Search Results (1,366)

Aflatoxins are carcinogenic secondary metabolites produced by Aspergillus flavus and Aspergillus parasiticus. These two fungi are ubiquitous in soil and are often found in agricultural fields. Four aflatoxin variants commonly found in infected crops are: AFB₁, AFB₂, AFG₁, and AFG₂. Aspergillus parasiticus can produce all aflatoxin variants, with A. flavus only able to produce the aflatoxin B variants. Production of aflatoxins typically occurs as pre-harvest contamination in the Australian peanut-growing regions of Queensland. This study analysed geographic variations in aflatoxin component variants using the HPLC method for the 2020–2024 season peanuts. Aflatoxin-G was found as most common aflatoxin variant across three of the four peanut-growing regions. This study also evaluated diversity of A. flavus and A. parasiticus across the four peanut-growing regions, using post-harvest soil samples from the 2023–2024 growing season. Aspergillus parasiticus was found to be most prevalent (97% isolates) across the regions, whereas A. flavus was least prevalent (3% isolates) and only found in the Tolga region. The North Burnett had no Aspergillus colonies identified from the soil samples in the current year of collection. The data suggests the aflatoxin G variant is most predominant in Australian peanuts and also that there is large variation between the growing regions for prevalence of Aspergillus species. Full article

The results of a multiscale study of fault and fracture geometry, distribution, density, and intensity are reported for Mesozoic platform carbonates cropping out along the axial zones of the southern Apennines fold-and-thrust belt, Italy. By integrating field structural observations with digital outcrop analysis, the study focuses on Cretaceous limestone rocks exposed along natural creeks and artificial trails of the Castelsaraceno area, Raparo Mt., southern Italy. There, the limestone beds are bounded by mm- to cm-thick marly–clayey interbeds, forming a well-layered succession made up of a few m-thick bed packages bounded by several cm-thick clayish interlayers. The carbonate multilayer was first affected by thrust tectonics, with the formation of low-angle intra-carbonate thrust faults and fault bend-folding. Then, the multilayer was crosscut by extensional–transtensional high-angle faults, which displaced the previously formed contractional structural elements, and allowed carbonate exhumation from shallow crustal depths. At outcrop scales, thrust-related deformation was solved by low-angle joints and veins, rare high-angle stylolites, and low-angle sheared fractures displaying reverse kinematics. Quantitative analyses of fracture density (P20) and intensity (P21) conducted on selected portions of the thrust fault zones indicate that the low-angle joints and veins attain their highest values in the vicinity of the main slip surfaces, whereas they are almost absent in the surrounding carbonate host rocks. Plio-Quaternary transtensional deformation was solved by NW–SE- and NE–SW striking faults. The latter fault set, nicely exposed along the flanks of the Raganello Creek, was characterized by right-lateral components of slip. Incipient faults, with ca. 1 cm throw, are made up of vertically discontinuous slip surfaces, which crosscut single bed packages and abut against clayish interlayers. The slip surfaces form conjugate geometries, and are associated to high-angle fractures and veins striking NE–SW, dissecting the bed packages. The fault core is virtually absent, whereas the damage zones are very discontinuous along dip. The P20 values computed for the high-angle fractures and veins increase toward the slip surfaces, whereas the P21 values remain nearly constant. These data are interpreted as being due to fault nucleation processes associated with fracture nucleation within the limestone rocks. NE–SW striking small faults displaying throws between 10 and 60 cm are comprised of through-going main slip surfaces crosscutting multiple bed packages, and poorly developed, discontinuous fault cores flanked by m-thick damage zones. The damage zones include sub-parallel high-angle shear fractures, fractures and veins showing a positive correlation between P20 and P21, whose values increase in the vicinity of the main slip surfaces. Such a positive correlation is interpreted as due to fault growth by linkage and coalescence of pre-existing high-angle fractures, and formation of fault-related joints and veins at the extensional quadrants of single shear fractures. Similarly, large-scale NE–SW striking mature faults with throws on the order of tens of meters, made up of a m-thick fault core and 10 s of m-thick damage zones including sub-parallel fractures and veins, also show a positive P20 and P21 correlation. The main outputs of this work are synthesized into a conceptual model illustrating the transition from thrust-related deformation to extensional–transtensional faulting, documenting the evolution of fracture networks from incipient-to-small-to-mature faults. Full article

Legume cultivation offers a chance for agricultural development on lands that have been abandoned over the years. In this study, simple agronomic indicators on the growth and yield of faba bean (Vicia faba L.), pea (Pisum sativum L.), and white lupin (Lupinus albus L.) were assessed on the abandoned agricultural lands of Foloi Plateau in Western Greece. Field trials were conducted from October 2023 to July 2025, and the legumes were grown either according to the false seedbed concept or with conventional seedbed preparation practices (direct sowing). The false seedbed involves pre-sowing weed control following initial seedbed preparation, and in these trials, it suppressed weed density by 62–77%. The Normalized Difference Vegetation Index (NDVI) of faba bean and pea increased by 13% on the false seedbed plots, while white lupin NDVI was not affected by treatments (p ≥ 0.05). Destructive crop biomass measurements were in accordance with NDVI assessments. Faba bean and pea seed yield demonstrated an increase of 17% and 23%, respectively, in the false seedbed plots compared to direct sowing plots. White lupin seed yield was not significantly affected by false seedbed (p ≥ 0.05). This study provides preliminary evidence supporting the use of legume crops as a component of sustainable agricultural revitalization in the Foloi region. However, further research is required to optimize legume cultivation on the abandoned lands of the wider region as a first step towards the agricultural revitalization in the Plateau. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. 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

Zero-shot remote sensing is attractive for scene classification because new regions, sensors, and label taxonomies often appear before sufficient annotated data are available for supervised adaptation. We present OATS-RS, an inference-centric framework that keeps a remote sensing vision–language model (VLM) backbone frozen and improves zero-shot decisions through ontology-aware prompt construction, hierarchical and contrastive scoring, adaptive multi-view aggregation, unlabeled transductive refinement, ambiguity-aware local re-ranking, and selective prediction. The method targets the common remote sensing regime in which neighboring classes such as annual crop, permanent crop, forest, pasture, herbaceous vegetation, river, and sea or lake overlap strongly in red–green–blue (RGB) appearance, meaning that they require more than a single class-name prompt. On the supplied final EuroSAT RGB evaluation with a GeoRSCLIP Contrastive Language–Image Pre-training (CLIP)-family Vision Transformer Base with 32 × 32-pixel patches (ViT-B-32) backbone, the complete pipeline obtains top-1 accuracy of 0.522, balanced accuracy of 0.522, macro-averaged F1 score (macro-F1) of 0.535, and top-3 accuracy of 0.887. The strongest classes are industrial area, residential area, river, highway, and pasture, whereas the weakest classes remain herbaceous vegetation and several fine-grained vegetation categories. Selective prediction increases accepted-example accuracy to 0.538 at 0.934 coverage, but the expected calibration error (ECE) remains high at 0.384. These results support a qualified conclusion: ontology-guided zero-shot inference can already recover useful semantic shortlists for structured remote-sensing scenes, but fine-grained natural-class disambiguation, calibrated confidence, multi-dataset transfer, component-level ablations, and measured runtime remain essential before dependable deployment claims can be made. Full article

Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article

Fruit harvesting systems are undergoing a paradigm shift toward sustainable and energy-efficient mechanized platforms driven by robotics, artificial intelligence, and advanced sensing technologies. This review synthesizes recent engineering developments in fruit harvesting, focusing on system architecture, fruit detachment mechanics, and mechanized harvesting strategies. It examines harvesting classifications, mechanical principles governing detachment, and pre-harvest factors affecting performance, along with principal mechanisms including shaking, cutting, and alternative detachment techniques. Post-detachment handling and fruit recovery processes are also analyzed, together with economic and sustainability-related trade-offs between manual and mechanized harvesting systems. Recent progress in robotic harvesting systems, machine vision, and multi-sensor fusion is evaluated within the framework of smart orchard engineering, with increasing emphasis on energy-efficient design, resource optimization, reduced postharvest losses, and environmental sustainability as key performance drivers. Despite these advancements, current technologies remain constrained by fruit damage susceptibility, biological variability, limited cross-crop adaptability, and high implementation costs, limiting large-scale adoption in commercial orchards. The novelty of this review lies in establishing a unified engineering framework that links mechanical detachment principles with robotic systems and intelligent sensing technologies under an energy-efficient sustainability perspective, enabling a system-level understanding of harvesting performance and supporting the development of next-generation adaptive and sustainable fruit harvesting systems. Full article

Improving the productivity and stability of winter wheat under increasingly variable climatic conditions remains a major challenge for sustainable agriculture. This study evaluated the effects of pre-sowing seed treatment with a microbial preparation (Nando BioExpert) and a biostimulant (Vitazyme), applied individually and in combination, on crop establishment, yield components, and grain yield of winter wheat under unstable moisture conditions in the Right-Bank Forest-Steppe of Ukraine. A three-year field experiment demonstrated that both treatments positively influenced plant growth, while their combined application produced a pronounced synergistic effect. Seed treatment enhanced plant establishment, increasing plant density at emergence from 242 plants m⁻² in the control to 372 plants m⁻² under the combined treatment. This improvement contributed to increased stand-level productive tiller density per unit area. Consequently, grain yield was consistently improved across years, with the combined treatment producing the highest average yield (6.04 t ha⁻¹), corresponding to a 37% increase relative to the control. The results indicate enhanced winter wheat resilience to environmental stress under biological seed treatment. Overall, integrating microbial inoculants with biostimulants represents an effective strategy for improving winter wheat productivity under moisture-limited conditions and supports the transition toward sustainable and resource-efficient crop production systems. Full article

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively. Full article

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security. Full article

Tomato crop residues (TCR) from soilless greenhouses are treated as waste, causing greenhouse gas emissions and biomass loss. Within a circular economy framework, gasification converts TCR into renewable energy and biochar; however, its high pH and electrical conductivity (EC) limit its use as a substrate. This study evaluated whether pre-treatment could enable TCR biochar to act as a substrate component and nutrient source in tomato and pepper seedlings. Biochar was produced by gasification and pre-treated by water incubation plus nitric acid, reducing EC from 27 to 8.7 dS m⁻¹ and pH from 10.4 to 8.2 while achieving nitrate loading without leaching. Pristine biochar severely restricted growth. Subsequent experiments evaluated pre-treated biochar mixed with perlite or cocopeat, with or without external N and K. The 15/85% (w/w) pre-treated biochar/cocopeat mixture (PTB/C) showed the best overall performance. In the absence of additional N/K, PTB/C produced shoot biomass and shoot N concentrations comparable to N-/K-supplemented cocopeat; shoot K was comparable in tomato and higher in pepper. With N and K supplementation, PTB/C exceeded supplemented cocopeat biomass by 1.41- and 1.95-fold in tomato and pepper, respectively. These results indicate that pre-treated TCR biochar can reduce dependence on imported cocopeat and external N/K supply. Full article

The two-year research involving laboratory and field studies supported by Geant4 computer simulation is aimed at determining the optimal parameters of 1 MeV accelerated electrons and 80 keV X-ray pre-planting irradiation of wheat seeds in order to find the optimal dose range which increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. During the laboratory studies we measured the germination rate and biometric properties of plants, as well as the type, number, and average diameter of fungi found in the irradiated and non-irradiated seeds after irradiation with electrons and X-rays with the dose range 2–1000 Gy. Following the laboratory studies showing that the doses exceeding 30 $\mathrm{Gy}$ decreased the germination rate of wheat, field studies evaluated the impact of pre-planting irradiation with the doses in the range of 5–30 Gy on the wheat productivity and the rate of fungal diseases in wheat plants grown from irradiated and non-irradiated seeds. It has been found that the dose range 5–15 Gy is more preferable for pre-planting wheat irradiation, both for e-beam and X-rays, since it increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. The X-ray dose of 15 $\mathrm{Gy}$ is found to be the most effective since it increased the yield up to 40% and also suppressed the Septoria glume blotch up to 40%. Since seed irradiation requires a particularly delicate approach given that the goal of irradiation is not only to reduce the rate of fungal diseases in the plants but also to increase the crop yield without detriment to the soil and the plant itself, consistency of dose uniformity across the seeds during pre-planting irradiation ensures the high reliability and repeatability of the irradiation effect. Our approach to irradiation planning with the use of Geant4 computer simulation allows us to precisely estimate the dose distribution in individual seeds and the distribution of radiation-chemical yield of radicals occurring as result of radiolysis in order to predict the effect of pre-planting irradiation and select the optimal irradiation parameters for maximizing the yield and crop quality. Full article

This study aimed to determine the optimal biochar application rate for sustaining cotton productivity in moderately saline soils under dry sowing with wet emergence (DSWE) conditions in Shaya County, Xinjiang. A two-year field experiment, arranged in a randomized complete block design with two replicates, evaluated six biochar application rates (S1–S6) against a non-amended control (CK). The biochar, derived from fruit-wood via limited-oxygen pyrolysis at 500 °C (pH 9.82, porosity 64.5%), was applied as a single pre-sowing amendment. Soil water–salt dynamics, crop emergence, and growth parameters were continuously monitored. The results indicated that biochar application consistently reduced soil salinity; specifically, seedling-stage salinity decreased by 30.1–42.2% in the first year compared with the CK. Cotton emergence and yield improved significantly across both seasons. However, the optimal application rate for maximizing yield varied between years. While a high rate (S5: 25 t·hm⁻²) produced the highest first-year yield (6243.8 kg·hm⁻²), a moderate rate (S3: 15 t·hm⁻²) demonstrated greater yield stability and achieved the maximum yield (5975.2 kg·hm⁻²) in the second year. This interannual shift is likely attributable to biochar aging and structural pore saturation in the high-dose plots. Combined with high regional evaporation, these factors exacerbated secondary salinization and reduced the residual benefits of the amendment over time. In contrast, the moderate dose maintained a more effective balance between continuous water–salt regulation and nutrient availability. Under the experimental conditions, a single pre-sowing application of 15 t·hm⁻² biochar, combined with a 375 m³·hm⁻² drip irrigation volume, is recommended as an effective strategy to ameliorate salinity and support long-term yield stability. Full article

The green-belly stink bug (GBB), Diceraeus melacanthus (Dallas, 1851) (Heteroptera: Pentatomidae) is a key South American maize (Zea mays L.) pest, feeding on seedlings and causing physiological disorders. Understanding D. melacanthus population distribution and establishing sampling plans is essential to manage this species. Hence, the objective was to determine a distribution pattern, recommend a sampling unit size and develop sampling plans for the GBB, covering maize pre-sow period up to the maize V4 stage. Assessments were carried out in an experimental field and nine crop fields in northern Paraná State. In the experimental field, quadrants of n, 2n, 4n, 8n and 16n (n = 0.25 × 0.25 m²) were tested, thus determining an aggregated distribution with a recommended sampling unit size of 0.5 × 0.5 m². After the nine crop field samplings, a negative binomial distribution was deemed fit to represent GBB in field conditions. Two sampling plans were developed, highlighted is the sequential presence–absence plan, which recommends a maximum of 60 sample points, and a minimum of 25, with at least six presences to make control decisions. For a more assertive sampling, divide the evaluated area into glebes with distinct natural characteristics and employ the sampling plan to each glebe. These sampling plans must be validated before IPM recommendation. Full article