Search Results (78)

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation. Full article

Echinochloa spp. are among the most problematic malignant weeds in paddy fields. Under long-term herbicide selection pressure, they have developed resistances to multiple herbicides, leading to diminished control efficacy. Precision herbicide application, tailored to the susceptibility disparities among Echinochloa species, has emerged as a promising strategy to enhance weed control efficacy and decelerate herbicide resistance development. Nevertheless, the herbicide susceptibility variation across different Echinochloa taxa remain uncharted. Therefore, in this study, we determined the susceptibility of five Echinochloa species to 15 commonly used herbicides using the whole-plant bioassay method. Additionally, we explored the feasibility of employing the CDDP molecular marker technique for the rapid identification of distinct Echinochloa species. The results showing that five Echinochloa species exhibited differential susceptibility to 12 of the 15 herbicides tested underscore the necessity of personalized herbicide application strategies. Among the seven CDDP markers, KNOX-3 generated a specific band in the Echinochloa caudata population, which can be used to distinguish it from the other four Echinochloa species. The findings of this study will facilitate the precision application of herbicides for Echinochloa management in paddy fields. Full article

The application of drones has contributed to automated herbicide spraying in the context of precision agriculture. Although drone technology is mature, the widespread application of agricultural drones and their numerous advantages still demand improvements in battery endurance during flight missions in agricultural operations. This issue has been addressed by optimizing the path planning to minimize the time of the route and, therefore, the energy consumption. In this direction, a novel framework for autonomous drone-based herbicide applications that integrates deep learning-based semantic segmentation and coverage path optimization is proposed. The methodology involves computer vision for path planning optimization. First, semantic segmentation is performed using a DeepLab v3+ convolutional neural network to identify and classify regions containing weeds based on aerial imagery. Then, a coverage path planning strategy is applied to generate efficient spray routes over each weed-infested area, represented as convex polygons, while accounting for the drone’s refueling constraints. The results demonstrate the effectiveness of the proposed approach for optimizing coverage paths in weed-infested sugarcane fields. By integrating semantic segmentation with clustering and path optimization techniques, it was possible to accurately localize weed patches and compute an efficient trajectory for UAV navigation. The GA-based solution to the Traveling Salesman Problem With Refueling (TSPWR) yielded a near-optimal visitation sequence that minimizes the energy demand. The total coverage path ensured complete inspection of the weed-infected areas, thereby enhancing operational efficiency. For the sugar crop considered in this contribution, the time to cover the area was reduced by 66.3% using the proposed approach because only the weed-infested area was considered for herbicide spraying. Validation of the proposed methodology using real-world agricultural datasets shows promising results in the context of precision agriculture to improve the efficiency of herbicide or fertilizer application in terms of herbicide waste reduction, lower operational costs, better crop health, and sustainability. Full article

The xeno-fungusphere, a novel microbial ecosystem formed by integrating exogenous fungi, indigenous soil microbiota, and electroactive microorganisms within microbial fuel cells (MFCs), offers a transformative approach for agricultural remediation and medicinal plant conservation. By leveraging fungal enzymatic versatility (e.g., laccases, cytochrome P450s) and conductive hyphae, this system achieves dual benefits. First, it enables efficient degradation of recalcitrant agrochemicals, such as haloxyfop-P, with a removal efficiency of 97.9% (vs. 72.4% by fungi alone) and a 27.6% reduction in activation energy. This is driven by a bioelectric field (0.2–0.5 V/cm), which enhances enzymatic activity and accelerates electron transfer. Second, it generates bioelectricity, up to 9.3 μW/cm², demonstrating real-world applicability. In medicinal plant soils, xeno-fungusphere MFCs restore soil health by stabilizing the pH, enriching dehydrogenase activity, and promoting nutrient cycling, thereby mitigating agrochemical-induced inhibition of secondary metabolite synthesis (e.g., ginsenosides, taxol). Field trials show 97.9% herbicide removal in 60 days, outperforming conventional methods. Innovations, such as adaptive electrodes, engineered strains, and phytoremediation-integrated systems, have been used to address soil and fungal limitations. This technology bridges sustainable agriculture and bioenergy recovery, offering the dual benefits of soil detoxification and enhanced crop quality. Future IoT-enabled monitoring and circular economy integration promise scalable, precision-based applications for global agroecological resilience. Full article

The aim of this study is to precisely elucidate the control efficacy of drip irrigation herbicide application against broadleaf weeds and comprehensively assess its safety to cotton. Broadleaf weeds were managed through the application of herbicide in the cotton field. The herbicide was dispensed from a fertilizer tank in tandem with water droplets. A field investigation was conducted via a fixed-point investigation method to assess the herbicide residue levels and the safety of the cotton crop from 2022 to 2023. When 100.8 g a.i./hm² of 48% Flumioxazin SC was applied via drip irrigation, it had no adverse effect on cotton safety at the mature stage. During the fruit-setting stage, it exhibited a significant weeding effect on annual broadleaf weeds such as Solanum nigrum L. and Chenopodium album L. Analysis revealed no pesticide residues in cotton and cottonseeds. Soil pesticide residues were found to be at a low level. The cotton yield reached 5618.1 kg/hm², and the cotton quality met the national standard requirements. For the control of broadleaf weeds in cotton fields, the application of 100.8 g a.i./hm² of 48% Flumioxazin SC via drip irrigation can effectively control broadleaf weeds. This method can suppress annual broadleaf weeds, with S. nigrum and C. album being the dominant weed communities, without compromising the safety and quality of cotton. Although drip irrigation technology offers advantages such as time savings and reduced labor demands, it is essential to adopt appropriate weed control techniques tailored to the specific conditions of different cotton fields. 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

Soybean–maize intercropping is a traditional yet high-yield cultivation model that faces technical challenges in weed management due to the different herbicide requirements of soybean and maize. This study presents the design and experiments of the innovative dual-herbicide direct injection system, which can simultaneously deliver glyphosate and fomesafen through real-time concentration modulation. The system operates by measuring the relationship between the mixing ratio and the conductivity value, mathematical model, and control algorithm. Experimental validation demonstrated that the correlation coefficient of herbicide mixing ratios and measured conductivity values across pressure ranges of 0.1–0.3 MPa are greater than 0.98, which means that measuring the mixing ratio using conductivity is reliable. Optimal operational performance was achieved at 0.2 MPa spraying pressure, characterized by superior mixing uniformity (CV < 5%) and system stability. This technological advancement provides a practical solution for precision agrochemical application in complex cropping models, with potential applications extending to other crop combinations requiring differential herbicide treatments. 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

Solanum rostratum Dunal, an invasive weed first recorded in Israel in the 1950s, undergoes multiple germination waves from early spring to late summer. Recently, its distribution has significantly expanded, with new populations reported throughout the country. This study assessed the efficacy of various herbicides for controlling S. rostratum populations under two distinct temperature regimes, focusing on temperature-dependent variations in herbicide performance. The results demonstrated that fluroxypyr and tembotrione consistently achieved high levels of control across all temperature conditions. Conversely, oxyfluorfen exhibited low performance under elevated temperatures and showed greater population-specific variability, while metribuzin proved more effective at higher temperatures across all S. rostratum populations. These findings emphasize the pivotal role of post-application temperature in influencing herbicide efficacy and underscore the importance of a precise application timing to optimize the control outcomes. Temperature-optimized herbicide strategies could play a critical role in limiting the spread and establishment of S. rostratum in agricultural systems, contributing to a sustainable and effective weed management. Full article

Background: Glyphosate has been extensively used as herbicide since the early 1970s. The daily exposure limit is set at 0.3 mg/kg bw/d in Europe and 1.75 mg/kg bw/d in the USA. Among its derivatives, aminomethylphosphonic acid is the most stable and abundant. Understanding their biological effects then requires reliable methods for quantification in biological samples. Methods: We developed and validated a fast, low-cost, and reliable chromatographic method for determining glyphosate and aminomethylphosphonic acid concentrations. The validation included following parameters: specificity, selectivity, matrix effect, accuracy, precision, calibration performance, limit of quantification, recovery, and stability. Sample extraction employed an anion exchange resin with elution using hydrochloric acid 50.0 mmol/L. For HPLC analysis, analytes were derivatized, separated on a C18 column with a mobile phase of phosphate buffer (0.20 mol/L, pH 3.0) and acetonitrile (85:15), and detected at 240 nm. Results: The method demonstrated high reliability and reproducibility across various matrices. Its performance met all validation criteria, confirming its suitability for quantifying glyphosate and aminomethylphosphonic acid in different biological and experimental setups. Conclusions: This method can offer a practical resource for applications in experimental research, medical diagnostics, quality control, and food safety. Full article

Efficient and accurate weed detection in wheat fields is critical for precision agriculture to optimize crop yield and minimize herbicide usage. The dataset for weed detection in wheat fields was created, encompassing 5967 images across eight well-balanced weed categories, and it comprehensively covers the entire growth cycle of spring wheat as well as the associated weed species observed throughout this period. Based on this dataset, PMDNet, an improved object detection model built upon the YOLOv8 architecture, was introduced and optimized for wheat field weed detection tasks. PMDNet incorporates the Poly Kernel Inception Network (PKINet) as the backbone, the self-designed Multi-Scale Feature Pyramid Network (MSFPN) for multi-scale feature fusion, and Dynamic Head (DyHead) as the detection head, resulting in significant performance improvements. Compared to the baseline YOLOv8n model, PMDNet increased mAP@0.5 from 83.6% to 85.8% (+2.2%) and mAP@0.50:0.95 from 65.7% to 69.6% (+5.9%). Furthermore, PMDNet outperformed several classical single-stage and two-stage object detection models, achieving the highest precision (94.5%, 14.1% higher than Faster-RCNN) and mAP@0.5 (85.8%, 5.4% higher than RT-DETR-L). Under the stricter mAP@0.50:0.95 metric, PMDNet reached 69.6%, surpassing Faster-RCNN by 16.7% and RetinaNet by 13.1%. Real-world video detection tests further validated PMDNet’s practicality, achieving 87.7 FPS and demonstrating high precision in detecting weeds in complex backgrounds and small targets. These advancements highlight PMDNet’s potential for practical applications in precision agriculture, providing a robust solution for weed management and contributing to the development of sustainable farming practices. Full article

The development of innovative, cost effective, and biocompatible sensor materials for rapid and efficient practical applications is a key area of focus in electroanalytical chemistry. In this research, we report on a novel biocompatible sensor, made using a unique polybenzoxazine-based carbon combined with amino cellulose and hyaluronic acid to produce a bio-polymer complex (PBC-ACH) (polybenzoxazine-based carbon with amino cellulose and hyaluronic acid). This sensor material is fabricated for the first time to enable the electroreduction of the herbicide, metribuzin (MTZ). The PBC-ACH sensor presents multiple advantages, including ease of fabrication, excellent biocompatibility, and low-cost production, making it suitable for various applications. In optimized experimental conditions, the sensor was fabricated by modifying a glassy carbon electrode (GCE) with the PBC-ACH complex, resulting in the creation of a GCE/PBC-ACH electrode. This modified electrode demonstrated the ability to detect MTZ at nanomolar levels, with an LoD of 13.04 nM, showcasing a high sensitivity of 1.40 µA µM⁻¹ cm⁻². Moreover, the GCE/PBC-ACH sensor exhibited remarkable selectivity, stability, and reproducibility in terms of its electrochemical performance, which are essential features for reliable sensing applications. The potential mechanism behind the detection of MTZ using the GCE/PBC-ACH sensor was investigated thoroughly, providing insights into its sensing behavior. Additionally, tests on real samples validated the sensor’s practicality and efficiency in detecting specific analytes. These findings emphasize the potential of the GCE/PBC-ACH sensor as a highly effective electrochemical sensor, with promising applications in environmental monitoring and other fields requiring precise analyte detection. Full article

In plasticulture production systems, the conventional practice involves broadcasting pre-emergent herbicides over the entire surface of raised beds before laying plastic mulch. However, weed emergence predominantly occurs through the transplant punch-holes in the mulch, leaving most of the applied herbicide beneath the plastic, where weeds cannot grow. To address this issue, we developed and evaluated a precision spraying system designed to target herbicide application to the transplant punch-holes. A dataset of 3378 images was manually collected and annotated during a tomato experimental trial at the University of Florida. A YOLOv8x model with a p2 output layer was trained, converted to TensorRT^® to improve the inference time, and deployed on a custom-built computer. A Python-based graphical user interface (GUI) was developed to facilitate user interaction and the control of the smart sprayer system. The sprayer utilized a global shutter camera to capture real-time video input for the YOLOv8x model, which activates or disactivates a TeeJet solenoid for precise herbicide application upon detecting a punch-hole. The model demonstrated excellent performance, achieving precision, recall, mean average precision (mAP), and F1score exceeding 0.90. Field tests showed that the smart sprayer reduced herbicide use by up to 69% compared to conventional broadcast methods. The system achieved an 86% punch-hole recognition rate, with a 14% miss rate due to challenges such as plant occlusion and variable lighting conditions, indicating that the dataset needs to be improved. Despite these limitations, the smart sprayer effectively minimized off-target herbicide application without causing crop damage. This precision approach reduces chemical inputs and minimizes the potential environmental impact, representing a significant advancement in sustainable plasticulture weed management. Full article

Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. They help with habitat function, herbicide application, temperature regulation, etc. Understanding the relationship between tree crown area and stock volume is crucial, as it provides a key metric for assessing the impact of land-use changes on ecological processes. Traditional ground-based stock volume estimation using DBH (Diameter at Breast Height) is labor-intensive and often impractical. However, high-resolution UAV (unmanned aerial vehicle) imagery has revolutionized remote sensing and computer-based tree analysis, making forest studies more efficient and interpretable. Previous studies have established correlations between DBH, stock volume and above-ground biomass, as well as between tree crown area and DBH. This research aims to explore the correlation between tree crown area and stock volume and automate stock volume and above-ground biomass estimation by developing an empirical model using UAV-RGB data, making forest assessments more convenient and time-efficient. The study site included a significant number of training and testing sites to ensure the performance level of the developed model. The findings underscore a significant association, demonstrating the potential of integrating drone technology with traditional forestry techniques for efficient stock volume estimation. The results highlight a strong exponential correlation between crown area and stem stock volume, with a coefficient of determination of 0.67 and mean squared error (MSE) of 0.0015. The developed model, when applied to estimate cumulative stock volume using drone imagery, demonstrated a strong correlation with an R² of 0.75. These results emphasize the effectiveness of combining drone technology with traditional forestry methods to achieve more precise and efficient stock volume estimation and, hence, automate the process. Full article