Search Results (3,055)

Weed canopy cover assessment, particularly using drone-acquired data, plays a vital role in precision agriculture by providing accurate, timely, and spatially detailed information, enhancing weed management decision-making in response to environmental and management variables. Despite the significance of this approach, few studies have investigated weed canopy cover through drone-based imagery. This study aimed to fill this gap by evaluating the effects of conventional tillage (CT) and no-till (NT) practices on weed canopy cover in a winter wheat field over two growing seasons. Results indicated that in the 2022–2023 season, weed populations were similar between tillage systems, with a high mean weed cover of 1.448 cm² ± 0.241 in CT plots. In contrast, during the 2023–2024 season, NT plots exhibited a substantially higher mean weed cover (1.784 cm² ± 0.167), with a significant overall variation (p < 0.05) in weed distribution between CT and NT plots. These differences suggest that, while CT practices initially mask weed emergence by burying seeds and disrupting root systems, NT practices encourage greater weed establishment over time by leaving seeds near the soil surface. These findings provide valuable insights for optimizing weed management practices, emphasizing the importance of comprehensive approaches to improve weed control and overall crop productivity. Full article

Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields. Full article

The production of wheat is fundamentally interconnected with worldwide food security. The practice of deep tillage (DT) cultivation has shown advantages in terms of soil enhancement and the mitigation of diseases and weed abundance. Nevertheless, the specific mechanisms behind these advantages are unclear. Accordingly, we aimed to clarify the influence of DT on rhizosphere soil (RS) microbial communities and its possible contribution to the improvement of soil quality. Soil fertility was evaluated by analyzing several soil characteristics. High-throughput sequencing techniques were utilized to explore the structure and function of rhizosphere microbial communities. Despite lowered fertility levels in the 0–20 cm DT soil layer, significant variations were noted in the microbial composition of the DT wheat rhizosphere, with Acidobacteria and Proteobacteria being the most prominent. Furthermore, the abundance of Bradyrhizobacteria, a nitrogen-fixing bacteria within the Proteobacteria phylum, was significantly increased. A significant increase in glycoside hydrolases within the DT group was observed, in addition to higher abundances of amino acid and carbohydrate metabolism genes in the COG and KEGG databases. Moreover, DT can enhance soil quality and boost crop productivity by modulating soil microorganisms’ carbon and nitrogen fixation capacities. Full article

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in this study based on multi-modal data to classify relevant operational events in mechanized weed control operations. The architecture of a neural network was tuned in terms of the number of hidden layers and neurons, and the regularization term was set at various values to obtain optimally tuned models for three data modalities: triaxial acceleration data coupled with speed extracted from GNSS signals (AS), triaxial acceleration (A), and speed alone (S). In the training and validation phase, the models based on AS and A achieved a very high classification accuracy, accounting for 92 to 93% when considering four relevant events. In the testing phase, which was run on unseen data, the classification accuracy reached figures of 91 to 92%, indicating a good generalization ability of the models. The results point out that multimodal data are able to provide the features for distinguishing events and add spatial context to the monitored operations, standing as a suitable solution for offline, partly automated monitoring. Future studies are required to see how the capabilities of online, real-time technologies such as deep learning coupled with computer vision can add more context and improve classification performance. Full article

Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems. Full article

Angelica archangelica L. (Garden angelica) is a medicinal and aromatic plant from the Apiaceae family, originating from North Europe (Iceland, Greenland, and Scandinavian countries). A. archangelica is commonly used in traditional medicine to treat anxiety, insomnia, stomach and intestinal disorders, skin conditions, respiratory problems, and arthritis. This plant is generally cultivated for its root and seed where the essential oil (EO) is concentrated the most. Angelica archangelica cultivation has a lot of challenges but the main one is weed control; so, the aim of this study was to investigate the influence of four different mulch types as non-chemical weed control measures on weediness, fresh root yield, and EO chemical composition and yield from A. archangelica roots. A field trial was conducted with the following six treatments: two organic mulches, two synthetic mulches, and two controls (regular hand-weeded and weeded). The results show that the most present weeds were Ambrosia artemisiifolia, Chenopodium album, Polygonum aviculare, and Polygonum lapathyfolium, but synthetic mulch foils achieved the best weed suppression (100%). These fields also achieved the highest fresh root yield in both of the experimental seasons. The highest EO yield was detected with agrotextile mulch foil at season I (0.41%, v/w) and with the weeded control (0.51%, v/w) at season II, but dominant components at both seasons were α-pinene and β-phellandrene. The results suggest that the agrotextile black and silver–brown mulch foils achieved complete weed suppression, but the agrotextile black mulch foil had a better effect on fresh root yield, EO yield, and its chemical composition. Full article

This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. Additionally, ablation experiments reveal that the latent-space-based diffusion subnetwork outperforms traditional models, such as the the residual diffusion network, which has a precision of only 0.75. By combining latent space feature extraction with self-attention mechanisms, the constructed lightweight model can respond quickly on mobile devices, showcasing the significant potential of deep learning technologies in agricultural applications. Future research will focus on data diversity and model interpretability to further enhance the model’s adaptability and user trust. Full article

The chemical composition of raw oat grain is responsible for the high dietary value and health-promoting properties of oat products. This article presents the results of a study investigating the biofortification of grain in two oat genotypes—hulless and hulled—through agronomic treatments: chemical plant protection against weeds and fungi and mineral nitrogen fertilization. The applied agronomic treatments induced different changes in the fatty acid profiles, content of tocopherols, macronutrients, and micronutrients in the grain of hulled and hulless oats. Plant health contributed to higher concentrations of unsaturated fatty acids and potassium in oat grain. In turn, nitrogen fertilization decreased the content of unsaturated fatty acids, potassium, and copper and increased the content of saturated fatty acids, calcium, and manganese in oat grain. At the same time, agronomic treatments reduced the tocopherol content of the grain, which implies that the nutritional value of oats increases in the absence of chemical plant protection agents. The correlations between the content of desirable chemical compounds and agronomic treatments were stronger in hulless oat grain, which may suggest that the agronomic modification of oat-based foods is more effective in this genotype. The content of exogenous alpha-linoleic acid C18:3 n-3 and alpha-tocopherol was higher in grain harvested from the control treatment (without chemical plant protection), whereas grain harvested from fully protected treatments accumulated more essential gamma-linolenic acid C18:3 n-6. The content of gamma-tocopherol and copper in oat grain was higher in the absence of nitrogen fertilization. Full article

The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas. Full article

The invasion of non-native plant species presents a significant ecological challenge worldwide, impacting native ecosystems and biodiversity. These invasive plant species significantly affect the native ecosystem. The threat of invasive plant species having harmful effects on the natural ecosystem is a serious concern. Invasive plant species produce secondary metabolites, which not only help in growth and development but are also essential for the spread of these plant species. This review highlights the important functions of secondary metabolites in plant invasion, particularly their effect on allelopathy, defense system, interaction with micro soil biota, and competitive advantages. Secondary metabolites produced by invasive plant species play an important role by affecting allelopathic interactions and herbivory. They sometimes change the soil chemistry to make a viable condition for their proliferation. The secondary metabolites of invasive plant species inhibit the growth of native plant species by changing the resources available to them. Therefore, it is necessary to understand this complicated interaction between secondary metabolites and plant invasion. This review mainly summarizes all the known secondary metabolites of non-native plant species, emphasizing their significance for integrated weed management and research. 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

Citrus trees, particularly oranges, are a highly significant plant genus due to their consumption as fresh produce and the multiple compounds derived from them, which are extensively used in the food, cosmetic, and pharmaceutical industries. Despite recent advancements, the understanding of metabolic processes in the Citrus genus remains limited, especially in the context of variable agricultural practices. This study aimed to investigate the metabolomic evolution in leaves of sweet orange (Citrus sinensis) cultivated under different conditions over two key developmental periods: pre-winter (t₁) and spring sprouting and flowering (t₂). Using proton nuclear magnetic resonance (H-NMR) spectroscopy, this research identified 27 key metabolites across five distinct cultivation treatments (T0, T1, T2, T3, T4), including amino acids, organic acids, and sugars, and their variation over time. T0 represents the traditional crop of the control plot, while T1, T2, T3, and T4 incorporate different strategies aimed at water-saving, such as the use of weed control mesh and subsurface drainage systems, all designed to improve profitability and crop efficiency under the same soil and climatic conditions. The treatments were evaluated for their impact on plant growth parameters such as height, trunk diameter, and flower production, with a focus on reducing water usage without compromising crop performance. The results indicate that the use of weed control mesh significantly improves plant growth, increases flower production, and stabilizes key metabolite levels, contributing to a concept termed “plant metabolomic homeostasis.” These findings are particularly relevant in regions like southeastern Spain, where water scarcity is a major concern. The study provides compelling evidence that the implementation of weed control mesh in orange cultivation can enhance water efficiency, promote healthier plant development, and maintain metabolic stability under variable growing conditions. These results suggest that such agricultural practices could be recommended for broader commercial application in citrus cultivation to improve sustainability and crop profitability. Full article

The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional parameters of a municipal football field. The analysis includes visual assessments, measurements of grass height, and evaluations of surface hardness, comparing the performance of the two cutting systems. Additionally, studies of turfgrass composition and machine learning techniques, particularly with YOLOv8s and YOLOv8n, are conducted to test the capability of assessing weed and turfgrass species distribution. The results indicate significant differences in grass color based on the position (5.36 in the corners and 3.69 in the central area) and surface hardness between areas managed with a traditional ride-on mower (15.25 Gmax) and an autonomous mower (10.15 Gmax) in the central region. Higher height values are recorded in the area managed with the ride-on mower (2.94 cm) than with the autonomous mower (2.61 cm). Weed presence varies significantly between the two cutting systems, with the autonomous mower demonstrating higher weed coverage in the corners (17.5%). Higher overall performance metrics were obtained through YOLOv8s. This study underscores the importance of innovative management practices and monitoring techniques in optimizing the quality and playability of a football field while minimizing environmental impact and management efforts. Full article

The comparative analysis of homologous enzymes is a valuable approach for elucidating enzymes’ structure–function relationships. Glutathione transferases (GSTs, EC. 2.5.1.18) are crucial enzymes in maintaining the homeostatic stability of plant cells by performing various metabolic, regulatory, and detoxifying functions. They are promiscuous enzymes that catalyze a broad range of reactions that involve the nucleophilic attack of the activated thiolate of glutathione (GSH) to electrophilic compounds. In the present work, three highly homologous (96–98%) GSTs from ryegrass Lolium perenne (LpGSTs) were identified by in silico homology searches and their full-length cDNAs were isolated, cloned, and expressed in E. coli cells. The recombinant enzymes were purified by affinity chromatography and their substrate specificity and kinetic parameters were determined. LpGSTs belong to the tau class of the GST superfamily, and despite their high sequence homology, their substrate specificity displays remarkable differences. High catalytic activity was determined towards hydroxyperoxides and alkenals, suggesting a detoxification role towards oxidative stress metabolites. The prediction of the structure of the most active LpGST by molecular modeling allowed the identification of a non-conserved residue (Phe215) with key structural and functional roles. Site-saturation mutagenesis at position 215 and the characterization of eight mutant enzymes revealed that this site plays pleiotropic roles, affecting the affinity of the enzyme for the substrates, catalytic constant, and structural stability. The results of the work have improved our understanding of the GST family in L. perenne, a significant threat to agriculture, sustainable food production, and safety worldwide. Full article

Harrow tine is a widely used tool for mechanical weeding. However, the effect of its geometry on its fatigue life is something that has not been studied well yet. In this work, two different harrow tines (0.5260” HT and 0.6253” HT) were analyzed to understand how their geometry affects their fatigue life under different field conditions and material properties. Finite element analyses were performed on these harrow tines by applying different degrees of leg deflections (3, 6, 8, 10, and 12 inches) for von Mises stress distributions and failure life cycles. The results suggested that 0.526” HT has a longer cycle life than 0.6253” HT. With the largest leg deflection and controlled conditions, 0.5260” HT exhibits a harrowing capability of 133.62 hectares, which is larger than 0.6253” HT’s 117.66 hectares. Apart from the numerical analyses, prototypes (1:0.35 scaled-down model) of these harrow tines were additively manufactured using a rigid ultraviolet curable resin. Finally, a custom test setup was used to perform simple load-bearing tests of these harrow tines. Full article