Search Results (12,333)

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

Controversy persists on a global scale regarding the trade-offs between greenhouse gas (GHG) emissions, yield, the global warming potential (GWP), and GHG intensity (GHGI) following organic fertilizer substitution within vegetable cropping systems. This study aimed to quantify these effects under diverse conditions and elucidate the direct and indirect drivers governing these outcomes through a meta-analysis and structural equation modeling (SEM). We synthesized 655 paired observations from 69 published studies using random-effects meta-analysis, finding that organic fertilizer substitution significantly increased CH₄ emissions and GWP compared to inorganic fertilizer controls. Although this was the general trend, organic fertilizer could reduce GWP under specific climatic and soil conditions by reducing N₂O emissions, such as mean annual precipitation <400 mm or soil total nitrogen ≥3 g kg⁻¹. These conditions were also associated with substantially higher yield and lower GHGI. Furthermore, SEM demonstrated that field management practices exerted significant direct effects on N₂O emissions, GWP, and GHGI. Reductions in N₂O emissions, GWP, and GHGI could be achieved with fertilizer application duration ≥10 years, total N application rate ≥300 kg ha⁻¹, and field cultivation or plowing. GHGI was also reduced through yield enhancement under a moderate organic substitution rate (33–66%) or irrigation ≥300 mm. Our study provides a scientific basis for moving beyond universal recommendations towards precision organic management, which is essential for optimizing fertilization strategies to mitigate agricultural GHG emissions. Full article

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO. Full article

Saline irrigation is a major constraint to crop production in newly reclaimed desert lands, even when pre-sowing soil salinity is low. This two-season field study evaluated whether plant growth-promoting rhizobacteria could improve barley performance under saline irrigation water with an electrical conductivity of 11.8 dS m⁻¹ in the El Moghra region, Egypt. The barley cultivar Giza 2000 was grown under five inoculation treatments: an uninoculated saline-irrigated control; a single inoculation with Azospirillum lipoferum; and combined inoculations with A. lipoferum and Bacillus coagulans, Bacillus circulans, or Enterobacter cloacae. Because freshwater was unavailable at the experimental site, treatment effects were evaluated relative to the saline-irrigated control. Across both growing seasons, single inoculation with A. lipoferum produced the most consistent improvements in growth, yield formation, nutrient accumulation, soil biological activity, and seed nutritional quality. The combined treatment of A. lipoferum and B. circulans was generally the second-most effective. Bacterial inoculation also improved adjustment to physiological stress, as indicated by greater proline accumulation, lower antioxidant enzyme activities, and enhanced expression of stress-related genes associated with proline biosynthesis and secondary metabolism. Overall, the results indicate that A. lipoferum applied alone was more effective than the tested combinations of bacteria under saline irrigation. These findings provide field-based evidence that inoculant performance depends on strain composition and that single-strain inoculation can be a promising strategy for improving barley production in reclaimed sandy soils irrigated with saline water. Full article

The present study was conducted to study the effect of exposure time to ultraviolet-C (UV-C) radiation on seed germination, fungal suppression and seedling growth of three Egyptian rice cultivars, namely, Sakha 105, Sakha 108, and Giza 183. Experiments were carried out under controlled laboratory conditions. Rice seeds were exposed to UV-C radiation with a wavelength of 253.7 nm and intensity of 1960 µW cm² for 0 (control), 10, 20, 30, 40, 50, and 60 min. Initial seed health testing showed the presence of several seed-borne fungi, mainly Alternaria alternata, Rhizoctonia solani, and Fusarium verticillioides, in addition to Aspergillus niger and Aspergillus flavus. Results revealed that UV-C exposure time, rice cultivar and their interactions significantly (p < 0.05) affected germination percentage, reduction percentage of seed fungal infection, and seedling growth parameters. The optimum exposure time was 30 min, which was found to maximize germination and improve shoot and root growth to achieve high levels of fungal suppression. Giza 183 exhibited the highest average germination percentage (92.40%), while Sakha 105 obtained the highest shoot height (17.00 cm) and root length (12.91 cm). The results indicate that UV-C irradiation is an effective, residue-free and environmentally sustainable seed treatment technology for improving rice seed quality as well as early seedling performance. Full article

Understanding how irrigation water management shapes crop performance is critical for improving productivity and resource-use efficiency in peri-urban agriculture. This study investigated the socio-technical factors driving sprinkler system abandonment and assessed how irrigation water variability influences vegetable yield variability at two market gardening sites (Bogdin and 14 Yaar) in Ouagadougou, Burkina Faso. Survey data from 50 farmers and field measurements of soil properties, irrigation water application, and lettuce yield were analyzed using descriptive statistics, Spearman correlations, and principal component analysis. More than 80% of farmers had ceased using the sprinkler system within two years of installation, 76% reported major equipment failures, and 70% expressed willingness to re-adopt an improved system. Irrigation dose and yield showed considerable variability across sites (CV = 20.9–42.3% and 36.4–44.0%, respectively). At 14 Yaar, irrigation dose was strongly associated with yield (r = 0.862, p = 0.006), indicating that uneven water application was a major constraint on productivity. At Bogdin, where irrigation was more uniform, no single soil or water variable dominated yield variability. Although soil fertility variables contributed to multivariate patterns, nutrient–yield correlations were not statistically significant under the available sample size, and their potential influence on yield requires confirmation with larger datasets. Overall, operational constraints, equipment failures, and inadequate support services contributed to sprinkler system abandonment, while variability in manual water application was associated with variability in crop productivity. These findings highlight the need for irrigation strategies that are both technically robust and adapted to farmers’ realities. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article

Organic vegetable cultivation requires soil management strategies that improve carbon balance and suppress soilborne diseases. This study evaluated the efficacy of acidified microbial biochar pellets (ABPM) in enhancing net ecosystem carbon balance (NECB) and suppressing clubroot disease (Plasmodiophora brassicae) during organic Chinese cabbage (Brassica rapa ssp. pekinensis) cultivation. In a field-scale evaluation, three treatments were compared: guano fertilizer (control), ABPM 27 (inoculated with Pseudomonas fluorescens 22BCO027), and ABPM 86 (inoculated with Bacillus megaterium 22BCO086). Soil incorporation of ABPM 27 and ABPM 86 significantly increased soil carbon sequestration by 29.1% and 22.4%, respectively, while simultaneously reducing cumulative greenhouse gas emissions under the experimental conditions. This resulted in positive NECB values of 2.63 and 2.94 t CO₂-eq ha⁻¹, suggesting enhanced carbon retention potential within the studied cultivation system. Beyond its impact on carbon dynamics, ABPM 27 increased marketable yield by 8.6% (77.4 t ha⁻¹) and reduced clubroot incidence by 46.2%. Rhizosphere microbial analysis revealed that ABPM 27 promoted late-season microbial diversity and the persistence of beneficial Bacillus spp. and Pseudomonas spp. populations. These findings suggest the potential multifunctional role of microbially engineered biochar pellets in improving crop production, carbon retention, and pathogen suppression under organic cultivation conditions. However, these findings are based on a single-season field experiment and NECB-based carbon balance estimates, and therefore require validation across multiple growing seasons and cultivation environments. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

For modern agricultural management, the accuracy of plant identification is crucial. However, the task becomes challenging because crops and weeds at early growth stages often exhibit similar color, leaf morphology, and texture in two-dimensional images captured under field conditions, despite their clear biological differences in terms of botanical species, root systems, and phenological characteristics. Furthermore, computing hardware in the field also has strict limits. Therefore, we developed the RT-BMTR network to handle these physical constraints. Within this architecture, image data is processed through a bilateral hybrid backbone named Bi-HMB. The DSFM captures small local details, and MambaVision understands the broader background information. Then, these features are fused by RepNCSPELAN4. We adopted this structure to reduce redundant calculations. Next, the model determines its bounding boxes using the Inner-ShapeIoU loss function. This geometric constraint improves the detection of small targets. When evaluated on the CropAndWeed dataset, our model achieved an average precision (AP) at IoU threshold 0.5 (AP⁵⁰) of 68.1%, AP⁷⁵ of 54.8%, and a mean AP averaged over IoU thresholds from 0.5 to 0.95 (AP^50–95) of 50.9%. Detection precision recorded 26.5% for small objects, 44.7% for moderate ones, and with 59.3% for large objects. Rates for the first two categories saw enhancements of 16.2% and 4.6%. Overall, our modified model outperforms the original RT-DETR baseline. We also shrank the overall parameter count by 30.1%, alongside a 4.2% decrease in computational demand. Full article

Optimizing nitrogen management is essential for maintaining high spring maize yield while mitigating nitrous oxide (N₂O) emissions in irrigated areas. However, the interactive effects of total nitrogen application rate and starter nitrogen proportion on yield and N₂O emissions remain insufficiently quantified. Reliable assessment of these interactions requires well-calibrated DeNitrification–DeComposition (DNDC) simulations, yet existing calibration studies often emphasize crop parameters while neglecting soil parameters critical for soil hydrothermal dynamics and N₂O production. In this study, field data from shallow-buried drip-irrigated spring maize in Tongliao during 2024–2025 were used to conduct Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis on 12 crop and 13 soil parameters of the DNDC model. Sensitive parameters were calibrated using the differential evolution algorithm, and 64 nitrogen management scenarios were simulated by combining eight total nitrogen application rates (100, 150, 200, 250, 300, 350, 400, and 450 kg N ha⁻¹) with eight starter nitrogen proportions (0%, 15%, 25%, 30%, 35%, 40%, 45%, and 50% of the total nitrogen rate). The results showed that DNDC outputs were jointly controlled by crop and soil parameters, among which maximum yield, leaf carbon-to-nitrogen ratio, stem fraction, grain carbon-to-nitrogen ratio, thermal degree days for maturity, grain fraction, soil organic carbon (SOC) decrease rate below topsoil, soil clay content, soil porosity, wilting point and depth of top soil with uniform SOC content were dominant. Compared with the conventional crop-parameter calibration, the sensitivity-screened parameter set improved the simulation of both cumulative N₂O emissions and yield. Across the 64 scenarios, cumulative N₂O emissions ranged from 0.42 to 4.87 kg [N]/ha, while simulated maize yield ranged from 1597 to 6347 kg [C]/ha. N₂O emissions increased with total nitrogen rate, whereas yield increased initially and then reached a plateau. Increasing the starter nitrogen proportion did not substantially enhance yield but increased N₂O emission risk under high nitrogen rates. Overall, the scenario with 300 kg/ha and no nitrogen applied at sowing achieved a relatively high yield of 5519 kg [C]/ha while maintaining a low cumulative N₂O emission of 0.98 kg [N]/ha and was therefore identified as the preferred trade-off strategy under shallow-buried drip irrigation. This study provides an EFAST–DNDC framework for optimizing nitrogen management to sustain spring maize yield while reducing N₂O emissions in the West Liaohe Plain. Full article

This study assesses the phytoremediation potential and biomass production of sweet sorghum (Sorghum bicolor L.) and switchgrass (Panicum virgatum L.) under saline field conditions in 2024 and 2025. Salinity was defined based on electrical conductivity (EC), and phytoremediation performance was evaluated in this study. Sweet sorghum consistently produced high biomass (56.700–78.600 kg/ha), yet saline irrigation decreased its yield by 6% in 2024 and 11% in 2025, alongside a 19% reduction in plant height in 2025. Conversely, saline irrigation promoted switchgrass growth, increasing biomass yield from 2548 to 3643 kg ha⁻¹ (43%) in 2024 and from 4503 to 5812 kg ha⁻¹ (29%) in 2025. Plant height also increased by up to 35% in 2025 under saline conditions. In this study, when the Na⁺ (me/L) results at 10 cm of irrigated soil under saline conditions were examined, sorghum and switchgrass plants produced statistically significant differences in their saline-irrigated plots compared to their plots irrigated with non-saline water. In contrast, no significant differences were observed between sorghum and switchgrass in terms of soil Na removal under either saline or non-saline irrigation conditions. Therefore, both plants have a similar sodium reduction capacity. Full article

Increasing pressure on surface water resources in intensive agricultural regions has driven the search for sustainable alternatives for irrigation supply, especially in areas where water quality limits crop safety and export opportunities. In this context, riverbank filtration (RBF) systems offer a nature-based solution by utilizing physical, chemical, and biological processes associated with river–aquifer exchange. However, their implementation depends on suitable site selection supported by hydrogeological, geomorphological, and hydraulic criteria. This study developed an integrated methodology to identify zones with potential for implementing RBF systems in the Roldanillo–Unión–Toro irrigation district, located in northern Valle del Cauca, Colombia. This region requires irrigation water over 10,256 ha of agricultural land (mainly sugarcane, maize, grapes, and guava). We combined geophysical methods (vertical electrical soundings, 2D electrical resistivity tomography, and passive seismic), geotechnical methods (CPTu tests), and hydraulic characterization of the river reach to evaluate subsurface stratigraphy, preliminary hydrogeological suitability, inferred river–aquifer connectivity conditions, and channel stability. The evaluation covered four sectors along an approximately 21 km stretch of the Cauca River’s left-bank alluvial valley. The results revealed pronounced lateral and vertical heterogeneity of alluvial materials. However, the “El Palmar” sector was identified as the best-supported priority sector for future RBF validation, due to the presence of profile-scale evidence of potentially permeable sandy and gravelly units with intermediate resistivity values (52–61 Ω·m), favorable stratigraphic organization, and stable river-reach conditions during the field campaign. In contrast, the other three sectors (La Esperanza, Candelaria, and Cayetana) showed more fine-grained sediments with deeper permeable strata. River-flow measurements during the July 2025 field campaign indicated high discharge conditions at the evaluated reach, while river-channel observations showed active fine-sediment transport; these findings provide hydraulic and sedimentary context for the future evaluation of induced infiltration and potential clogging, but do not constitute direct evidence of river–aquifer exchange. This study highlights the value of integrated screening approaches for prioritizing candidate RBF sites in agricultural alluvial settings, while indicating that pumping tests, piezometric monitoring, hydraulic-gradient analysis, and water-quality validation remain necessary before engineering implementation. Full article