Search Results (74)

Similarly to conventional field crops, weeds often pose significant problems in the cultivation of medicinal plants. To date, no comprehensive documentation exists regarding weed infestation levels in these crops in Serbia. The objective of this study was to provide a valuable foundation for developing effective, site-specific weed management strategies in medicinal crop production. Weeds in five medicinal crops (lemon balm, fennel, peppermint, ribwort plantain, German chamomile), were surveyed based on the agro-phytosociological method between 2019 and 2024, and across 59 plots. A total of 109 weed species were recorded, belonging to 29 families and 88 genera. Among them, 75 were annuals and 34 perennials, including 93 broadleaved species, 10 grasses, and one parasitic species. All surveyed plots were heavily infested with perennial weeds such as Elymus repens, Cirsium arvense, Convolvulus arvensis, Lepidium draba, Rumex crispus, Sorghum halepense, Taraxacum officinale, etc. Also, several annual species were found in high abundance and frequency, including Amaranthus retroflexus, Chenopodium album, Galium aparine, Lactuca serriola, Lamium amplexicaule, L. purpureum, Papaver rhoeas, Stellaria media, Veronica hederifolia, V. persica, etc. The most important ecological factors influencing the composition of weed vegetation in investigated medicinal crops were temperature and light for fennel and peppermint plots, soil reaction for lemon balm and ribwort plantain plots, and nutrient content for German chamomile plots. A perspective for exploitation of these results is the development of effective weed control programs tailored to this specific cropping system. Weed control strategies should consider such information, targeting the control of the most frequent, abundant, and dominant species existing in a crops or locality. Full article

The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production. 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

Weed competition in inter- and intra-row zones presents a substantial challenge to crop productivity, with intra-row weeds posing a particularly severe threat. Their proximity to crops and higher occlusion rates increase their negative impact on yields. This study examines the efficacy of advanced deep learning architectures—namely, Faster R-CNN, RT-DETR, and YOLOv11—in the accurate identification of weeds and crops within commercial maize fields. A comprehensive dataset was compiled under varied field conditions, focusing on three major weed species: Cyperus rotundus L., Echinochloa crus-galli L., and Solanum nigrum L. YOLOv11 demonstrated superior performance among the evaluated models, achieving a mean average precision (mAP) of 97.5% while operating in real-time at 34 frames per second (FPS). Faster R-CNN and RT-DETR models achieved a mAP of 91.9% and 97.2%, respectively, with processing capabilities of 11 and 27 FPS. Subsequent hardware evaluations identified YOLOv11m as the most viable solution for field deployment, demonstrating high precision with a mAP of 94.4% and lower energy consumption. The findings emphasize the feasibility of employing these advanced models for efficient inter- and intra-row weed management, particularly for early-stage weed detection with minimal crop interference. This study underscores the potential of integrating State-of-the-Art deep learning technologies into agricultural machinery to enhance weed control, reduce operational costs, and promote sustainable farming practices. Full article

Mutagenesis breeding, combined with the application of corresponding herbicides to develop herbicide-resistant rice germplasm, provides great promise for the management of weeds and weedy rice. In this study, a topramezone-resistant rice mutant, TZR1, was developed from the indica rice line Chuangyu 9H (CY9H) through radiation mutagenesis and topramezone selection. Dose–response curves revealed that the resistance index of TZR1 to topramezone was 1.94-fold compared to that of CY9H. The resistance mechanism of TZR1 was not due to target-site resistance. This resistance could be reversed by a specific inhibitor of glutathione S-transferase (GST). The activity of antioxidant enzymes was analyzed. SNPs and Indels were detected using whole-genome resequencing; differentially expressed genes were identified through RNA sequencing. Then, they underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Key candidate genes associated with topramezone resistance were validated via a real-time quantitative PCR assay. Five GST genes, two UDP-glycosyltransferase genes, and three ATP-binding cassette transporter genes were identified as potential contributors to topramezone detoxification in TZR1. Overall, these findings suggest that GST enzymes possibly play an important role in TZR1 resistance to topramezone. This study will provide valuable information for the scientific application of 4-hydroxyphenylpyruvate dioxygenase inhibitors in paddy fields in future. Full article

Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses this gap. However, information on Raspberry Pi’s use in PA remains limited. This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE Xplore) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). Narrative synthesis and subgroup analysis were used to synthesize the results. Findings suggest Raspberry Pi can be a central unit to control sensors, enabling cost-effective automated decision support for PA, particularly in plant disease detection, site-specific weed management, plant phenotyping, biomass estimation, and irrigation systems. Despite focusing on these areas, further research is essential on other PA applications such as livestock monitoring, UAV-based applications, and farm management software. Additionally, Raspberry Pi can be used as a valuable learning tool for students, researchers, and farmers and can promote PA adoption globally, helping stakeholders realize its potential. Full article

Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices. Full article

This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale. Full article

Genetically modified (GM) herbicide-tolerant soybean ‘Zhonghuang 6106’, which introduces a glyphosate-resistant gene, ensures soybean yield while allowing farmers to reduce the use of other herbicides, thereby reducing weed management costs. To protect consumer rights and facilitate government supervision, we have established a simple and rapid on-site nucleic acid detection method for GM soybean ‘Zhonghuang 6106’. This method leverages the isothermal amplification characteristics of RPA technology and the high specificity of CRISPR-Cas12a to achieve high sensitivity and accuracy in detecting GM soybean components. By optimizing experimental conditions, the platform can quickly produce visual detection results, significantly reducing detection time and improving efficiency. The system can detect down to 10 copies/μL of ‘Zhonghuang 6106’ DNA templates, and the entire detection process takes about 1 h. The technology also has strong editing capabilities; by redesigning the primers and crRNA in the method, it can become a specific detection method for other GM samples, providing strong technical support for the regulation and safety evaluation of GM crops. Full article

The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under various management strategies. Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy. Although fitness costs associated with resistant plants reduced the resistance evolution, they did not affect the speed of its spread. The most effective strategies for slow resistance involved diversifying cropping sequences and herbicide applications. Pollen flow was the main dispersal vector, with seed dispersal also making a significant contribution. Strategies limiting seed dispersal effectively decreased resistance spread. However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area. It would be beneficial to optimize the movement of harvesters between fields. The model presented here is a useful tool that could assist in the exploration of novel management strategies within the context of site-specific weed management at landscape scale as well as in the advancement of our understanding of resistance dynamics. Full article

Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield. Full article

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features. Full article

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology. Full article

Environmental DNA (eDNA) is widely used for detecting target species, including monitoring endangered species and detecting the presence of invasive species. Detecting targeted species using the eDNA approach is typically carried out with species-specific qPCR assays. Amazon frogbit (Limnobium laevigatum) is classified as a State-Prohibited Matter Weed in NSW, Australia. It is a fast-growing perennial aquatic weed that outcompetes native aquatic plants, leading to a reduction in the habitats of aquatic animals. Early detection is crucial for the effective management of this species. In this study, we developed a qPCR assay for L. laevigatum based on the rpoB gene sequence. This assay was validated against 25 non-target aquatic and terrestrial species. It was found to be species-specific, with the positive signal exclusively detected in L. laevigatum. The assay was highly sensitive with the modelled detection limits of 3.66 copies of DNA/µL. Furthermore, our assay was validated using environmental samples collected from field sites with and without the presence of L. laevigatum. Our assay is an effective tool for targeted eDNA detection of L. laevigatum, which will enhance efforts to monitor and control this invasive aquatic weed. Full article

The scientific aim of this article is to investigate the potential benefits of implementing a multi-cropping system, specifically focusing on the incorporation of caraway, to improve soil agrochemical and biological properties, prevent soil degradation and erosion, and ultimately enhance soil quality and health to better adapt to climate change. This study aims to provide valuable insights into the comparative analysis of various soil parameters and biological indicators to showcase the promising perspectives and importance of perennial crop production for improving soil quality and agricultural sustainability. These crops are designed to provide multiple benefits simultaneously, including improved yields, enhanced ecosystem services, and reduced environmental effects. However, an integrated assessment of their overall effects on the agroecosystem is crucial to understand their potential benefits and trade-offs. The field experiment was conducted over three consecutive vegetative seasons (2017 to 2021) at the Experimental Station of Vytautas Magnus University Agriculture Academy (VMU AA) in Kaunas district, Lithuania. The experimental site is located at 54°53′7.5″ N latitude and 23°50′18.11″ E longitude. The treatments within a replicate were multi-cropping systems of sole crops (spring barley (1), spring wheat (2), pea (3), caraway (4)), binary crops (spring barley–caraway (5), spring wheat–caraway (6), pea–caraway (7)), and trinary crops (spring barley–caraway–white clover (8), spring wheat–caraway–white clover (9), pea–caraway–white clover (10)) crops. However, an integrated assessment of their impact on the agroecosystem is needed to understand their potential benefits and processes. To determine the complex interactions between indicators, the interrelationships between indicators, and the strength of impacts, this study applied an integrated assessment approach using the comprehensive assessment index (CEI). The CEI values showed that integrating caraway (Carum carvi L.) into multi-cropping systems can have several positive effects. The effect of the binary spring barley and caraway and the trinary spring barley, caraway, and white clover crops on the agroecosystem is positively higher than that of the other comparative sole, binary, and trinary crops. Caraway, after spring wheat together with white clover, has a higher positive effect on the agroecosystem than caraway without white clover. Specifically, this study addresses key aspects, such as soil health, nutrient cycling, weed management, and overall agricultural sustainability, within the context of multi-cropping practices. By evaluating the effects of these cropping systems on soil agrochemical properties and ecosystem dynamics, the research provides valuable insights into sustainable agricultural practices that promote environmental conservation and long-term soil health. Full article