Search Results (49)

Marigold (Tagetes erecta L.), a crop of significant medicinal, ornamental, and economic value, faces severe industrialization challenges due to weed-induced yield losses (up to 60%). This study aims to identify safe and highly efficient herbicides for marigold, assess their effects on dominant weeds and crop safety, and provide a practical basis for large-scale cultivation. We evaluated 11 pre-emergence herbicides, 13 post-emergence herbicides, and agronomic practices (plastic mulch) through three field trials to optimize weed control, crop safety, and productivity. In Experiment 1, pre-emergence applications of pendimethalin (35% SC) and oxyfluorfen (240 g/L EC) under plastic mulch suppressed 85–99% of grass and broad-leaved weeds, elevating marigold yield to 1655.6 kg/667 m² and increasing lutein content by 10.7% compared to controls, with no phytotoxicity to subsequent wheat (Triticum aestivum L.)or broad beans (Vicia faba L.). Experiment 2 demonstrated that post-cultivation soil treatment with metolachlor · oxyfluorfen · pendimethalin (50% EC) enhanced weed suppression (47.8–53.6%) and yield (3.4% increase) while ensuring crop safety. Experiment 3 revealed that the post-emergence herbicides haloxyfop-P-methyl (108 g/L EC) and fomesafen (250 g/L SL) achieved over 92% reduction in grass weed biomass and over 75% reduction in broadleaf weed density, respectively, alongside a 6.1% yield improvement. Therefore, region-specific strategies are recommended based on local agronomic conditions: high-value production zones should adopt integrated systems combining plastic mulch with pre-emergence herbicides; arid lands with extended crop rotation intervals require pre-emergence herbicides after intertillage and earthing-up; labor-abundant regions can rotate targeted post-emergence herbicides to delay resistance evolution. This study provides data-driven optimization strategies for comprehensive weed management in marigold fields, offering practical solutions to enhance industrial productivity and ecological sustainability. Full article

The direct-seeded rice (DSR) system is poised to become the dominant rice cultivation method due to its advantages, including reduced water usage, less labor requirements, decreased greenhouse gas emissions, and improved adaptation to climate change. However, weeds, particularly jungle rice (Echinochloa colona), significantly hinder DSR and cause substantial yield losses. This study aimed to develop rice cultivars competitive against jungle rice through selective breeding, focusing on early seed germination (ESG) and seedling vigor (ESV). We utilized 181 early-backcross selective introgression breeding lines (EB-SILs) developed using Green Super Rice (GSR) technology by backcrossing Weed Tolerant Rice1 (WTR1) with three donor parents, Haoannong, Cheng Hui 448, and Y134. Using the tunable genotyping-by-sequencing (tGBS^®, Data2Bio Technologies, Ames, IA, USA) method, we identified 3971 common single nucleotide polymorphisms (SNPs) that facilitated the mapping of 19 novel quantitative trait loci (QTLs) associated with weed competitiveness—eight linked to ESG traits and eleven to ESV traits. Notably, all QTLs were novel except qRPH1, linked to relative plant height at 14 and 21 days after sowing. Key QTLs were located on chromosomes 2, 3, 5, 6, 8, 9, 10, and 12. Candidate genes identified within these QTLs are implicated in the plant’s response to various abiotic and biotic stresses. Our findings enhance the understanding of the genetic basis for ESG and ESV traits critical for weed competitiveness, supporting marker-assisted and genomic selection approaches for breeding improved rice varieties. Furthermore, this research lays the groundwork for employing gene expression, cloning, and CRISPR editing strategies to combat jungle rice, with potential applications for other weed species and contributing to effective integrated weed management in the DSR system. Full article

German chamomile (Matricaria chamomilla L.) is not only considered a weed but also an important crop cultivated for the pharmaceutical industry. Chamomile seeds are positively photoblastic and must be sown on the soil surface. However, heavy rainfall can bury the seeds, resulting in uneven germination and partial yield loss. To address both agricultural and scientific aspects, we applied various light sources with different spectra ranging from 400 to 720 nm versus darkness, as well as smoke compounds formulated in smoke water (SW), to chamomile seeds. Our results indicate that a high proportion of red light within the spectrum is crucial for seed germination and seedling establishment. Fluorescent lamps were the least effective due to their high blue light content, highlighting the need to use white or red LEDs in experiments with chamomile seeds. The smoke compounds present in SW compensated for the absence of light, increasing seed germination by 20% in the dark, and their mode of action was additive to light, suggesting that they share the same signaling pathway. The effect of SW on seeds was stimulatory regardless of the light regime, suggesting that smoke formulations may act as a priming factor for chamomile seeds. Full article

Weeds remain one of the major limiting factors affecting agricultural production, causin significant yield loss globally. Spot spraying of resistant weeds as an alternative to broadcast applications provides the delivery of chemicals closer to the plant canopy. Also, wind speed can cause spray displacement and can lead to inefficient coverage and environmental contamination. To mitigate this issue, this study sought to evaluate drop nozzles configured to direct the spray closer to the target. A remotely piloted aerial application system was retrofitted with a 60 cm drop nozzle comprising a straight stream and a 30° full cone nozzle. A tracer spray solution was applied on 13 Kromekote cards placed in a grid configuration. The center of deposition for each spray application was determined using the Python (3.11) software. Regardless of nozzle angle, the drop nozzle produced ca. 76% lower spray displacement than the no drop nozzle. The no drop nozzles had a narrower relative span compared to the drop nozzles. This suggests that smaller, more driftable fractions of the spray did not deposit on the targets due to spray displacement. Additional research investigating in-field weed species under various meteorological conditions is required to move this technology forward. Full article

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments. Full article

Field dodder (Cuscuta campestris yuncker) is an important parasitic weed that has negative impacts on the growth and yield of alfalfa (Medicago sativa L.). Effective control of field dodder in alfalfa crops is necessary to reduce yield losses. Field studies were conducted to evaluate the efficacy of various herbicides on field dodder in a new-seeded alfalfa crop at three locations (Karaj, Jiroft, and Urmia) in Iran during 2020–2021. The herbicides evaluated were imazethapyr (100 g a.i. ha⁻¹), glyphosate (six doses ranging from 143 to 348 g a.i. ha⁻¹ + ammonium sulfate at 8 kg ha⁻¹), and propyzamide (1250, 1500, and 1750 g a.i. ha⁻¹). The results demonstrated that propyzamide at 1750 g a.i. ha⁻¹ provided complete control of field dodder in terms of percentage change in biomass at first (100%) and second (99%) cuttings of alfalfa in Urmia. The imazethapyr at 100 g a.i. ha⁻¹ provided 96 and 87% control at the first and second cuttings, respectively, whereas propyzamide at 1750 g a.i. ha⁻¹ showed the best efficacy in increasing alfalfa biomass at the first (up to 82%) and the second cuttings (up to 81%), depending on the location. Glyphosate provided reasonable control of the field dodder at the first (73%) and the second cutting (82%). However, at higher rates (307 and 348 g a.i. ha⁻¹), it caused significant reductions in alfalfa biomass due to crop injury, especially at the first cutting. The use of propyzamide at 1750 g a.i. ha⁻¹ is recommended for highly effective control of field dodder in alfalfa crops without compromising the crop yield. Full article

The phytohormones strigolactones (SLs) control root and shoot branching and are exuded from roots into the rhizosphere to stimulate interaction with mycorrhizal fungi. The exuded SLs serve as signaling molecules for the germination of parasitic plants. The broomrape Phelipanche aegyptiaca is a widespread noxious weed in various crop plants, including tomato (Solanum lycopersicum). We have isolated three mutants that impair SL functioning in the tomato variety M82: SHOOT BRANCHING 1 (sb1) and SHOOT BRANCHING 2 (sb2), which abolish SL biosynthesis, and SHOOT BRANCHING 3 (sb3), which impairs SL perception. The over-branching phenotype of the sb mutants resulted in a severe yield loss. The isogenic property of the mutations in a determinate growth variety enabled the quantitative evaluation of the contribution of SL to yield under field conditions. As expected, the mutants sb1 and sb2 were completely resistant to infection by P. aegyptiaca due to the lack of SL in the roots. In contrast, sb3 was more susceptible to P. aegyptiaca than the wild-type M82. The SL concentration in roots of the sb3 was two-fold higher than in the wild type due to the upregulation of the transcription of SL biosynthesis genes. This phenomenon suggests that the steady-state level of root SLs is regulated by a feedback mechanism that involves the SL signaling pathway. Surprisingly, grafting wild-type varieties on sb1 and sb2 rootstocks eliminated the branching phenotype and yield loss, indicating that SL synthesized in the shoots is sufficient to control shoot branching. Moreover, commercial tomato varieties grafted on sb1 were protected from P. aegyptiaca infection without significant yield loss, offering a practical solution to the broomrape crisis. Full article

Camelina is an interesting crop and producers must adopt cultural practices to achieve the highest yield and oil content possible. Considering the size of the seed, the harvesting phase is crucial to reduce losses and maximize income. Furthermore, in recent years, with the worsening of climatic conditions due to global warming, crop management can no longer follow the classic seasonality but must adapt to new climatic conditions. The possibility of double cropping, that is the process of producing two crops in a single season, allows multiple advantages such as weed control, greater remuneration, and less exposure to bare soil which determine greater resilience of the production system. To enable this, especially in recent years, even a few days of difference in the cultivation phases can guarantee the success of double cropping. For these reasons, the authors compared two different harvesting strategies: direct combining at full maturity (DC); swathing + combining at full maturity (SW). The working performance, cost, and seed losses associated with each harvesting method were calculated. The results highlighted how SW reduced the crop cycle length by 11 days, did not influence seed losses and crop yield but showed lower performance and higher cost with respect to DC. Full article

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures. Full article

Weeds are considered the main reason for crop yield loss in the world. Weed control and management include various treatments such as cultural, physical, chemical, and biological methods. Chemical control of weeds is the most common method; however, the application of commercial synthetic herbicides caused several dangerous hazards in the environment including the appearance of resistant weed biotypes. Prosopis farcta (Banks & Sol.) J.F.Macbr. (Family: Fabaceae), is a common weed plant in the Middle East, where it is hard to eliminate due to its deep and overlapped roots. On the other side, it has many traditional uses around the world. Herein, the essential oil (EO) of P. farcta above-ground parts was extracted via hydrodistillation techniques and then analyzed using gas chromatography-mass spectroscopy (GC-MS). From the GC-MS analysis, 47 compounds were identified with a relative concentration of 98.02%, including terpenes as the main components (95.08%). From overall identified compounds, cubenol (19.07%), trans-chrysanthenyl acetate (17.69%), torreyol (8.28%), davana ether (3.50%), camphor (3.35%), and farnesyl acetone (3.13%) represented the abundant constituents. Furthermore, the phytotoxic activity of the P. farcta EO was assessed against the weed Dactyloctenium aegyptium (L.) Willd. The EO of P. farcta, at a concentration of 100 µL L⁻¹, significantly inhibited the germination, seedling shoot growth, and seedling root growth by 64.1, 64.0, and 73.4%, respectively. The results exhibited that the seedling root growth is the most affected followed by the seed germination and seedling shoot growth with respective IC₅₀ at 64.5, 80.5, and 92.9 µL L⁻¹. It can be concluded that weeds are not absolutely harmful, but they may have beneficial uses, such as, for example as a source of phytochemicals with application in weed control practices (bioherbicides). It is advised to conduct additional research to characterize the allelopathic action of the major chemicals in their pure form, either alone or in combination, against a variety of weeds. Full article

Parasitic flowering plants represent a diverse group of angiosperms, ranging from exotic species with limited distribution to prominent weeds, causing significant yield losses in agricultural crops. The major damage caused by them is related to the extraction of water and nutrients from the host, thus decreasing vegetative growth, flowering, and seed production. Members of the root parasites of the Orobanchaceae family and stem parasites of the genus Cuscuta are among the most aggressive and damaging weeds, affecting both monocotyledonous and dicotyledonous crops worldwide. Their control and eradication are hampered by the extreme seed longevity and persistence in soil, as well as their taxonomic position, which makes it difficult to apply selective herbicides not damaging to the hosts. The selection of resistant cultivars is among the most promising approaches to deal with this matter, although still not widely employed due to limited knowledge of the molecular mechanisms of host resistance and inheritance. The current review aims to summarize the available information on host resistance with a focus on agriculturally important parasitic plants and to outline the future perspectives of resistant crop cultivar selection to battle the global threat of parasitic plants. Full article

One factor limiting the achievement of high yields in the soybean crop is weed interference. The level of weed interference can vary according to the specificities of the weed community but also due to the agronomic characteristics of the soybean varieties. The objective of this work was to evaluate the effects of weed interference on soybean varieties of different relative maturity groups (RMG). A field experiment was implemented in a randomized complete block design, in a 3 × 4 factorial scheme, with five replications. The treatments were composed of the varieties BMX Flecha^® (RMG 6.6), BMX Power^® (RMG 7.3), and BMX Bônus^® (RMG 7.9), associated with the following four weed managements: weeding throughout the cycle; weeded up to 20 days after emergence (DAE); weeded after 20 DAE until the end of soybean cycle; not weeded throughout the entire cycle. There was no interaction between the effects of the varieties and the weed management for emergence speed index, plant height, chlorophyll, first pod height insertion, plant population, thousand-grain weight and yield. The initial weed management caused changes in the composition of the weed community. The managements weeded throughout the cycle and weeded up until 20 DAE provided higher levels of chlorophyll and grain yield. The management without weeding during the entire cycle negatively influenced yield components. Late interventions in weed control, regardless of the soybean variety, result in yield losses. Full article

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1–5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with ‘extreme’ phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits. Full article

Abutilon theophrasti Medik. (velvetleaf) is a problematic annual weed in field crops which has invaded many temperate parts of the world. Since the loss of crop yields can be extensive, approaches to manage the weed include not only conventional methods, but also biological methods, for instance by microorganisms releasing phytotoxins and plant-derived allelochemicals. Additionally, benzoxazinoid-rich rye mulches effective in managing common weeds like Amaranthus retroflexus L. have been tested for this purpose. However, recent methods for biological control are still unreliable in terms of intensity and duration. Rye mulches were also ineffective in managing velvetleaf. In this review, we present the attempts to reduce velvetleaf infestation by biological methods and discuss possible reasons for the failure. The resilience of velvetleaf may be due to the extraordinary capacity of the plant to collect, for its own survival, the most suitable microorganisms from a given farming site, genetic and epigenetic adaptations, and a high stress memory. Such properties may have developed together with other advantageous abilities during selection by humans when the plant was used as a crop. Rewilding could be responsible for improving the microbiomes of A. theophrasti. Full article

Cuscuta campestris Yunck. is a parasitic weed responsible for severe yield losses in crops worldwide. The selective control of this weed is scarce due to the difficult application of methods that kill the parasite without negatively affecting the infected crop. trans-Cinnamic acid is secreted by plant roots naturally into the rhizosphere, playing allelopathic roles in plant–plant communities, although its activity in C. campestris has never been investigated. In the search for natural molecules with phytotoxic activity against parasitic weeds, this work hypothesized that trans-cinnamic acid could be active in inhibiting C. campestris growth and that a study of a series of analogs could reveal key structural features for its growth inhibition activity. In the present structure–activity relationship (SAR) study, we determined in vitro the inhibitory activity of trans-cinnamic acid and 24 analogs. The results showed that trans-cinnamic acid’s growth inhibition of C. campestris seedlings is enhanced in eight of its derivatives, namely hydrocinnamic acid, 3-phenylpropionaldehyde, trans-cinnamaldehyde, trans-4-(trifluoromethyl)cinnamic acid, trans-3-chlorocinnamic acid, trans-4-chlorocinnamic acid, trans-4-bromocinnamic acid, and methyl trans-cinnamate. Among the derivatives studied, the methyl ester derivative of trans-cinnamic acid was the most active compound. The findings of this SAR study provide knowledge for the design of herbicidal treatments with enhanced activity against parasitic weeds. Full article