Search Results (384)

Labor shortages and the push for higher greenhouse efficiency have accelerated interest in automated harvesting. However, the development of a universal harvesting robot has been constrained by large variations in crop architecture, especially plant height. In this review, a height-based stratification of greenhouse harvesting robots and transferable high-wire crop harvesters is presented, covering ground-level crops (<0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato), and high-wire crops (>1.5 m, e.g., trellised cucumber). For each height layer, key design features, technical progress, prototype performance, and common obstacles—including fruit occlusion, mechanical crop damage, unreliable operation, and high commercial costs—are analyzed. Future efforts should target intelligent perception, soft end-effectors, and height-specific solutions (swarm robotics for ground crops, modular hybrid designs for medium crops, infrastructure co-design for high-wire crops). By using plant height as the primary stratification criterion, a design-oriented framework is provided, distinct from conventional crop-type or mechanism-based categorizations. Full article

Artificial light at night (ALAN) and the broader use of artificial light in agriculture are increasingly recognized to affect the physiology and behavior of nocturnal insects, yet their influence on pheromone-mediated reproductive communication in economically important insect pest species remains poorly understood. Here, we investigated the effects of artificial illumination on calling behavior, sex pheromone production, blend composition, and mating success in the fall armyworm Spodoptera frugiperda, under both laboratory and semi-field conditions. Virgin females were exposed during the scotophase to one of four wavelength-specific treatments, including dark control, red (620 nm), green (520 nm), or blue (460 nm) LED light. Female calling behavior occurred during the early-to-mid scotophase under dark and red light conditions, while it was significantly delayed and suppressed under blue and green light. Specifically, blue light suppressed production of the key pheromone component (Z)-9-tetradecenyl acetate (Z9-14:Ac) and enhanced (Z)-11-hexadecenyl acetate (Z11-16:Ac), altering the species-specific pheromone blend composition. Mating success was significantly reduced under blue light in both laboratory and semi-field experiments. Under controlled laboratory conditions, green light also affected mating success, but this effect was not significant in the semi-field experiment. Blue light imposed the strongest inhibitory effect, reducing mating success by 39% relative to the dark condition. Red light produced minimal effects in all measured parameters, consistent with reduced sensitivity to longer wavelengths in nocturnal moths. These results demonstrate that wavelength-specific artificial light can disrupt several components of the reproductive communication system in S. frugiperda and suggest that manipulation of artificial light may offer a non-chemical approach for disrupting mating behavior in economically important insect pest species within integrated pest management programs. Full article

Porcine somatic cell nuclear transfer (SCNT) demonstrates significant potential for application in biomedical research, agricultural breeding, and xenotransplantation. However, compared to in vivo fertilized embryos, SCNT embryos exhibit suboptimal developmental capacity, higher rates of abortion, and neonatal abnormalities, which collectively hinder the large-scale application of this technology. Such developmental anomalies arise from a complex interplay of interconnected factors, primarily incomplete epigenetic reprogramming (the core driver), telomere shortening, mitochondrial dysfunction, and technical manipulation-related damage—all of which disrupt the spatiotemporal regulation of gene expression and embryonic lineage commitment. This article provides a critical and integrated review of porcine SCNT research, establishing a coherent conceptual framework that links biological mechanisms, developmental defects, diagnostic tools, and improvement strategies. We emphasize mechanistic insights into key barriers, evaluate the efficacy and limitations of existing diagnostic and therapeutic approaches, and highlight pig-specific biological features that distinguish porcine SCNT from other mammalian models. This review aims to offer a novel perspective on unresolved questions in the field and provide a rigorous reference for in-depth studies on porcine SCNT embryo development. Full article

To address the evaluation requirements for agricultural robotic harvesting, this work presented a fruit-grasping assessment system based on tactile fusion analysis. Four piezoresistive pressure sensors were symmetrically integrated into the inner surfaces of a flexible gripper. A signal-conditioning circuit and a data acquisition module transmitted tactile signals to a Transformer–Mamba fusion network for feature extraction and target classification. After being trained on a dataset comprising 300 samples, the model extracted deep tactile features to distinguish among three target categories: citrus fruits, branches, and leaves. Classification outputs generated control commands for a robotic manipulator, enabling obstacle-avoidance retraction and precise harvesting operations. Experimental evaluations, conducted in both indoor and outdoor environments, demonstrated a target recognition accuracy of 93.12%. The manipulator response time was below 0.5 s, and the operational success rate was 90%. The proposed sensing system and algorithmic framework showed strong adaptability and supported quantitative assessment of grasping performance. The fruit detachment, compression damage, and plant-collision risks were effectively reduced while operational stability and harvesting efficiency improved. Full article

Soil microbiomes are essential for nutrient cycling, plant health, stress resilience, and sustainable agriculture. Recent advances in high-throughput sequencing, multi-omics technologies, systems biology, and artificial intelligence (AI) have transformed our understanding of plant–microbiome interactions and enabled the development of innovative microbiome engineering strategies. This review provides a comprehensive overview of the mechanisms governing plant-associated soil microbiome assembly, microbial community functions, plant–microbe communication, and microbiome-mediated stress resistance in agricultural ecosystems. Current approaches to plant-associated soil microbiome manipulation and engineering, including microbial inoculants, synthetic microbial communities (SynComs), microbiome transplantation, rhizosphere steering, and synthetic biology-based interventions, are critically examined. The review further discusses the growing role of metagenomics, metabolomics, metatranscriptomics, machine learning (ML), and precision agriculture technologies in improving microbiome characterization, prediction, and management. Particular attention is given to the application of microbiome-based solutions for sustainable crop production, nutrient management, biological control, climate-smart agriculture, and ecosystem restoration. Despite significant progress, challenges related to field-scale variability, colonization stability, biosafety, regulatory frameworks, and data integration continue to limit large-scale implementation. Future advances in precision microbiome engineering are expected to combine ecological principles, multi-omics technologies, AI, and synthetic biology to develop predictive and resilient microbiome-based solutions for sustainable and climate-resilient agriculture. Full article

The automation of fruit harvesting has become an important research topic in precision agriculture due to increasing labor shortages, rising production costs, and the need for improved harvesting efficiency. Among horticultural crops, strawberries present particular challenges for robotic harvesting because of their variability in size, shape, ripeness, and frequent occlusions caused by leaves and surrounding fruit. The objective of this work is to demonstrate the feasibility of a reproducible perception-to-manipulation framework for robotic strawberry harvesting based on commercially available hardware and established computer vision techniques, rather than to propose a novel object detection algorithm. The proposed system integrates a YOLOv3-based (You Only Look Once) object detector, monocular vision for fruit localization, and a Universal Robots UR5e collaborative manipulator. Strawberry coordinates estimated from monocular images are transformed into the robot reference frame and transmitted through the XML-RPC (Extensible Markup Language-Remote Procedure Call) protocol, enabling robot positioning. The system was experimentally validated in a controlled indoor environment under different artificial illumination conditions. The YOLOv3 detector achieved a ${\mathrm{mAP}}_{0.5:0.95}$ of 37.4%, a precision of 84.2%, a recall of 76.1%, and a latency of 6.5 ms per image (153.8 FPS). The experiments also demonstrated reliable communication between the perception and robotic manipulation modules, enabling the robotic arm to reach the estimated strawberry positions. The proposed framework provides a practical and low-cost solution for integrating deep-learning-based perception with robotic manipulation and establishes a solid basis for future work on localization accuracy, automated grasping, harvesting efficiency, and deployment in real agricultural environments. Full article

A deeper understanding of the molecular mechanisms shaping plant development is relevant for enhancing agricultural productivity. Among these mechanisms, the regulation of gene expression plays a central role in determining phenotypes and plasticity. This specificity is largely mediated by transcription factors (TFs) that bind cis-regulatory elements (CREs), short DNA sequences dispersed within intergenic regions. Despite their key roles, CRE function remains incompletely understood. However, growing evidence indicates that variation in CREs has contributed to crop domestication, adaptation, and trait diversification. This review highlights the role of CRE variation in shaping agronomic traits, discusses current approaches for CRE identification with a focus on multi-omics strategies, and examines recent genome-editing technologies for CRE manipulation and their potential applications in crop improvement. Full article

A dual-platform mushroom cultivation system integrating artificial intelligence (AI)-assisted environmental monitoring and controlled-environment agriculture (CEA) was developed to support experiential STEM education across K–12 and undergraduate settings. Hands-on instruction with multicellular fungi is often limited by reliance on microbial models and by constraints associated with field-based activities. To address this gap, we implemented an indoor instructional platform that combines a commercial AI-assisted automated cultivation unit with a tent-based chamber for hands-on environmental control. Representative cultivated species included oyster mushrooms (Pleurotus spp.) and lion’s mane (Hericium erinaceus). The AI-assisted system provided sensor/camera-based monitoring, app-based feedback, and software-assisted regulation of humidity, light, and airflow, whereas the tent-based system enabled direct student manipulation of cultivation conditions. Together, the systems allowed students to observe fungal development, manage environmental parameters, and collect quantitative and qualitative data within a single academic term. Post-harvest activities, including mushroom-based food preparation and tasting, further connected fungal biology with food and sustainability. A matched pre- and post-course survey (n = 30) showed increases in students’ self-reported perceived understanding, cultivation confidence, and engagement, with mean scores increasing from approximately 2–4 to 6–8. Because the survey instrument was not formally validated and no control group was included, these results are interpreted as preliminary self-reported perceptions rather than objective evidence of learning gains. The platform provides a practical model for integrating fungal biology, AI-assisted environmental monitoring, and CEA into STEM education. Full article

Reliable detection of small agricultural targets is fundamental to precision crop protection, phenotyping, yield estimation, and robotic intervention. Typical examples include detecting aphids such as Aphis gossypii, whiteflies such as Bemisia tabaci, planthoppers such as Nilaparvata lugens, and other tiny pests on sticky traps or crop canopies for early warning, identifying crop-like weed seedlings for site-specific herbicide spraying, locating early disease lesions for targeted treatment, and detecting young fruits, flowers, or wheat heads for yield estimation and robotic manipulation. Agricultural small-object detection differs from generic small-object detection because target visibility is jointly determined by pixel area, physical size, imaging distance, ground sampling distance, canopy structure, biological similarity, and task-specific intervention requirements. Existing reviews have summarized agricultural object detection or general small-object detection, but they rarely connect agricultural failure modes with detector-level mechanisms and reproducible evaluation practices. This review addresses this gap through a mechanism-oriented synthesis of agricultural small-object detection. First, we revisit the limitations of the COCO-style 32²-pixel threshold and propose an agricultural scale-reporting framework that combines pixel area, physical scale, relative image occupancy, and acquisition geometry. Second, we organize recent methods according to the mechanisms by which they address detail loss, scale shift, occlusion, dense distributions, foreground–background confusion, localization uncertainty, and edge-deployment constraints. Third, we summarize public datasets, quantitative evaluation metrics, reporting checklists, and real-device deployment evidence to support fair and field-oriented comparison. Finally, we identify future directions in multimodal sensing, foundation-model adaptation, label-efficient learning, and hardware-aware optimization. By linking agricultural scene characteristics, detector mechanisms, and evaluation requirements, this review aims to provide a more actionable framework for developing robust small-object detection systems in precision agriculture. Full article

Stereo vision is essential for passive three-dimensional perception in resource-constrained applications that require low power consumption, predictable latency, and explainable geometry. Although deep learning architectures dominate recent benchmarks, the classical block-matching pipeline remains a foundational approach. Optimizing this pipeline involves navigating complex trade-offs among matching robustness, map density, and computational efficiency. This study systematically surveys and physically validates the classical stereo framework. After revisiting geometric first principles, three matching costs (SAD, NCC, ZNCC) are benchmarked alongside Sobel preprocessing and structural refinements, with subsequent validation using a calibrated consumer webcam rig. Middlebury benchmarks (2001–2021) indicate that while SAD fails under complex radiometric distortion, NCC consistently achieves superior quantitative metrics, incurring only a 1.2-fold computational overhead. Extending the disparity search range improves foreground localization, while block size imposes a trade-off between resolving the aperture problem and preserving fine geometric detail. To bridge theoretical analysis and practical deployment, the pipeline is validated using a custom-calibrated consumer stereo rig. The optimized Sobel-NCC architecture is then evaluated for real-time edge deployment on constrained hardware (NVIDIA Jetson Nano) and narrow-baseline sensors (OAK-D SR) in the context of agricultural robotic manipulation. By prioritizing metric precision over dense prediction, the classical pipeline reconstructs target surfaces with approximately 1 cm depth accuracy at 21 frames per second. These results demonstrate that optimized local algorithms offer deterministic and reliable geometric foundations for real-time edge-computed robotics. Although neural networks are essential for dense reconstructions in ill-posed regions, the foundational principles established here remain indispensable for advanced stereo vision system deployment. Full article

Accurate and scalable urban land cover mapping is critical for sustainable urban planning and environmental management. While deep learning models offer powerful tools for this task, their performance is often constrained by the need for vast, manually labeled datasets, which are costly and challenging to acquire for diverse urban environments. To address this limitation, we propose the Cross-Scale U-Net, an original, highly adaptable operational framework that systematically exploits the inherent scale effects of remote-sensing imagery to optimize transfer learning. By operationalizing prior theoretical findings on receptive fields, this workflow provides an actionable method for users to manipulate spatial resolution, identify an optimal scale to bridge the domain gap, and subsequently automate feature extraction with significantly reduced manual effort. Using the well-annotated ISPRS Potsdam dataset as the source domain, our framework transfers learned knowledge to classify National Agriculture Imagery Program (NAIP) data from Phoenix, AZ (2015), into four primary land cover classes. We systematically evaluated the framework’s performance across spatial resolutions ranging from 15 cm to 100 cm, achieving a peak overall accuracy (OA) of 82.45%. To assess generalizability, the model was applied in a label-free transfer scenario to NAIP imagery from Las Vegas, NV (2015), and Phoenix, AZ (2013 and 2019), consistently delivering OA values above 70%. In a comparative analysis, the Cross-Scale U-Net significantly outperformed traditional classification techniques. While our current empirical validation is focused on arid urban environments due to experimental constraints, the framework introduces a highly flexible, actionable scale-adjustment process. This approach offers a scalable workflow that can be tailored to various landscape scales—such as expanding to coarser resolutions for large-scale forests or protected areas—delivering high-fidelity maps while mitigating data scarcity. Full article

Purple non-sulfur bacteria (PNSB) have attracted attention as a group of microorganisms with plant growth-promoting abilities. Notably, agriculturally important PNSB, including members of the genera Rhodopseudomonas and Rhodobacter, are classified within the class α-Proteobacteria. However, molecular visualization of these bacteria in plant tissues remains challenging without bacterial genetic manipulation. In this study, a DIG-labeled RNA probe was developed from a 16S rRNA region showing relatively high sequence conservation among the tested α-Proteobacterial strains. Northern blot analysis using RNA extracted from 13 bacterial strains demonstrated preferential hybridization of the probe to the tested α-Proteobacterial strains. Then, in situ hybridization (ISH) of fixed bacterial cells produced positive signals only in the tested α-Proteobacterial strains. Finally, after inoculation of Rhodopseudomonas palustris C2 during rice seed priming and seedling hydroponic cultivation, ISH analysis of rice roots revealed probe-positive bacterial structures in root epidermal and root hair-associated regions. Collectively, these results demonstrate the applicability of the developed RNA probe for molecular visualization from extracted RNA and bacterial cells to rice root tissues under controlled inoculation conditions and provide a useful approach for investigating bacterial localization and plant–bacteria interactions in rice roots. Full article

Arthropod natural enemies—encompassing predators and parasitoids—form the backbone of sustainable agriculture, delivering irreplaceable ecosystem services via biological pest suppression. Driven by global demand for eco-friendly alternatives to synthetic pesticides, research in this domain has grown sharply over the past decade. Here, we report a systematic bibliometric analysis of 6515 Web of Science Core Collection papers focused on arthropod natural enemies in biological control (2016–2025), with the goal of charting the field’s intellectual structure. Performance metrics confirmed an initial rapid increase from 2016 to 2019 followed by a plateau and a slight rise in 2025, with the US, China, and Brazil dominating output. Keyword co-occurrence networks pinpointed core themes, including conservation biological control, predatory mites, and integrated pest management (IPM). Temporal trends further revealed a pivot toward applied work on invasive pest systems. Co-citation analysis uncovered six foundational research clusters, while bibliographic coupling of 2021–2025 papers uncovered five active emerging subfields: landscape ecology and habitat manipulation, tri-trophic interaction mechanisms, high-impact invasive pest biocontrol, non-target risk assessment for introduced agents, and fall armyworm integrated management. We synthesize cross-cutting implications and outline future priorities—including AI-enabled rearing systems, functional biodiversity boosting, climate adaptation, and multifunctional landscape tuning. By consolidating historical progress and forward-looking directions, this framework empowers researchers, extension practitioners, and policymakers to scale sustainable pest management worldwide. Full article

Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications. Full article

This review provides a comprehensive synthesis of robotic fruit harvesting systems, with a particular focus on the system-level integration of perception, manipulation, and fruit detachment within autonomous harvesting environments. Recent advances in machine vision, deep learning, sensor fusion, robotic end-effectors, grasping strategies, and motion planning are critically analyzed alongside cutting, pulling, and vibration-based detachment mechanisms under unstructured orchard conditions. Beyond component-level analysis, this review emphasizes the critical role of perception–action coupling and highlights key system integration challenges, including localization errors, perception-to-action latency, and environmental variability, which continue to limit reliable field deployment. In addition, orchard and pre-harvest-related factors such as canopy structure, fruit distribution, and detachment force variability are examined in relation to their direct impact on system performance, robustness, and harvesting efficiency. Furthermore, the review extends toward system-level considerations by incorporating performance evaluation metrics, economic feasibility, and scalability constraints, which are essential for transitioning robotic harvesting systems from experimental prototypes to commercially viable solutions, including practical field deployment in distributed and multi-robot harvesting systems. Emerging technologies, including artificial intelligence, advanced sensing, digital agriculture, and energy-aware system design, are discussed as key enablers for achieving adaptive, data-driven, and scalable autonomous harvesting. The novelty of this work lies in proposing an integrated framework that explicitly links perception, manipulation, and detachment with orchard-level constraints and deployment requirements, thereby bridging the gap between algorithmic advancements and real-world implementation of autonomous fruit harvesting systems. Full article