Search Results (192)

Agricultural mobile platforms provide the physical foundation for sensing, field operations, and material handling in smart farming, yet their design is strongly constrained by crop geometry, terrain conditions, task-specific payloads, energy demand, and operational reliability. This review examines agricultural mobile platforms from four complementary perspectives: design and operational requirements, platform classification, powertrain and mobility technologies, and agricultural applications. Wheeled, tracked, legged and wheel-legged, and rail-guided or constrained-motion platforms are compared in terms of mobility characteristics and suitable operating environments. Power sources, drive systems, steering mechanisms, mobility control, and platform–implement integration are further discussed, with particular attention to electrification, distributed drive, dynamic loads, and coordinated power allocation. Representative applications include crop monitoring and phenotyping, precision crop management, weeding and harvesting, and transportation and multi-purpose operations. Current challenges arise from the strong coupling among terrain adaptability, payload, energy capacity, autonomy, and long-term reliability, as well as limited interoperability between platforms and implements. Future development should emphasize task-oriented reconfigurable platforms, standardized mechanical and electrical interfaces, task-level energy management, and mobility control that accounts for real-time platform and terrain conditions. Full article

Laser weeding offers significant advantages over conventional weed control methods; however, its practical deployment in maize fields remains challenging due to dynamic target variations, imprecise weed localization, and inefficient laser execution. In this study, a closed-loop laser weeding framework was developed for maize fields by integrating visual perception, multi-target tracking, safety-constrained decision making, and priority-based laser scheduling to achieve accurate and efficient weed treatment under dynamic field conditions. The system consists of visual perception, galvanometer control, and laser emission modules. ByteTrack was introduced to achieve stable ID assignment and continuous position feedback for weed targets, thereby reducing repeated ineffective irradiation and energy consumption. A target scheduling strategy constrained by a maize safety zone was further developed. An adaptive elliptical safety zone was constructed to screen candidate weed targets and optimize their priorities. By incorporating safety-zone modeling, galvanometer transition cost, and target urgency, the proposed strategy optimizes the laser striking sequence while reducing crop-injury risk and improving target-selection efficiency. The method was deployed on the developed platform and evaluated through field experiments under different travel speeds and illumination conditions. The results showed that the average weeding rate, maize seedling injury rate, and weed regrowth rate were 83.67%, 2.23%, and 5.23% under three travel speeds, and 82.57%, 2.63%, and 4.87% under three illumination levels, respectively. These results demonstrate that the proposed method enables stable weed tracking and efficient laser weeding while improving real-time performance, operational safety, and intelligent decision making. This study provides a deployable technical solution for precision laser weeding in field applications. Full article

A. tauschii is the wild progenitor that contributed the D genome to common wheat. Since the 1990s, however, it has progressively become a destructive weed in northern China, posing a serious threat to wheat production. This review systematically summarizes current knowledge of A. tauschii, including its taxonomy, population differentiation, mechanisms underlying infestation and crop damage, environmentally sustainable management strategies, and the exploitation of its genetic resources. Two ecotypes have been identified in China: the wild-type and the weed-type. The weed-type spreads rapidly through pathways such as contaminated seed lots. Owing to its highly synchronized life cycle with winter wheat, strong environmental adaptability, prolific reproductive capacity, and superior competitive ability for ecological niches, A. tauschii can rapidly establish populations within wheat fields, resulting in substantial yield losses. Current management strategies emphasize an integrated control system combining primary agronomic practices, chemical control for rapid intervention, and smart agricultural technologies to enhance monitoring and precision management. A. tauschii represents an invaluable reservoir of genes associated with abiotic stress tolerance, disease resistance, and yield-related traits. Future research should integrate advances across multiple disciplines to simultaneously improve weed management and maximize the utilization of this important genetic resource. Full article

While previous reviews have largely focused on individual crops or single target-site mechanisms, the full-chain comparative landscape across major cereal crops remains unexplored. Here, we fill this critical gap by providing the first systematic, cross-crop comparative review that spans herbicide targets, resistance mechanisms, and breeding applications across four major cereals—rice, maize, wheat, and sorghum. Weed infestation is a serious constraint on crop production. Chemical weed control faces challenges such as herbicide resistance evolution and ecological risks. Developing herbicide-resistant varieties is a fundamental approach to achieve green and sustainable weed management. This review systematically summarizes research progress on herbicide resistance genes from three aspects: herbicide classification, resistance mechanisms, and crop breeding applications. It highlights key differences among four major cereal crops (rice, maize, wheat, and sorghum) in resistance-gene discovery and translational progress. Rice has the richest target-site resistance-gene resources. Maize leads in commercialization of transgenic herbicide resistance. Wheat focuses on endogenous precise editing due to genome complexity and regulatory constraints. Sorghum relies on specific mutations to serve cereal–legume intercropping systems. Based on this comparison, this review identifies the core trends in resistance breeding: from single-gene to multi-gene stacking, and from exogenous gene introduction to endogenous gene editing. It also points out common bottlenecks, including insufficient systematic mining of resistance-gene resources, lagging elucidation of non-target-site resistance regulatory networks, and strong genotype dependence in genetic transformation. Future efforts should focus on exploring broad-spectrum resistance genes, optimizing precise editing technologies, and developing sustainable resistance management strategies. This review provides a theoretical framework and practical references for molecular breeding of herbicide-resistant crops. Full article

Accurate weed stem localization is essential for site-specific weed control, including precision spraying, laser weeding, and other targeted weed-control operations. To address species diversity, morphology, and costly multiclass annotation in maize seedling fields, this study proposes a two-stage method based on crop-region exclusion. First, MSDNet, a lightweight YOLOv8n-based maize detector integrating ShuffleNetV2, enhanced feature fusion, coordinate attention, and Wise-IoU loss, detects maize seedlings; pixels within the detected boxes are set to zero. Second, hue–saturation–value thresholding, morphological processing, and area filtering extract vegetation and suppress soil noise. Principal component analysis determines each weed contour’s principal axis, and the image-moment centroid is projected onto this axis to estimate the stem center. MSDNet achieved a mean average precision of 93.4% at an intersection-over-union threshold of 0.5, 8.7 percentage points above the baseline, while reducing parameters by 28.66%. Vegetation segmentation achieved a mean pixel accuracy of 97.6% and a mean intersection over union of 93.8%. Within a 15-pixel tolerance (9.50 mm), stem detection rate and localization precision reached 90.1% and 92.5%, respectively, with a mean localization error of 10.65 pixels (6.74 mm). The proposed method provides visual perception and target-localization support for site-specific weed control while reducing reliance on fine-grained multiclass annotation and species-specific models. Full article

Environmental DNA (eDNA) is a highly sensitive tool widely used in biodiversity studies and for detecting organisms of biosecurity or conservation importance. However, the persistence of eDNA, even after the removal of an organism, can complicate interpretation and lead to false indications of ongoing presence. In contrast, environmental RNA (eRNA) is short-lived and may offer improved spatiotemporal precision for detecting living organisms. In this study, we evaluated the viability of environmental messenger RNA (emRNA) for targeted detection of aquatic weeds, using Amazon frogbit (Hydrocharis laevigata) as a model species. A highly sensitive qPCR assay, targeting the chloroplast rpoB transcript, was used alongside positive and negative controls to ensure workflow reliability. Our results showed that emRNA was undetectable at low density (0.56 plants per litre) and was consistently detectable at very high abundance (over eight plants per litre), although copy numbers were very low. These findings suggest that the effective detection threshold for emRNA may exceed densities typically encountered during early invasion. Consequently, the qPCR-based emRNA approaches evaluated in this study showed limited applicability for biosecurity surveillance, where reliable detection at low abundance is essential. Future research should focus on improving detection sensitivity, including eRNA enrichment. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

This article presents a systematic, evidence-weighted review of Deep Learning (DL) in farming, with a primary emphasis on the agricultural production stage and on four operational domains: precision crop management, precision livestock farming, soil and water resource management, and autonomous agricultural systems. Following a PRISMA 2020-oriented protocol, 56 primary studies (42 with quantitative results) were retained from an initial pool of 18,731 records. The reviewed literature reports applications in plant disease detection, weed recognition, yield prediction, fruit detection, livestock identification and health monitoring, soil-property estimation, crop-water-stress assessment, and robotic perception. High performance on controlled datasets, however, is frequently reported without external, temporal, or cross-site validation, making practical generalisation difficult to establish: in one widely cited benchmark, disease-classification accuracy fell from above 99% on held-out laboratory images to 31.4% on field-acquired images of the same classes. Persistent weaknesses include the limited availability of public benchmarks, inconsistent validation protocols, limited interpretability, fragmented data governance, and insufficient techno-economic analysis. The review argues that the next stage of agricultural AI should be judged less by isolated benchmark scores and more by field realism, reproducibility, deployment maturity, and practical usefulness. Unlike broad surveys that mainly catalogue architectures and applications, this review interprets the literature according to dataset representativeness, validation protocols, benchmarking transparency, deployment realism, and reproducibility, distinguishing algorithmic performance under controlled conditions from practical readiness for real farming environments. The most promising research directions include self-supervised and multimodal learning, explainable and privacy-preserving AI, edge-aware deployment, and hybrid process-informed models. Full article

Orchard production involves intensive labor requirements, limited operational periods, and highly dynamic and complex working environments. Consequently, the development of intelligent orchard robots has become a important approach to enhancing production efficiency and reducing reliance on manual operations. This review focuses on the demands of autonomous robotic systems operating in challenging orchard scenarios and provides a comprehensive overview of key technologies, including environmental perception and semantic cognition, autonomous navigation and environmental modeling, intelligent task execution, and collaborative robotic systems. Recent advances in fruit and blossom detection, branch and canopy structure perception, multi-modal sensor fusion for localization, semantic mapping, robotic harvesting control, variable-rate spraying, precision pollination, and autonomous intra-row weed management are systematically discussed. Furthermore, emerging technologies such as multi-robot coordination, robot–UAV cooperation, large language models (LLMs), and vision-language models (VLMs) for enhancing decision-making capabilities in agricultural robotics are reviewed. Finally, the existing challenges of orchard robots in terms of perception reliability, long-term autonomous navigation, operational robustness, system-level integration, and standardized performance evaluation are analyzed, followed by discussions on potential future research directions. Full article

Weed management in woody perennial crops relies mainly on broadcast herbicide application, with well-documented costs to soil health, biodiversity, and crop physiology. Robotic platforms offer a path toward selective, site-specific control, but orchard environments present challenges such as irregular geometries, trunks, and strong illumination variability under the canopy that have limited fully integrated solutions. This work presents an autonomous robotic platform for selective under-canopy weed control in woody crops, combining multi-sensor perception, a six-degree-of-freedom robotic arm with a mechanical trunk-avoidance mechanism, and a precision spraying module with independently controlled nozzles. A weed image dataset tailored to Mediterranean orchard conditions was built from controlled-cultivation and commercial-orchard imagery under a two-phase training strategy, and the platform was evaluated in a commercial almond orchard in southern Spain. Field trials confirmed the platform’s ability to avoid tree trunks (presenting an average of 1.28% coverage near the tree trunks) and spray only selected targets under typical orchard operation but weed detection accuracy dropped substantially between winter conditions (mAP@0.5 = 93.5%) and summer conditions (mAP@0.5 = 47.8%), with uneven canopy lighting identified as the main cause. These results confirm the technical feasibility of integrating perception, navigation, and actuation into a single autonomous platform, while highlighting robust perception under canopy-induced illumination heterogeneity and tighter perception–navigation integration as the main remaining challenges. Full article

Strigolactones (SLs) are multifunctional plant metabolites that govern shoot architecture, facilitate symbiosis with arbuscular mycorrhizal fungi, and trigger seed germination of parasitic weeds, making them attractive targets for crop improvement. Their agricultural potential has been validated in field trials for parasitic weed suppression, drought resilience, and grain yield improvement. However, a major challenge is decoupling their beneficial effects from undesirable functions. To address this, we adopt a precision intervention framework distinguishing two strategies: functional decoupling, which separates beneficial from detrimental SL activities; and situational decoupling, which exploits detrimental functions in controlled contexts. We evaluate progress across parasitic weed control, abiotic stress mitigation, and agronomic trait optimization. We also identify scientific gaps and practical barriers limiting translation and critically assess emerging solutions to these barriers. By critically analyzing where decoupling works and what trade-offs limit its success, this review aims to guide sustainable implementation of SL-based technologies in agriculture. Full article

Weed segmentation in soybean fields is essential to improving herbicide use efficiency and supporting precision variable-rate spraying. This study developed an SiLU–CPCA–Gate U-Net (SCG-UNet) using fused UAV RGB and multispectral imagery to improve the delineation of small and partially occluded weeds under complex canopy conditions. SCG-UNet integrates channel–spatial feature enhancement, attention-guided skip-feature fusion, and smooth nonlinear activation within a U-Net framework. A total of 400 spatially aligned RGB–multispectral image groups collected from a soybean field in Henan Province, China, were manually annotated for model development and evaluation. Paired bootstrap comparisons showed that RGB+NIR achieved the highest numerical performance among the tested inputs and significantly outperformed RGB, RGB+R, and RGB+G in mIoU after Holm correction, while remaining statistically comparable to RGB+REdge and RGB+NIR+REdge. With RGB+NIR input, SCG-UNet achieved an mPA of 92.35%, an mIoU of 83.43%, a Dice coefficient of 79.50%, and an F1-score of 80.77%, exceeding the baseline U-Net by 0.71, 1.50, 2.19, and 2.09 percentage points, respectively. Five-fold spatial block cross-validation yielded an mIoU of 82.92 ± 0.29% and an F1-score of 80.06 ± 0.40%, indicating stable performance across different regions of the same field. SCG-UNet also achieved the highest numerical mIoU among the evaluated convolutional, high-resolution, and Transformer-based models, exceeding TransUNet and LeViT-UNet by 0.90 and 0.71 percentage points, respectively, while requiring fewer parameters and lower reported memory consumption. The segmentation results were further converted into a conceptual variable-rate spraying prescription map with five spray volume levels ranging from 220 to 300 L/ha. These results demonstrate the potential of RGB–multispectral fusion for soybean weed mapping, although field validation of prescription execution, weed control efficacy, and economic benefits remains necessary. Full article

Sustainable agricultural production requires environmentally friendly weed management solutions. Steam-based thermal weed control is a promising alternative to conventional herbicide-based weed management. However, optimizing steam delivery systems remains computationally expensive due to the repeated simulation-based design evaluations required. This paper presents a design optimization framework for a novel root-targeted steam injection module intended for integration into an autonomous agricultural platform for sustainable thermal weed management. The mechanical behavior of different nozzle geometries was evaluated using finite element analysis using the ABAQUS/CAE software, generating the simulation dataset used for optimization. To reduce the computational cost associated with repeated finite element simulations, a Kriging surrogate model was constructed from this dataset and coupled with an evolutionary optimization algorithm to identify the optimal nozzle geometry. The evaluated nozzle geometries exhibited maximum stresses ranging from 5.12 to 20.41 N/mm². The stress value predicted by the Kriging model for the optimized configuration was validated through an additional finite element simulation, showing a deviation of only 6.6%. Furthermore, an artificial neural network model implemented in PyTorch 2.7.1 was trained on the simulation dataset and used as an independent validation tool to estimate the stress corresponding to the optimized nozzle geometry, with a prediction deviating by approximately 5.4% from the corresponding finite element result. The proposed approach significantly reduces computational cost while maintaining high accuracy in stress prediction, enabling the identification of a structurally reliable nozzle geometry for sustainable herbicide-free thermal weed management. Full article

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

Accurate and robust weed detection is a critical prerequisite for precision agriculture and site-specific weed management. However, real-world agricultural environments pose significant challenges to existing object detectors due to severe illumination variability, high inter-class similarity between crops and weeds, and the prevalence of small and occluded targets at early growth stages. To address these challenges, this paper proposes DSD-YOLOv11, a domain-adaptive and structurally refined detection framework tailored for complex field scenarios. Specifically, a physics-based data augmentation strategy is first introduced to simulate realistic illumination conditions and soil background variations, effectively broadening the training distribution without increasing model complexity. In addition, a lightweight Feature Enhancement Module (FEM) is selectively injected at the P3 detection layer, where high-resolution features are preserved. The FEM integrates a SpatialAttentionLite mechanism with a projection-based feature alignment strategy, enabling precise enhancement of fine-grained spatial cues while maintaining compatibility with pre-trained backbones. An epoch-aware alpha controller is further designed to ensure stable optimization by gradually activating the enhancement pathway during training. Extensive experiments on a real-world agricultural weed dataset demonstrate that the proposed method consistently outperforms baseline YOLOv11 models across multiple evaluation metrics. Notably, DSD-YOLOv11 achieves an absolute mAP@50 improvement of +12.73 percentage points over the native baseline without data augmentation (reaching 87.14%, where the physics-based augmentation contributes +7.94 percentage points and the FEM module contributes an additional +4.79 percentage points over the augmented YOLO11n baseline), while operating at 84.2 FPS on a desktop GPU (NVIDIA RTX 4090; NVIDIA Corporation, Santa Clara, CA, USA) and 7.2 FPS on an edge computing platform (NVIDIA Jetson Nano; NVIDIA Corporation, Santa Clara, CA, USA) with only marginal parameter increases. These results indicate that the proposed framework provides an effective and efficient solution for weed detection in unstructured agricultural environments. Full article