Search Results (262)

Crop rotation is a well-established practice that helps reduce nutrient depletion and pressure from pests and weeds. At the same time, the use of artificial intelligence tools to recognize crops from satellite multispectral imagery is gaining momentum as a first step toward automated agricultural monitoring. However, the recognition process is limited by inherent errors and the scarcity of available data. In this paper, we build upon Monte Carlo simulation methods to investigate whether incorporating crop rotation information—encoded as a Markov chain—can improve identification accuracy. To broaden the simulation across diverse datasets, we also synthesize multispectral pixels for underrepresented crop types. Crop rotation is used not only in post-processing, but also integrated into the classifier, where a Gradient Boosting Machine is adapted to penalize learners that predict the same crop as in the previous year. Our evaluation uses Sentinel satellite imagery of agricultural crops, combined with the DACIA5 database from the Brașov region of Romania. We conclude that incorporating accurate prior information and crop rotation models noticeably improves crop identification performance. Synthesized data further enhances recognition rates and enables broader applicability, beyond the original region. Full article

Field bindweed (Convolvulus arvensis L.) is a competitive herbaceous perennial weed that reduces productivity in irrigated pastures. Grazing might reduce competition by field bindweed when it begins growth in the spring, thereby encouraging encroachment by desirable grass species during the summer. To test this hypothesis, a two-year study was conducted in two adjacent, privately owned, irrigated, warm-season perennial grass pastures (replicates) that were heavily infested with field bindweed. Study sites were near Tucumcari, NM, USA. The fields were grazed with exclosures to evaluate ungrazed management. Aboveground biomass of field bindweed, other weeds, and perennial grass were measured, and field bindweed plants were counted in May of 2018 and 2019. There was no difference between years for any variable. Other weed biomass and field bindweed biomass and plant numbers were reduced (p < 0.05) by grazing (61.68 vs. 41.67 g bindweed biomass m⁻² for ungrazed and grazed management, respectively, and 108.5 and 56.8 bindweed plants m⁻² for ungrazed and grazed management, respectively). Otherwise, perennial grass production was unaffected by either year or management. These results indicate that grazing can be an effective tool to reduce field bindweed competition in warm-season perennial grass pastures. Full article

Asia minor bluegrass (Polypogon fugax), a widespread Poaceae weed, exhibits broad tolerance to abiotic stresses. Validated reference genes (RGs) for reliable RT-qPCR normalization in this ecologically and agriculturally significant species remain unidentified. This study identified eight candidate RGs using transcriptome data from seedling tissues. We assessed the expression stability of these eight RGs across various abiotic stresses and developmental stages using Delta Ct, BestKeeper, geNorm, and NormFinder algorithms. A comprehensive stability ranking was generated using RefFinder, with validation performed using the target genes COR413 and P5CS. Results identified EIF4A and TUB as the optimal RG combination for normalizing gene expression during heat stress, cold stress, and growth stages. EIF4A and ACT were most stable under drought stress, EIF4A and 28S under salt stress, and EIF4A and EF-1 under cadmium (Cd) stress. Furthermore, EIF4A and UBQ demonstrated optimal stability under herbicide stress. Additionally, application of validated RGs revealed higher acetyl-CoA carboxylase gene (ACCase) expression in one herbicide-resistant population, suggesting target-site gene overexpression contributes to resistance. This work presents the first systematic evaluation of RGs in P. fugax. The identified stable RGs provide essential tools for future gene expression studies on growth and abiotic stress responses in this species, facilitating deeper insights into the molecular basis of its weediness and adaptability. Full article

Common ragweed (Ambrosia artemisiifolia L.) has been identified as one of the most harmful invasive weed species in Europe due to its allergenic pollen and competitive growth in diverse habitats. In the first part of this review [Common Ragweed—Ambrosia artemisiifolia L.: A Review with Special Regards to the Latest Results in Biology and Ecology], its biological characteristics and ecological behavior were described in detail. In the current paper, control strategies are summarized, focusing on integrated weed management adapted to the specific habitat where the species causes damage—arable land, semi-natural vegetation, urban areas, or along linear infrastructures. A range of management methods is reviewed, including agrotechnical, mechanical, physical, thermal, biological, and chemical approaches. Particular attention is given to the spread of herbicide resistance and the need for diversified, habitat-specific interventions. Among biological control options, the potential of Ophraella communa LeSage, a leaf beetle native to North America, is highlighted. Furthermore, innovative technologies such as UAV-assisted weed mapping, site-specific herbicide application, and autonomous weeding robots are discussed as environmentally sustainable tools. The role of legal regulations and pollen monitoring networks—particularly those implemented in Hungary—is also emphasized. By combining traditional and advanced methods within a coordinated framework, effective and ecologically sound ragweed control can be achieved. Full article

Goosegrass (Eleusine indica) is a major weed in Brazilian soybean, corn, and cotton systems, infesting over 60% of grain-producing areas and potentially reducing yields by more than 50%. Its competitiveness is due to its rapid emergence, fast tillering, C4 metabolism, and adaptability to various environmental conditions. A critical challenge relates to its widespread resistance to multiple herbicide modes of action, notably glyphosate and acetyl-CoA carboxylate (ACCase) inhibitors. Resistance mechanisms include 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) target-site mutations, gene amplification, reduced translocation, glyphosate detoxification, and mainly ACCase target-site mutations. This literature review summarizes the current knowledge on herbicide resistance in goosegrass and its management in Brazil, with an emphasis on integrating chemical and non-chemical strategies. Mechanical and physical controls are effective in early or local infestations but must be combined with chemical methods for lasting control. Herbicides applied post-emergence of weeds, especially systemic ACCase inhibitors and glyphosate, remain important tools, although widespread resistance limits their effectiveness. Sequential applications and mixtures with contact herbicides such as glufosinate and protoporphyrinogen oxidase (PPO) inhibitors can improve control. Pre-emergence herbicides are effective when used before or immediately after planting, with adequate soil moisture being essential for their activation and effectiveness. Given the complexity of resistance mechanisms, chemical control alone is not enough. Integrated weed management programs, combining diverse herbicides, sequential treatments, and local resistance monitoring, are essential for sustainable goosegrass management. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Noxious weeds (NWs) are increasingly recognized as a significant threat to the native alpine grassland ecosystems of the Qinghai–Tibetan Plateau (QTP). However, large-scale quantification of their continuous fractional cover remains challenging. This study proposes a pixel-level estimation framework utilizing time-series Sentinel-2 imagery. A Dynamic Mask Non-Stationary Transformer (DMNST) model was developed and trained using multi-temporal multispectral data to map the spatial distribution of NWs in the Three-River Headwaters Region. The model was calibrated and validated using field data collected from 170 plots (1530 quadrats). The results demonstrated that both the dynamic masking module and the non-stationary normalization significantly enhanced the prediction accuracy and robustness, particularly when applied jointly. The model performance varied across different combinations of spectral bands and temporal inputs, with the optimal configurations achieving a test R² of 0.770, MSE of 0.009, and RMSE of 0.096. These findings underscore the critical role of the input configuration and architectural enhancements in accurately modeling the fractional cover of NWs. This study confirms the applicability of Sentinel-2 time-series imagery for modeling the continuous fractional cover of NWs and provides a scalable tool for invasive species monitoring and ecological risk assessment in alpine ecosystems. Full article

Long-term no-till systems have led to shifts in weed communities and reduced the effectiveness of herbicide-based control. Occasional tillage is proposed as an alternative strategy to disrupt weed emergence patterns by redistributing seeds within the soil profile. This study aimed to evaluate the impact of occasional tillage on weed seedbank composition and vertical distribution of viable weed seeds and propagules within the soil profile, after more than 20 years of continuous no-till. A paired-plot experiment was conducted in Carlos Casares, Buenos Aires, Argentina, with three replications. Treatments included continuous no-till and occasional tillage (two disk harrow passes in August 2022 and April 2023) combined with three soil depths (0–5, 5–10, and 10–15 cm). Soil samples were collected in spring 2022 and fall 2023, and weed emergence was recorded under semi-controlled conditions. Overall species richness did not differ significantly between tillage treatments but was consistently greater in the upper 0–5 cm soil layer. Weed abundance also declined with depth. Five species, Chenopodium album, Stellaria media, Eleusine indica, Oxybasis macrosperma, and Heliotropium curassavicum, were frequent across treatments. Some species were exclusive to either no-till or tilled plots, for example, Datura ferox, Poa annua, and Veronica peregrina were found only in tilled plots, while Portulaca oleracea, Medicago lupulina, and Trifolium repens were exclusive to no-till plots. These results indicate that occasional tillage alters species composition and vertical seed distribution in the seedbank without significantly reducing total richness or abundance, offering an additional, but not always effective, tool to influence weed community structure in no-till systems. Full article

Soil management in vineyards is a crucial component of sustainable viticulture. Weed control under the row has traditionally been addressed using mechanical, physical, and chemical techniques, but herbicides pose environmental and health risks. The circular economy offers an alternative approach by converting organic waste into a resource, such as compost. This study explores the effectiveness of compost derived from the organic fraction of municipal solid waste (MSW) not only as a mulching technique but also as a potential biological agent for weed control through allelopathic mechanisms in vineyards. Experiments were conducted both in the field and under controlled conditions. In the field, compost was applied under the vine row as mulch and incorporated into the soil. Under controlled conditions, germination tests were performed to assess weed inhibition at different compost concentrations. Field results demonstrated that compost applications, both as mulch and incorporated into the soil, significantly inhibited weed growth during the first period after application compared to the tilled control without compost. Thus, this inhibition is not limited to physical mulching; it also applies to the release of allelopathic compounds from compost. Controlled condition experiments showed strong inhibition of germination in Cichorium intybus and Foeniculum vulgare seeds, confirming the anti-germinative effects of compost, particularly on small-seeded weed species. Compost is a promising tool for sustainable vineyard management, offering fertilization and weed-suppression benefits while reducing herbicide use. Full article

This study aimed to detect weeds in vineyards throughout the crop cycle using pixel-based classification of RGB imagery captured by unmanned aerial vehicles (UAVs). Five vegetation indices (NGRDI, NDVI, GLI, NDRE, and GNDVI) and three supervised classifiers (SVM, RT, and KNN) were evaluated during four flight campaigns. Classification performance was assessed using precision, recall, and F1-Score, supported by descriptive statistics (mean, standard deviation, and 95% confidence interval), inferential tests (Shapiro–Wilk, ANOVA, and Kruskal–Wallis), and visual map inspection. Statistical analyses, both descriptive and inferential, did not indicate significant differences between classification methods. NGRDI consistently showed strong performance, especially for vine and soil classes, and effectively detected weeds, with F1-Scores above 0.78 in some campaigns, occasionally outperforming the supervised classifiers. GLI displayed variable results and a higher sensitivity to noise, whereas NDVI showed limitations when applied to RGB data, particularly in sparsely vegetated areas. Among the classifiers, the SVM achieved the highest F1-Score for vine (0.9330) and soil (0.9231), whereas KNN produced balanced results and visually coherent maps. RT showed lower accuracy and greater variability, particularly in the weed class. Despite the lack of statistically significant differences, visual analysis favored NGRDI and SVM for generating cleaner classification outputs. Study limitations include lighting variability, reduced spatial coverage owing to low flight altitude, and a lack of spatial context in pixel-based methods. Future research should explore object-based approaches and advanced classifiers (e.g., Random Forest and Convolutional Neural Networks) to enhance robustness and generalization. Overall, RGB-based indices, particularly NGRDI, are cost-effective and reliable tools for weed detection, thereby supporting scalable precision in viticulture. Full article

Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). Full article

Agasicles hygrophila is the most effective natural enemy for the control of the invasive weed Alternanthera philoxeroides (Mart.) Griseb. However, research on the gene function and potential genetic improvement of A. hygrophila is limited due to a lack of effective genetic tools. In this study, we employed the A. hygrophila white (AhW) gene as a target gene to develop a CRISPR/Cas9-based gene editing method applicable to A. hygrophila. We showed that injection of Cas9/sgRNA ribonucleoprotein complexes (RNPs) of the AhW gene into pre-blastoderm eggs induced genetic insertion and deletion mutations, leading to white eyes. Our results demonstrate that CRISPR/Cas9-mediated gene editing is possible in A. hygrophila, offering a valuable tool for studies of functional genomics and genetic improvement of A. hygrophila, which could potentially lead to more effective control of invasive weeds through the development of improved strains of this biocontrol agent. In addition, the white-eyed mutant strain we developed could potentially be useful for other transgenic research studies on this species. Full article

Against the backdrop of increasing herbicide resistance and societal and political objectives for reducing plant protection products, combinations of mechanical and herbicide weed control methods are gaining importance. In row crops such as sugar beet, the use of mechanical hoeing between crop rows (interrow) combined with band spraying of herbicides within rows (intrarow) can lead to significant herbicide savings compared to standard broadcast herbicide applications. However, effective weed control remains crucial. In this study, a two-year field experiment was conducted to evaluate different combinations of band spraying, mechanical hoeing, and broadcast spraying in post-emergence weed control applications in sugar beet. The weed control efficacy of each treatment was assessed relative to an untreated control using weed counting to determine absolute weed density and image analysis to quantify weed cover. Compared to the untreated control, total weed control efficiencies of up to 90.8% (weed counting) and 99.5% (image analysis) were achieved. In comparison to three consecutive broadcast herbicide applications, the mechanical–chemical combinations resulted in a similar or even superior weed control efficacy while enabling herbicide reductions of up to 65.59%. These results highlight the valuable potential of mechanical–chemical weed control combinations for herbicide-reduced weed management within post-emergence application systems in sugar beet. They represent a key tool in the context of integrated weed management (IWM). Full article

This study presents the evaluation of tools for weed analysis and management to support agroecological practices in organic farming, emphasizing agriculture digitalization, and remote sensing. The main aim was to provide techniques for monitoring and predicting weed spread using multispectral satellite and drone data, without the use of chemical inputs. Key findings indicate that VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density. However, RE and NIR channels of drone detected significant variations and proved effectiveness for weediness monitoring. The NIR channel is sensitive to agrotechnical factors such as cultivation type, making it valuable for field monitoring. Correlation and regression analyses revealed that B2, B3, B8 channels of Sentinel-2, and RE and NIR drone channels are the most reliable for predicting weed levels. Conversely, Sentinel-1 showed limited predictive utility. Random effect models confirmed that Sentinel-2 and drone channels can accurately account for site characteristics and timing of weed proliferation. Taken together, these tools provide effective organic weed monitoring systems, enabling rapid identification of problem areas and adjustments in agronomic practices. Full article

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures. Full article