Search Results (663)

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

Mabroom dates (Phoenix dactylifera L.) are recognized as one of the most important crops in Qatar. Fresh fruit dates are susceptible to mould and post-harvest spoilage, resulting in a significant financial loss. Octanoic fatty acid (OFA) has been shown to regulate the growth of mould-causing organisms such as fungi and bacteria. It is known to have antibacterial properties. The objective of the current study was to evaluate the in vitro effect of OFA on the post-harvest pathogens of Mabroom fruits. Fresh, apparently healthy, and fully ripe Mabroom dates were obtained from the National Agriculture and Food Corporation (NAFCO). The chosen fruits were packed in sterile, well-ventilated plastic boxes and transported to the lab under controlled conditions. The fruits were distributed into five groups (G1 to G5). The groups G1, G2, and G3 received 1%, 2%, and 3.5% OFA, respectively, while G4 was left untreated and G5 was washed only with tap water as a positive control treatment. Each group contained 200 g of fresh and healthy semi-soft dates. The samples were then dried and incubated in a humidity chamber at 25 °C ± 2 for seven days. The signs and symptoms of decay were monitored and recorded. The presence of pathogens was confirmed via phenotypic and microscopic-based methods. The results showed a significant difference (p ≤ 0.05) among the groups. OFA at 3.5% had the strongest inhibitory action against post-harvest pathogens, followed by OFA2%. However, there were no differences (p ≤ 0.05) between OFA1% and the control groups. Aspergillus spp., Penicillium spp., Rhizopus spp., and Botrytis spp. were most abundant in the control group, followed by OFA2% and OFA1%, respectively. In conclusion, octanoic fatty acid at 3.5% may improve the quality of date fruits through its high antimicrobial activity, reduce the effect of post-harvest decay, minimize the loss of date fruits during storage, and improve the sustainability of date fruits. Further experiments are necessary to confirm the effectiveness of OFA as a green solution for sustainable date fruit production. Full article

Sweet cherry (Prunus avium L.) is becoming increasingly popular in China, but its postharvest quality deteriorates significantly during harvest storage and transport. Here, we investigated the efficiency of different modified atmosphere packaging (MAP) treatments on the quality and physiology of ‘Meizao’ sweet cherry during 60 days of cold storage (0 ± 0.5 °C). Fruits were sealed in four types of MAP low-density polyethylene (LDPE) liners (PE20, PE30, PE40, and PE50), with unsealed 20 μm LDPE packaging bags used as the control. Our findings demonstrated that PE30 packaging established an optimal gas composition (7.0~7.7% O₂ and 3.6~3.9% CO₂) that effectively preserved ‘Meizao’ sweet cherry quality. It maintained the fruit color, firmness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc) content while simultaneously delaying deteriorative processes such as weight loss, pedicel browning, and fruit decay. These results indicate that PE30 was the most suitable treatment for preserving the quality of ‘Meizao’ sweet cherries during cold storage. Furthermore, physiological research showed that significant inhibition of respiration rate was achieved by PE30, accompanied by maintained activities of antioxidant enzymes (CAT, POD, and SOD), which consequently led to reduced accumulations of ethanol and malondialdehyde (MDA) during cold storage. To date, no systematic studies have investigated the physiological and biochemical responses of ‘Meizao’ to different thickness-dependent LDPE-MAP conditions. These observations highlight the power of the optimized PE30 packaging as an effective method for extending the fruit storage life, delaying postharvest senescence, and maintaining fruit quality of ‘Meizao’ sweet cherry. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield. Full article

Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022–2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January–June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha⁻¹), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R² of 0.802 (RMSE = 983.8 kg ha⁻¹), the protein model an R² of 0.786 (RMSE = 1.71%), and the RFV model an R² of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures—particularly in March and May—and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management—prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity—can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions. Full article

This work proposes an insight into the mycotoxins detected in wheat from the 2023 and 2024 harvests in Belgium and highlights the link between agronomic conditions and mycotoxin contamination. The study utilized samples from a Belgian trial network, covering nine locations in 2023 and eight in 2024, ensuring diverse pedoclimatic contexts and including 11 different varieties. Sowing and harvest dates, previous crops and meteorological data were collected for these locations. A validated UPLC-MS/MS multi-mycotoxin method able to detect 20 mycotoxins, regulated or not, was used. Deoxynivalenol, zearalenone, and enniatins B and B1 were detected in the 2023 and 2024 samples. Enniatin A1 was only detected in the 2024 samples. Mycotoxin contamination was higher in 2024 compared to 2023, in terms of both the number of contaminated samples and the contamination levels. Enniatins B and B1, non-regulated mycotoxins, were widely detected in the 2024 wheat samples, with enniatin B detected in 68 out 88 samples ranging from 12 to 488 µg/kg. Differences between the wheat varieties were observed, with some varieties showing significantly higher contamination. Additionally, geographic location appeared to influence contamination levels, which could be related to previous crops or meteorological events. In conclusion, this research provides a comprehensive analysis of mycotoxin co-contamination in wheat samples from diverse pedoclimatic contexts in Belgium based over 2 years. It shows the importance of weather conditions on mycotoxin contamination. It also emphasizes the importance of variety selection to manage mycotoxin contamination. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

Melon is a valuable crop that generates significant by-products during consumption and processing. Among these, seeds are rich in phenolic compounds and might be used to produce sprouts with increased content of these bioactive substances. This study evaluated phenolic compounds (PhCs) in sprouts of two melon cultivars, Thales and SV9424ML, obtained from seeds having different germination speeds, thus harvested at 6 and 14 days after sowing (DAS). A factorial combination of cultivar and harvest time was tested in a completely randomized design with four replicates. Thales produced more ready-to-eat sprouts at 6 DAS than SV9424ML (64.0% vs. 46.7%). Sprouting significantly increased total PhCs content, particularly flavonoids, with Thales showing higher values than SV9424ML (50.2 vs. 32.6 mg kg⁻¹ DW). Phenolic profiles significantly varied among cultivars and harvests. Sprouts at 6 DAS had more total hydroxybenzoic acids and flavonoids, while 14 DAS sprouts were richer in hydroxycinnamic acids. Significant differences between harvest dates were observed in the concentrations of protocatechuic, vanillic (VanA), p-coumaric (p-CouA), ferulic (FerA) acids, and orientin (Ori) for Thales, and of VanA, p-CouA, FerA, and Ori for SV9424ML. Results are encouraging, but future investigations are essential to understand whether these sprouts can be suitable for fresh consumption, food supplements, or phytochemical extraction. Full article

The date palm industry is a vital pillar of agricultural economies in arid and semi-arid regions; however, it remains vulnerable to challenges such as pest infestations, post-harvest diseases, and limited access to real-time monitoring tools. This study applied the baseline YOLOv11 model and its optimized variant, YOLOv11-Opt, to automate the detection, classification, and monitoring of date fruit varieties and disease-related defects. The models were trained on a curated dataset of real-world images collected in Saudi Arabia and enhanced through advanced data augmentation techniques, dynamic label assignment (SimOTA++), and extensive hyperparameter optimization. The experimental results demonstrated that YOLOv11-Opt significantly outperformed the baseline YOLOv11, achieving an overall classification accuracy of 99.04% for date types and 99.69% for disease detection, with ROC-AUC scores exceeding 99% in most cases. The optimized model effectively distinguished visually complex diseases, such as scale insert and dry date skin, across multiple date types, enabling high-resolution, real-time inference. Furthermore, a visual analytics dashboard was developed to support strategic decision-making by providing insights into production trends, disease prevalence, and varietal distribution. These findings underscore the value of integrating optimized deep learning architectures and visual analytics for intelligent, scalable, and sustainable precision agriculture. Full article

Presented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. Both models were trained and tested using a publicly available annotated dataset. YOLO, a single-stage detector, achieved a mAP@0.5 of 0.942 with a training time of approximately 2 h. It demonstrated strong generalization, especially in simpler conditions, and is well-suited for real-time applications due to its speed and lower computational requirements. Faster R-CNN, a two-stage detector using a ResNet-50 backbone, reached comparable accuracy (mAP@0.5 = 0.94) with slightly higher precision and recall. However, its training required significantly more time (approximately 19 h) and resources. Deep learning metrics analysis confirmed both models performed reliably, with YOLO favoring inference speed and Faster R-CNN offering improved robustness under occlusion and variable lighting. Practical recommendations are provided for model selection based on application needs—YOLO for mobile or field robotics and Faster R-CNN for high-accuracy offline tasks. Additional conclusions highlight the benefits of GPU acceleration and high-resolution inputs. The study contributes to the growing body of research on AI deployment in precision agriculture and provides insights into the development of intelligent harvesting and crop monitoring systems. Full article

Almond (Prunus dulcis Mill.) is characterized by its water stress tolerance and adaptability to diverse management strategies, allowing it to maintain or even enhance almond quality while achieving optimal yields. Limited research has been conducted to date on how almond production and quality vary across different water regimes and production systems, or how tree age modulates crop responses to deficit irrigation and organic practices. This study examines the effects of regulated deficit irrigation (RDI) under organic (OPS) and conventional (CPS) production systems, analyzing the impact on nut quality (physical and chemical parameters) and its sensorial properties in an almond orchard during seasons in 2019 and 2023, when the trees were 3-years old and when they were close to their yield potential at 7-years old, respectively. The PS and irrigation strategy affected the nut quality, yield, and tree growth. The OPS and RDI methods accumulated season-dependent yield losses in both studied periods. The kernel weight under OPS was lower than CPS in 2019, with these differences being less evident in 2023. The highest antioxidant activity and total phenolic compound values were obtained with the OPS and RDI methods in 2019, whereas the sugar and organic acid contents showed improvements under the OPS and the RDI strategy during 2019 and 2023, respectively. Finally, significant improvements were observed in relation to the fatty acids profile for nuts harvested under OPS in both seasons, especially in the latter season with RDI. Thus, almond quality can be enhanced by the integration of both OPSs and RDI strategies, although these improvements are dependent on tree age. Full article

Interest in using warm-season grasses, including switchgrass (SG) (Panicum virgatum L.), as a bioenergy crop has increased in Europe. This study evaluated the effects of harvesting regimes with two cuts per year on the productivity, chemical composition and biochemical methane potential of the SG cultivars ‘Dacotah’, ‘Foresburg’ and ‘Cave in Rock’ in environments with cool and moderate climates in Europe with minimal fertilizer application. The results of two harvest years suggest that the biomass yield, chemical composition and energy potential depend on the grass cultivars and harvesting time. Significant effects (p < 0.05) of the harvest date and cultivar were observed for most of the measured parameters for biomass and silage quality. All three SG cultivars harvested on August 8 produced the lowest (p < 0.05) volume of methane per kg of biomass (181–202 normal litres (NL) per kg⁻¹ volatile solids (VS)) compared to the biomass of the respective cultivar harvested on 14 July (287–308 NL kg⁻¹ VS) or on October 3, as regrowth after the first cut made in mid-July (274–307 NL kg⁻¹ VS). The stands of all three SG cultivars, when the first harvest was completed in mid-July, achieved a higher annual area-specific methane yield than those harvested first in August (1128–1900 Nm³ ha⁻¹ and 888–1332 Nm³ ha⁻¹, respectively). Depending on the harvest regime and cultivar, the annual gross energy presented as a lower heating value varied from 31.8 GJ ha⁻¹ to 68.0 GJ ha⁻¹. It is concluded that SG growing under the cool temperate climate of Northern Europe could be an interesting alternative crop for methane production. Our study proves that the cultivar choice also plays an important role. Full article

The acute shortage of agricultural labor necessitates the development of predictive models to optimize farm operations. This study investigates the effectiveness of artificial-intelligence-driven models to accurately forecast the timing and yield of persimmon harvests, using meteorological data alongside historical harvest records. An artificial neural network was designed to estimate peak harvest dates by analyzing key meteorological variables. The model was trained and validated using data from the JA Nara Prefecture Nishiyoshino Sorting Facility and Nara Prefecture Agriculture Research and Development Center. Its reliability was confirmed based on mean absolute error, demonstrating the ability to make predictions with an accuracy of approximately three days. Additionally, extreme gradient boosting models were developed to predict yields, incorporating elevation data to refine predictions at the field scale. The model was trained and validated using data from fields cultivated in the Gojo-Yoshino region. The effectiveness of these models was evaluated using root mean square error, demonstrating an improvement in prediction accuracy of up to 20% with the inclusion of elevation data, illustrating their capability to effectively capture yield variations across different orchards. These models can significantly improve labor management, harvest scheduling, and overall productivity within the realm of smart agriculture. Full article

Sustainability in food production is a pressing priority due to environmental and political crises, the need for long-term food security, and feeding the populace. Food producers need to increasingly adopt sustainable practices to reduce negative environmental impacts and food waste. The ocean is a source for sustainable food systems; deforestation, water scarcity, and greenhouse gas emissions burden traditional, terrestrial resources. Our oceans contain the largest unexploited resource in the world in the form of mesopelagic fish species, with an estimated biomass of 10 billion metric tons. This resource is largely untapped due in part to the difficulties in harvesting these species. To ensure sustainability of this resource, management of fish stocks and fish processing practices must be optimised. Generation of fish protein hydrolysates from by-catch/underutilised species creates high-value, functional ingredients while also reducing waste. Marine hydrolysates offer a renewable source of nutrition and align with the principles of the circular economy, where waste is minimised and resources are reused efficiently. Ocean-derived solutions demand fewer inputs, generate less pollution, and have a smaller carbon footprint compared to traditional agriculture. This review collates clearly and succinctly the current and potential uses of FPHs for different market sectors and highlights the advantages of their use in terms of the scientifically validated health benefits for humans and animals and fish, and the protection and crop yield benefits that are documented to date from scientific studies. Full article