Search Results (254)

Unmanned aerial vehicles (UAVs) are becoming an important platform for precision agriculture, supporting both high-throughput sensing and active field operations such as spraying, monitoring, and phenotyping. However, unlike general UAV applications, agricultural environments impose distinctive challenges due to heterogeneous field structures, canopy occlusion, terrain variation, dynamic disturbances, and strong coupling between navigation performance and task quality. To address this gap, this review presents a systematic analysis of UAV navigation in agricultural environments from a system-level perspective. The review first summarizes the core technical components of agricultural UAV navigation, including sensing, localization, mapping, planning, and control. It then discusses how navigation requirements vary across representative scenarios such as open fields, orchards, and terraced farmland, and examines their roles in key applications including aerial mapping, field monitoring, precision spraying, and close-range orchard operations. In addition, datasets, simulation platforms, and evaluation protocols relevant to agricultural UAV navigation are reviewed. Finally, major challenges are identified, including scene heterogeneity, perception degradation, insufficient task-semantic integration, limited control robustness, and the lack of standardized benchmarks. Future research should move toward robust, task-aware, and modular navigation architectures that support reliable and scalable agricultural UAV deployment. Full article

Crop simulation models and irrigation decision support systems (IDSS) are essential tools for improving water use efficiency, particularly in Mediterranean and semi-arid regions where water scarcity is a major constraint. However, many platforms are either too complex for widespread adoption or too simplified to capture the combined effects of temperature, water stress, and elevated CO₂ on crop responses. This paper presents the Easy Simulator Crop Model (EaSiCroM), a modular, low-parameterisation system designed to simulate daily crop growth, soil water dynamics, and irrigation requirements. Canopy development follows a beta-function LAI trajectory with Beer–Lambert canopy cover, progressively constrained by temperature (T_lim) and water stress (K_stress, KS_cc). Biomass accumulation combines a water productivity (WP) approach with an optional radiation-use efficiency (RUE) pathway, both scaled by a Michaelis–Menten CO₂ fertilisation sub-model. The soil water balance includes a two-stage bare-soil evaporation formulation and multiple irrigation triggering strategies. EaSiCroM is implemented as a Docker-containerised web application supporting single-crop, multi-plot, and near-real-time irrigation modes, with optional assimilation of user-provided canopy observations from field or remote sensing sources. A proof-of-concept evaluation across four Mediterranean crops (processing tomato, biomass sorghum, sunflower, and durum wheat) yielded RRMSE values between 13.8% and 26.1%, comparable to AquaCrop and CropSyst on the same datasets. Its modular architecture makes it suitable for both research and operational irrigation management in water-scarce environments. Full article

Sowing space and depth critically influence wheat canopy architecture, yet their layer-specific effects remain poorly understood. This two-year field study evaluated the effects of three sowing spaces (1.5, 3.0, 4.5 cm) and three sowing depths (2, 3, 6 cm) on canopy projection area, leaf inclination angle, leaf area distribution, and leaf area index (LAI) of dryland wheat (Triticum aestivum ‘Ningmai 13’) in Luhe, Nanjing, China, using image-based phenotyping with manual validation. Narrow spacing (1.5 cm) with intermediate depth (3 cm) produced the largest canopy projection area (0.239–0.245 m²) and an increase in leaf erectness in the middle canopy layer (+23% above average). The highest LAI values (4.23–4.28 m² m⁻²) were achieved with narrow spacing (A₁B₁, A₁B₂), demonstrating that dense canopies can be established under dryland conditions. Grain yield (g/plant) was measured as a supporting agronomic indicator; the highest yield per plant (14.36 g/plant) was observed in A₃B₁. Image-based measurements showed excellent agreement with manual methods (R² > 0.97 for all traits), validating the phenotyping pipeline. These findings contribute to a deeper understanding of how sowing parameters shape wheat canopies in dryland systems. Full article

Soybean is a vital crop supporting global food, feed, and biofuel production. Soybean yields have surged, with record yields reaching 14,678 kg/ha⁻¹, though average farm yields remain stagnant at 2770–2790 kg ha⁻¹. The persistent yield gaps leave 44% of potential production unrealized due to climate change, threatening food security. To meet future caloric demands, which are projected to rise by 46.8% by 2050, soybean breeding must prioritize climate-resilient, high-yielding varieties with minimal ecological footprints. In this comprehensive and in-depth review, we synthesized existing literature and Google Patents and reviewed the multifaceted impacts of climate-change driven eCO₂ and stresses (heat, drought, flooding, salinity, and pathogens), revealing non-linear interactions where eCO₂ may not compensate yield losses under combined stresses. We then highlight key strategies for soybean breeding under climate-change scenario. To this regard, we provide a detailed trait-by-trait breeding roadmap covering seed number, seed size, seed weight, protein-oil balance and their metabolic trade-offs, above and below ground plant architecture, nitrogen fixation and nodulation dynamics, root system architecture, water use efficiency, canopy architecture, flowering time regulation, early maturity etc., in light of specific genes and validated strategies. We explicitly discuss the novel strategies including deeper understanding of traits, abiotic stress physiology, changing pathogen dynamics, phenomics, (multi-)omics, machine learning, and modern biotechnological techniques for developing future soybean varieties. We provide a future roadmap prioritizing specific actions, including engineering climate-resilient ideotypes through gene stacking, optimizing nitrogen fixation and nutrition under stresses leveraging omics data, pan-genome, wild soybean, speeding breeding hubs, and participatory farmer-network validation, while redefining the future soybean breeder would be a hybrid orchestrator of data and dirt. This review establishes a foundational framework for translating climate-adaptive morphological, biochemical, physiological, omics, agronomic, phenomics, and biotechnological insights into actionable breeding strategies, thereby guiding policy-driven investment in soybean improvement programs targeting 2050 and beyond. Full article

The complex surface architecture of natural oyster reefs is widely considered to promote biological attachment, yet the underlying mechanisms and the relevance to the design of artificial reefs are not fully understood. Here, we combined field experiments, 3D surface characterization, and numerical modelling to quantify how reef-like roughness regulates biofouling development and near-wall flow around artificial substrates. Surface morphological characteristics of natural oyster reefs were first obtained by 3D scanning and used to fabricate concrete panels with simulated rough textures, while traditional smooth concrete panels served as controls. The two types of panels were simultaneously deployed in the target sea area for a hanging-panel experiment. Samples were collected after 3, 6, 9, and 12 months to track changes in biofouling communities. At each sampling time, the panel surfaces were quantified by canopy roughness ($R_C$), surface heterogeneity ($\sigma$), and fractal dimension ($D$), and these metrics were integrated into numerical simulations combined to resolve the flow field, turbulence kinetic, and near-wall shear stress around the colonized panels. The research results show that, after 12-month immersion, the mean thickness of the biofouling layer on rough and control panels reached 6.39 mm and 5.91 mm, respectively. Rough panels exhibited consistently higher $R_C$ and $\mathsf{\sigma }$ than controls, and these two parameters are strongly linearly correlated ($R^2=0.891)$. Numerical simulations reveal that increased $R_C$ enlarges the oyster settlement shear-stress window (OSSW), indicating more favorable hydrodynamic conditions for oyster settlement and growth on rough panels. Nevertheless, the hydrodynamic differences between the initial rough panels and control panels gradually diminish over time, suggesting that biological growth can progressively naturalize initially smooth substrates. These findings advance the mechanistic understanding of how small-scale roughness and biofouling co-evolve to shape oyster habitat quality and provide a quantitative basis for the eco-engineering design of artificial oyster reefs. Full article

Autonomous navigation is essential for orchard transportation robots to support automated operations and precision orchard management. However, in trellis orchards, dense vegetation and complex canopy structures often degrade the stability of GNSS-based navigation in in-row environments. To address this issue, this study proposes a GNSS–vision integrated navigation framework for orchard transportation robots. The performance of GNSS-based navigation in out-of-row environments and vision-based navigation in in-row environments was experimentally evaluated under representative orchard operating conditions. In out-of-row areas, the robot employs GNSS-based path planning and trajectory tracking to achieve reliable navigation in relatively open, lightly occluded environments. During in-row navigation, a deep learning-based real-time object detection approach is used to detect tree trunks and trellis supporting structures. By integrating corner-point selection with temporal RANSAC-based line fitting, a stable orchard row structure is constructed to generate robust navigation references. The visual perception module serves as the front-end sensing component of the navigation system and is designed to be independent of specific object detection architectures, allowing flexible integration with different real-time detection models. Field experiments were conducted under various orchard layouts and growth stages. The average lateral deviation of GNSS-based navigation in out-of-row scenarios ranged from 0.093 to 0.221 m, while the average heading deviation of in-row visual navigation was approximately 5.23° at a robot speed of 0.6 m/s. These results indicate that the proposed perception and navigation methods can maintain stable navigation performance within their respective applicable scenarios in trellis orchard environments. The experimental findings provide a practical and engineering-oriented basis for future research on automatic navigation mode switching and system-level integration of orchard transportation robots. Full article

Background/Objectives: Plant architecture is a critical determinant of high-density tolerance and yield potential in maize (Zea mays L.), yet the genetic networks orchestrating these complex traits require deeper elucidation. Methods: In this study, we utilized a Multi-parent Advanced Generation Inter-cross (MAGIC) population comprising 935 recombinant inbred lines (RILs) derived from 16 diverse elite founders. A comprehensive phenotypic characterization of six pivotal architectural traits—plant height (PH), ear height (EH), ear leaf length (LL), ear leaf width (LW), tassel main axis length (TL), and tassel branch number (TBN)—was conducted across three distinct agro-ecological environments. Results: Phenotypic analysis revealed substantial natural variation and high broad-sense heritability (H² ranging from 60% to 86%), with TBN exhibiting the most pronounced variability. Correlation architecture demonstrated a strong coupling between vertical growth traits (PH and EH, r = 0.73), while lateral leaf expansion (LW) and tassel complexity (TBN) showed significant genetic independence. Using a mixed linear model (MLM) for genome-wide association studies (GWAS), we identified 21 significant SNP–trait associations, including distinct chromosomal clusters on chromosome 8 for EH and chromosome 7 for TBN. By integrating genomic intervals with tissue-specific expression profiling, 23 core candidate genes were prioritized. Notably, Zm00001d042528 (FAS1), involved in chromatin assembly, was implicated in modulating meristematic cell division for plant stature. Other key regulators included Zm00001d020537 (O5) and Zm00001d025360 (F-box protein), which were associated with reproductive organ development and leaf elongation, respectively. Conclusions: These results indicate that maize plant architecture is regulated by a modular genetic framework, with specific loci independently regulating canopy structure and source–sink components. It should be noted that the findings of this study are based solely on statistical models identifying significant associations between genetic loci and phenotypes; the biological regulatory functions of the candidate genes have not yet been experimentally validated. Nevertheless, this study provides new insights into the molecular mechanisms underlying maize morphogenesis and lays a solid theoretical foundation for molecular design breeding aimed at developing high-yielding varieties tolerant of high planting densities. Full article

Cane density is a key management factor in raspberry production, directly affecting yield formation and canopy structure. However, most previous studies have focused on floricane cultivars and relied on conventional field measurements, while the response of primocane raspberries and their canopy level dynamics remain less explored. The objective of this study was to evaluate how cane density influences yield components, cane growth, and canopy structure in primocane raspberry cultivars, and to assess whether these effects can be captured using UAV-based multispectral imaging. Field experiments were conducted over two growing seasons using two primocane cultivars grown under different cane density treatments. Yield components and cane growth parameters were measured, and repeated drone multispectral surveys were performed during the production period to quantify the spatial and temporal variability of vegetation indices. Increasing cane density led to higher total yield per unit area in both cultivars, mainly through an increase in fruit number rather than fruit weight, indicating a compensatory yield response. Cane density significantly modified canopy architecture, with responses varying between cultivars and seasons. Multispectral vegetation indices revealed predominantly consistent density-dependent gradients, characterized by higher mean values and reduced spatial and temporal variability at higher cane densities. Denser cane configurations were associated with lower total temporal amplitude and smoother seasonal trajectories, indicating a stabilization of canopy reflectance dynamics. Although this overall pattern was preserved across indices, the magnitude and regularity of temporal responses were index-specific and cultivar-dependent. The results demonstrate that cane density management in primocane raspberries affects both yield formation and canopy structure, and that these effects can be effectively monitored using UAV-based multispectral imaging. Integrating remote sensing with field measurements offers a valuable approach for supporting data-driven optimization of raspberry production systems. Full article

The strategic choice of training system is essential for adapting viticulture to current climate change, ensuring a balance of physiological efficiency and the sustainability of productivity and oenological quality. This study evaluated the effects of vertical shoot positioning and foldable lyre systems (set at angles of 20°, 30° and 40°) on the physiological performance and yield of ‘Merlot’ grapevines. The experiment was conducted in a humid temperate region in Brazil over two consecutive seasons. The experiment followed a randomized block design. The variables evaluated included: the number of clusters per shoot, cluster weight, pruning weight, Ravaz Index, leaf area and yield; gas exchange parameters such as net CO₂ assimilation rate, stomatal conductance, transpiration rate, rubisco carboxylation efficiency, intercellular CO₂ concentration and photosynthetic photon flux density; and chemical composition of berries such as pH, Total Soluble Solids and Titratable Acidity. The data were subjected to an analysis of variance, and the means were compared using Tukey’s test at a 5% probability level. The results indicated that canopy architecture significantly influenced solar radiation interception, with the 30° and 40° foldable lyre systems achieving the highest mean daily radiation levels, exceeding the vertical positioning system by 73.7% and 76.6%, respectively. Although gas exchange at the leaf level remained comparable across all systems, agronomic performance varied considerably. The 40° foldable lyre system achieved the highest yield (22.99 t ha⁻¹), representing a 63.1% increase over the vertical positioning system (14.10 t ha⁻¹). The number of buds in the foldable lyre systems increased by around 70%, which is closely in line with the observed increase in yield. In addition, the foldable lyre systems provided about 40% more leaf area than the vertical positioning system. These findings suggest that divided canopy systems, such as foldable lyre systems, particularly at 30° and 40°, optimize bud load, fruitfulness per shoot, light interception and significantly increase yield without compromising individual physiological efficiency and berry chemical composition, with a balance between vegetation and fruit load preserved and with positive effects on the ripeness and quality of the grapes. Full article

Accurate and timely cotton-field mapping is essential for irrigation management, water resource allocation, and regional yield assessment in arid irrigated agroecosystems. However, existing deep-learning-based crop mapping approaches generally lack interpretability and often exhibit performance variability across phenological stages, thereby limiting their reliability for operational deployment. To address these limitations, we developed an interpretable semantic segmentation framework for cotton mapping in the Wei-Ku Oasis, Xinjiang, China, under multi-source remote sensing conditions. The proposed model integrates Sentinel-2 surface reflectance, Sentinel-1 VV/VH backscatter, DEM, vegetation indices, and GLCM texture features. By incorporating a receptive-field enhancement mechanism together with an embedded feature-attribution module, the framework enables importance estimation of multi-source predictors within the network architecture, thereby providing intrinsic model interpretability. Under a unified training and evaluation protocol, the proposed model achieved an mIoU of 85.62% and an F1-score of 92.96% on the test set, outperforming U-Net, DeepLabV3+, and SegFormer baselines. Monthly classification results indicated that August provided the most discriminative acquisition window (mIoU = 85.54%, F1 = 92.83%), while June–July also maintained high recognition accuracy. Feature attribution results indicate that the importance of different predictors varies across phenological stages: Sentinel-2 red-edge bands remained highly influential throughout the growing season, NDVI/EVI exhibited increased contributions during June–August, SAR VH showed relatively higher importance during peak canopy development, and DEM maintained stable information contribution across all stages. Cross-year and cross-region experiments further demonstrated the model’s generalization capability, achieving an mIoU of 82.81% in same-region cross-year evaluation and 74.56% under cross-region transfer. Overall, the proposed segmentation framework improves classification accuracy while explicitly modeling and quantifying feature importance, providing a methodological reference for cotton-field mapping and acquisition timing selection in arid irrigated regions. Full article

Growth-Regulating Factors (GRFs) are plant-specific transcription factors that, together with GRF-Interacting Factors (GIFs) and under post-transcriptional control by miR396, coordinate cell proliferation and expansion to define organ size. This GRF–GIF–miR396 regulatory module holds major agronomic importance, shaping leaf architecture, source–sink relationships, nitrogen-use efficiency (NUE), and stress resilience in crops. Upregulation of specific GRF genes has been shown to enhance leaf width, yield potential, and other important agronomic traits. Synthetic GRF–GIF chimeras have revolutionized regeneration and genome editing in multiple crop species, revealing both successes and species-specific limitations. Expanding GRF/GIF gene families and functional analyses across various crops highlight conserved developmental functions with variable outcomes, including improved drought and salinity tolerance through sustained canopy growth. This review, focused on crop systems, integrates current advances in GRF-regulated leaf development, their contributions to abiotic and biotic stress adaptation, and the emerging utility of GRF–GIF chimeras. Finally, it outlines key challenges and future opportunities for leveraging GRFs in designing climate-resilient, high-efficiency crop ideotypes. Full article

The intensification of Global Warming and Urban Heat Island phenomena necessitates advanced, computationally effective tools for evaluating outdoor thermal comfort and microclimatic dynamics by means of Mean Radiant Temperature assessment. However, existing high-resolution physical models often suffer from prohibitive computational costs. This research proposes LUCIDiT (Lean Urban Comfort Intelligent Digital Twin), a physically based modeling framework implemented for a quick mean radiant temperature assessment inside complex urban morphologies. The method integrates a simplified balance of mutual radiative heat exchanges with recursive time-series filtering to account for the thermal inertia of different urban materials, alongside greenery heat exchange due to evapotranspiration. This architecture creates an operational urban comfort digital twin that reduces computational times by orders of magnitude for large-scale mappings, without sacrificing physical accuracy. Validation against drone-acquired thermographic data and the established Urban Multi-scale Environmental Predictor model demonstrates high reliability and coherence with the real physical phenomena and context. The application to an urban pilot site in Florence reveals that strategic interventions, such as substituting impervious surfaces with irrigated greenery and arboreal canopies, can mitigate radiant loads by up to 20 °C. Findings show that the proposed urban comfort digital twin can be a robust, scalable instrument for designing evidence-based climate adaptation strategies and quick testing mitigation scenarios to enhance urban resilience. Full article

While climate change shifts plant phenology, conventional satellite-based studies often overlook intra-individual variations due to spatial averaging. This study utilized high-resolution UAV imagery and Digital Surface Models (DSMs) to investigate how intra-crown microclimatic heterogeneity affects the spatiotemporal patterns of flowering and autumn leaf senescence. Rhododendron yedoense f. poukhanense (H.Lév.) M. Sugim (RY) and Acer triflorum Kom. (AT) were monitored at the Korea National Arboretum, with 23 time-series images acquired between April and November 2025. Cumulative solar duration was calculated for 0.5 m intra-crown grids, and phenological events were detected using derivative analysis of vegetation indices (Red Chromatic Coordinate [RCC] and Green Chromatic Coordinate [GCC]). The results confirmed asynchrony in phenological events within single individuals depending on crown sectors. However, the linear relationship between intra-crown microclimatic heterogeneity and phenological duration was statistically weak ($\rho$ > 0.05), suggesting that strong physiological buffering mitigates the direct impact of spatial light variation. Despite this buffering, species-specific response patterns were observed: RY exhibited spatially independent flowering responses, whereas AT maintained relatively higher synchrony. Furthermore, AT showed a “Phenological Velocity” gap, where sunlit sectors tended to experience senescence approximately 1.12 days later than shaded areas**, while RY showed no significant directional lag.** This research demonstrates that phenological responses can be spatially dispersed even within an individual, and the buffering mechanisms against environmental variability differ by crown structure and growth form. These findings highlight the necessity of individual-level spatial resolution in understanding plant responses to climate change. Full article

Forest canopy cover is a crucial indicator for measuring ecological functions. However, traditional plot-based measurement methods suffer from low efficiency and insufficient spatial continuity. Addressing issues in UAV RGB imagery—such as tree crown boundary adhesion, shadow interference, and texture confusion—this paper proposes a lightweight and edge-sensitive tree crown segmentation network. The model employs MobileNetV3-Large to replace the traditional U-Net encoder, significantly reducing parameter count and computational load while satisfying the potential for edge device deployment. In the decoding phase, a Semantic-guided Channel Compression and Focus (SCCF) module is designed to enhance semantic-guided channel compression and feature focusing. Furthermore, a Gradient-guided Morphological Tree Crown Attention Module (G-MTCAM) is proposed. By utilizing Gradient-Induced Center Difference Convolution (GI-CDC) and a variance-based statistical gating mechanism, this module constructs a dual-stream architecture for morphology and texture interaction, achieving precise cutting of tree crown boundaries and effective filtering of background noise. Additionally, a boundary-enhanced composite loss function is introduced to improve the accuracy of crown edge identification. Experimental results indicate that the proposed model achieves an IoU, Acc, and F1 score of 88.59%, 88.62%, and 93.77%, respectively. Compared to the classic U-Net, these represent improvements of 2.77%, 1.71%, and 1.44%, while the parameter count and computational cost are only 5.98 M and 6.71 GFLOPs. The forest Canopy Cover (CC) estimated based on the segmentation results shows high consistency with ground-based near-zenith canopy hemispherical percentage ($CH{P}_{0–30}$, denoted as $C{C}_{obs}$), with a correlation coefficient ($R^2$) exceeding 0.90. This verifies the effectiveness of the method in forest canopy structure monitoring and provides technical support for the application of consumer-grade UAVs in forestry surveys. Full article

This study investigates the vibration response and energy transfer characteristics of walnut trees in mechanical vibration harvesting, aiming to improve fruit detachment efficiency and reduce structural damage. Three walnut tree architectures were classified based on branching height, trunk stiffness, canopy size, and geometric regularity. A dynamic model of the trunk was established, modeled as an equivalent conical beam with Rayleigh damping, and the clamping point was simplified to a single-degree-of-freedom system. To quantify energy transfer, three indicators were introduced: energy transfer coefficient, energy attenuation rate, and trunk overload index (OLI). Sweep-frequency experiments (9–17 Hz) were conducted at a clamping height of 80 cm. Triaxial acceleration responses were measured, and branch kinetic energy was calculated. The model-predicted natural frequencies matched the experimental acceleration peaks well, identifying a frequency-sensitive band between 15 and 17 Hz. Significant differences in energy distribution were observed among the three tree architectures. Tree 1 exhibits intense energy concentration near the trunk, with rapid energy decay along branches and the highest canopy vibration index (OLI: 6.13), indicating the highest trunk overload risk. Tree 2 demonstrates whole-tree coordinated vibration and the lowest OLI value (2.10). Tree 3 possesses two sensitive frequency bands with relatively uniform energy distribution and an OLI of 2.89. Trunk stiffness, branching height, canopy structure, and geometric irregularities collectively determine energy distribution within resonance bands and overload risk. The proposed energy metrics and OLI provide quantitative guidance for selecting excitation frequencies and controlling operational duration during walnut vibration harvesting. Full article