Search Results (6,757)

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Mining and metallurgical activities are among the main sources of heavy metal (HM) contamination of terrestrial ecosystems and the creation of persistent technogenic pollution hotspots. This study aimed to provide a comprehensive assessment of the accumulation of zinc (Zn), cooper (Cu), cadmium (Cd) and lead (Pb) in soils and vegetation under conditions of long-term industrial impact in Ridder, East Kazakhstan Region. A total of 52 soil samples were collected from 0–5 cm and 5–20 cm depths at 26 sites, and 44 species of natural vegetation, as well as three dominant agricultural crops, were examined. Soil concentrations of Zn (4415 mg·kg⁻¹), Cu (1177 mg·kg⁻¹), Cd (179 mg·kg⁻¹), and Pb (1996 mg·kg⁻¹) were classified as extremely high. Cadmium contributed most to the potential ecological risk (Cd > Pb > Zn > Cu). The industrial zone’s vegetation cover was predominantly formed by stress-tolerant and ruderal species, including Artemisia vulgaris, Calamagrostis epigeios, Bunias orientalis, Dactylis glomerata, Convolvulus arvensis, and Urtica dioica. The agricultural crops (Helianthus annuus, Avena sativa, and Triticum aestivum) mainly accumulated HMs in their root systems, with limited translocation to their aboveground organs (TF < 1). This indicates the predominance of phytostabilisation mechanisms, and highlights the potential of locally adapted plants for managing contaminated areas. Full article

Selenium nanoparticles (SeNPs) synthesized through green routes have emerged as promising nanobiostimulants in sustainable agriculture due to their ability to enhance plant growth and antioxidant defense. The aim of this study was to evaluate the biostimulant effect of SeNPs on Capsicum annuum at two stages of crop development to characterize the response to SeNP exposure and identify concentration-dependent effects and application methods. Physiological indicators, including growth, photosynthetic pigment content, and antioxidant activity, were evaluated. Different concentrations of SeNPs were tested during germination, and dosage and two types of application were compared during the vegetative phase in a hydroponic experiment. SeNPs at concentrations of 1.25, 2.5, 5, 10, 20, 40, and 80 µM were applied to chili seeds for 20 days. The plants were exposed to SeNPs concentrations ranging from 1.25 to 80 µM, applied through the roots and leaves. Germination parameters were not significantly affected except for the seed vigor index, which increased at all concentrations, particularly at 20 µM. Low to moderate doses (1.25–20 µM) acted as biostimulants, enhancing plant height, root length, biomass accumulation, photosynthetic pigment content, and phenolic and flavonoid compound synthesis. Conversely, high doses (80 µM) induced phytotoxic effects, especially via root exposure, reflected by growth inhibition, and reduced chlorophyll content. Foliar application demonstrated a systemic biostimulant response, improving root growth and photosynthetic activity without toxicity symptoms. Antioxidant assays (DPPH and ABTS) revealed dose-dependent modulation of redox balance, suggesting adaptive responses to SeNP-induced oxidative conditions. These findings highlight the potential of SeNPs as biostimulants that improve physiological performance in chili plants, while emphasizing the importance of an optimal dosing and application method for sustainable nanotechnology-based crop management. Full article

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R² = 0.811; RMSE = 2.931 t ha⁻¹). Texture features alone also showed strong predictive capability (R² = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions. Full article

The reliability of power transmission and distribution depends on the proper functioning of power transformers, which use conventional mineral oil as an insulating fluid. The lower fire class and biodegradability of mineral oil have led to a shift towards second-generation oils from vegetable and plant crops. Ester fluids provide a better performance in combination with solid pressboard/paper insulation, increasing the lifetime of power transformers compared to those using mineral oil. Considering the need for sustainability in the near future, second-generation oils are no longer feasible, and hence, third-generation oils derived from microalgae species are suitable alternative fuels for the energy sector. The fatty acid methyl ester (FAME) content of algae is similar to that of biodiesel, making it a suitable fluid for power transformers. A detailed overview of third-generation feedstock (algae) for power transformer applications is provided, focusing on the extraction of algal oil, in conjunction with safety precautions and its fatty acid content, and a comparison with conventional vegetable and plant-based oils is presented. Various properties of algal oil (fatty acid composition, kinematic viscosity, oxidation stability, breakdown voltage, etc.) are analyzed to assess its suitability as a transformer fluid. This review article comprehensively analyzes the current research landscape surrounding the use of algal oil as an insulating fluid in transformers. It critically evaluates both the potential advantages and the unique challenges associated with this alternative to conventional mineral oil and second-generation vegetable and plant-based oils. Full article

Ulluco (Ullucus tuberosus Caldas) is an Andean tuber crop of high nutritional and genetic importance. However, its vegetative propagation promotes the accumulation of pathogens and limits the availability of uniform, high-quality planting material. In this study, an efficient and reproducible in vitro micropropagation protocol was established for an ulluco genotype from the Amazonas region of Peru. Nodal segments were cultured on MS (Murashige and Skoog) medium supplemented with 6-benzylaminopurine (BAP) or kinetin (KIN) at increasing concentrations (0.0–2.0 mg L⁻¹). For rooting, in vitro-derived shoots were transferred to MS medium supplemented with indole-3-butyric acid (IBA) or 1-naphthaleneacetic acid (NAA) at the same concentration range (0.0–2.0 mg L⁻¹). The explants exhibited a high basal morphogenetic capacity; however, the addition of cytokinins significantly enhanced the response. KIN at 2.0 mg L⁻¹ achieved 100% regeneration, whereas BAP at 0.2 mg L⁻¹ maximized shoot proliferation, producing 2.07 shoots per explant. Shoot elongation was greater with KIN at 1.0 mg L⁻¹, reaching 39.15 mm. In the rooting phase, the response varied depending on the type and concentration of auxin. NAA at 0.1 mg L⁻¹ resulted in 100% rooting and produced the greatest root length (41.93 mm), whereas IBA at 0.1 mg L⁻¹ maximized the number of roots (4.67), although roots were shorter. Rooted plantlets exhibited 100% survival after eight weeks of acclimatization. This protocol provides an effective system for the rapid production of vigorous and uniform clonal plants and represents a useful tool for the propagation, conservation, and future biotechnological improvement of ulluco. Full article

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts. Full article

Pea (Pisum sativum L.) is an important source of food and feed and contributes to soil improvement through its association with nitrogen-fixing bacteria. By enabling higher yields and selection of tolerant genotypes, controlled environment agriculture (CEA) could meet increasing nutritional needs despite adverse conditions. The main objective of this study was to investigate the effects of drought stress on the development of vegetable pea genotypes under controlled vertical farming conditions. Plants were grown in CEA and exposed to drought stress at different developmental stages, after flowering and after pod formation. Drought significantly reduced pod and seed numbers, showing a stronger effect than genotype. For example, genotype Favorit produced 7.67 and 9.00 seeds per plant under control conditions, compared with only 2.00 and 2.67 seeds per plant under drought treatments. Pod length, seed number, and seed weight were also lower under stress, highlighting the importance of water availability during seed setting and filling. Fresh and dry biomass were mainly influenced by genotype, indicating differences in stress adaptability. The results also demonstrate that CEA can be used for reproducible abiotic stress experiments relevant to plant breeding and crop production. Full article

Charcoal rot of common bean, caused by Macrophomina, is one of the most economically important diseases worldwide. In Mexico, charcoal rot of bean has been associated exclusively with M. phaseolina; however, in recent years, new Macrophomina species affecting various crops have been described globally. Information on this pathogen in common bean in Mexico remains limited. Therefore, the objectives of this study were to characterize Macrophomina isolates obtained from bean fields in northern Sinaloa morphologically and molecularly using species-specific primers, and to determine their aggressiveness and vegetative compatibility groups (VCGs). During the 2020–2021 growing season, 50 Macrophomina isolates were obtained from common bean tissues exhibiting charcoal rot symptoms collected from 12 fields in the municipalities of Ahome and Guasave, Sinaloa, Mexico. Molecular analysis using species-specific primers for three Macrophomina species (M. phaseolina, M. pseudophaseolina, and M. euphorbiicola) identified all 50 isolates as M. phaseolina. Pathogenicity tests indicated that the M. phaseolina isolates differed in aggressiveness toward common bean plants. Mycelial compatibility assays revealed at least seven vegetative compatibility groups among M. phaseolina isolates distributed across northern Sinaloa. To our knowledge, this is the first study to provide phenotypic characterization, aggressiveness assessment, vegetative compatibility grouping, and species-specific primer-based identification of M. phaseolina isolates from common bean fields in Sinaloa, Mexico. Full article

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana^®) with two planting densities (625 and 1250 trees ha⁻¹) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article

Brassicaceae plants are generally considered non-mycorrhizal; however, recent studies have challenged this non-host status, suggesting occasional colonization during reproductive stages or by overlooked fungi such as Mucoromycotina Fine Root Endophytes (MFRE). To re-evaluate the non-host status of Brassicaceae, we cultivated five Brassicaceae species, including rapid life cycle Brassica rapa (Fast plants) using field soil containing both Glomeromycotina Arbuscular Mycorrhizal Fungi (G-AMF) and MFRE. To ensure inoculum potential, a co-planting system with lettuce (Lactuca sativa) as a nurse plant was employed. While lettuce roots were rapidly colonized by both G-AMF and MFRE, no mycorrhizal colonization was observed in any Brassicaceae roots throughout their entire life cycle, from vegetative growth to flowering and seed maturation in Fast plants. Furthermore, co-planting with Brassicaceae did not significantly affect the mycorrhizal colonization or shoot phosphorus concentrations of the neighboring lettuce. These results demonstrate that Brassicaceae plants maintain a robust non-host status against both G-AMF and MFRE. Moreover, they function as “neutral non-hosts” that do not disrupt the symbiotic networks of neighboring plants. This characteristic reinforces the value of Brassicaceae in sustainable crop rotation systems. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article