious weed prevalent in rice fields that remains challenging to farmers worldwide. Herbicide resistant plants can be obtained through transgenic approach or by mutagenesis of regular plant and screening of mutants with elevated resistance to herbicide. In this study, we conducted ethyl methyl sulfonate mutagenesis (EMS) to elite indica cultivar Huanghuazhan (HHZ) and screened for mutants resistant to imazapic, a herbicide that can inhibit the acetolactate synthase (ALS) in plants. We obtained three mutants of OsALS gene that have not been reported previously in rice. One of the mutants, with Trp548 changed to Met (W548M), was analyzed in more details in this study. This mutation had no negative effect on the plant physiology and morphology as well as rice yield. Compared with the imidazolinone-resistant mutant S627N (Ser627 changed to Asn) that has been deployed for Clearfield rice development, W548M mutant showed high levels of resistance to a broad spectrum of five families of ALS-inhibiting herbicides, in addition to a higher level of resistance to herbicides of the imidazolinone family. The herbicide-resistance was stably inherited by crossing into other rice lines. Thus, the W548M mutation provides a valuable resource for breeding of herbicide resistant rice and weed management.

Allelopathy of Cover Crops on the Germination and Initial Development of Euphorbia heterophylla

Cover crop is a important way for weed management in agriculture. The objective of this study was to investigate the effects of aqueous extracts of the aerial part of Avena strigosa, Cichorium intybus, Chenopodium quinoa and Fagopyrum esculentum in different concentrations on the germination and development of Euphorbia heterophylla. The experimental design was completely randomized, in factorial 4 &amp;times; 5 with four replicates. Factor A-aqueous extracts of the aerial part of the cover plants and factor B-concentrations of 0%; 1%, 2.5%, 5% and 10%. A total of 25 E. heterophylla seeds, arranged in gerboxes, were used and 15 ml of the extracts were added according to the treatments. The germination test was performed in a growth chamber (BOD) with photoperiod of 12/12 light/dark hours and constant temperature of 25&amp;plusmn;1 &amp;deg;C. The data were submitted to analysis of variance by the F test, a comparison was made between means and the concentration factor of the extracts and the regression analysis. The C. quinoa aqueous extracts (10%) presented greater allelopathic potential than the other extracts in the variables tested for E. heterophylla. C. intybus presented allelophatic potential with results superior to A. strigosa and F. esculentum. The C. quinoa and C. intybus extracts have allelopathic potential when compared to the control with the highest dose (10%), interfering mainly in the root portion of E. heterophylla.

Potential Allelopathic Candidates for Land Use and Possible Sustainable Weed Management in South Asian Ecosystem

Weed management is one of the significant challenges of field crops since weeds pose a remarkable threat to crop productivity in South Asian countries, including Bangladesh. Allelopathy, a phenomenon whereby secondary metabolites produced and released by one plant species influence the growth and development of other species can be exploited in sustainable management. The focus of this study was to evaluate potential allelopathic plant species which can be further explored as alternatives to synthetic herbicides or incorporated as part of integrated weed management in sustainable agriculture. Two hundred fifty-two plant samples from 70 families were collected from Bangladesh and evaluated with the sandwich bioassay. Thirty-one percent of the samples showed significant allelopathic potential on lettuce radicle elongation. Among the species that showed substantial inhibition, more than 7% of the samples showed higher inhibition (HI) and 25% showed moderate inhibition (MI) on lettuce radicle. Fruit pulps of Couroupita guianensis (95.4%), fruits of Phyllanthus emblica (95.4%), and Acacia concinna (95.4%) showed the highest inhibition on lettuce radicle elongation. In contrast, the leaf of Bombax insigne had growth promoting activity by stimulating radicle (23%) and hypocotyl (80%) elongation of lettuce seedlings. This result suggested that the species with significant plant growth inhibitory potential may play a vital role as an alternative to the increasing use of synthetic herbicides for sustainable weed management in agricultural land.

Gene drive systems: do they have a place in agricultural weed management?

There is a pressing need for novel control techniques in agricultural weed management. Direct genetic control of agricultural pests encompasses a range of techniques to introduce and spread novel, fitness‐reducing genetic modifications through pest populations. Recently, the development of CRISPR–Cas9 gene editing has brought these approaches into sharper focus. Proof of concept for CRISPR–Cas9‐based gene drives has been demonstrated for the control of disease‐vectoring insects. This article considers whether and how gene drives may be applied in agricultural weed management, focusing on CRISPR–Cas9‐based systems. Population‐suppression drives might be employed to introduce and proliferate deleterious mutations that directly impact fitness and weediness, whereas population‐sensitizing drives would seek to edit weed genomes so that populations are rendered more sensitive to subsequent management interventions. Technical challenges relating to plant transformation and gene editing in planta are considered, and the implementation of gene drives for timely and sustainable weed management is reviewed in the light of weed population biology. The technical, biological, practical and regulatory challenges remain significant. Modelling‐based studies can inform how and if gene drives could be employed in weed populations. These studies are an essential first step towards determining the utility of gene drives for weed management. © 2018 The Author. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes.