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20 pages, 33056 KB  
Article
Spatiotemporal Analysis of Vineyard Dynamics: UAS-Based Monitoring at the Individual Vine Scale
by Stefan Ruess, Gernot Paulus and Stefan Lang
Remote Sens. 2025, 17(19), 3354; https://doi.org/10.3390/rs17193354 - 2 Oct 2025
Abstract
The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and [...] Read more.
The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R2 = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article
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22 pages, 8178 KB  
Article
Vibration Control and Energy Harvesting of a Two-Degree-of-Freedom Nonlinear Energy Sink to Primary Structure Under Transient Excitation
by Xiqi Lin, Xiaochun Nie, Junjie Fu, Yangdong Qin, Lingzhi Wang and Zhitao Yan
Buildings 2025, 15(19), 3561; https://doi.org/10.3390/buildings15193561 - 2 Oct 2025
Abstract
Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can [...] Read more.
Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can supply power to the structural health monitoring sensor device. In this work, the electromechanical-coupled governing equations of the primary structure coupled with the series-connected 2-degree-of-freedom NES (2-DOF NES) integrated by a piezoelectric energy harvester are derived. The absorption and dissipation performances of the system under varying transient excitation intensities are investigated. Additionally, the targeted energy transfer mechanism between the primary structure and the two NESs oscillators is investigated using the wavelet analysis. The reduced slow flow of the dynamical system is explored through the complex-variable averaging method, and the primary factors for triggering the target energy transfer phenomenon are revealed. Furthermore, a comparison is made between the vibration suppression performance of the single-degree-of-freedom NES (S-DOF NES) system and the 2-DOF NES system as a function of external excitation velocity. The results indicate that the vibration suppression performance of the first-level NES (NES1) oscillator is first stimulated. As the external excitation intensity gradually increases, the vibration suppression performance of the second-level NES (NES2) oscillator is also triggered. The 1:1:1, high-frequency, and low-frequency transient resonance captures are observed between the primary structure and NES1 and NES2 oscillators over a wide frequency range. The 2-DOF NES demonstrates superior efficiency in suppressing vibrations of the primary structure and exhibits enhanced robustness to varying external excitation intensities. This provides a new strategy for structural vibration suppression and online power supply for health monitoring devices. Full article
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15 pages, 1468 KB  
Article
Performance Comparison of Hybrid and Standalone Piezoelectric Energy Harvesters Under Vortex-Induced Vibrations
by Issam Bahadur, Hassen Ouakad, El Manaa Barhoumi, Asan Muthalif, Muhammad Hafizh, Jamil Renno and Mohammad Paurobally
Modelling 2025, 6(4), 120; https://doi.org/10.3390/modelling6040120 - 2 Oct 2025
Abstract
This study investigates the effect of incorporating an electromagnetic harvester inside the bluff body of a 2-DoF hybrid harvester in comparison to a standalone piezoelectric harvester for various external loads. The harvester is excited through a vortex-induced vibration owing to the resultant wake [...] Read more.
This study investigates the effect of incorporating an electromagnetic harvester inside the bluff body of a 2-DoF hybrid harvester in comparison to a standalone piezoelectric harvester for various external loads. The harvester is excited through a vortex-induced vibration owing to the resultant wake vortices created behind the bluff body. The coupled dynamics of the two harvester components are modeled, and numerical simulations are conducted to evaluate the system’s performance under varying electrical loads. Numerical results show that at high, optimum electrical load, the standalone piezoelectric harvester outperforms the hybrid harvester. Nevertheless, for small electrical loads, the results show that the hybrid harvester outperforms the standalone PZT harvester by up to 18% in peak power output, while reducing the bandwidth by approximately 10% compared to the standalone piezoelectric harvester. Optimal spring stiffness values were identified, with the hybrid harvester achieving its maximum output power at a spring stiffness of 83.56 N/m. These findings underscore the need for careful design considerations, as the hybrid harvester may not achieve enhanced power output and bandwidth under higher electrical loads. Full article
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23 pages, 808 KB  
Article
Integrated Effects of Tillage Intensity, Genotype, and Weather Variability on Growth, Yield, and Grain Quality of Winter Wheat in Maize–Wheat Rotation
by Jan Buczek, Beata Michalska-Klimczak, Renata Tobiasz-Salach and Dorota Gawęda
Agriculture 2025, 15(19), 2069; https://doi.org/10.3390/agriculture15192069 - 1 Oct 2025
Abstract
The aim of the study was to compare grain yield, grain quality, and morphophysiological parameters of three winter wheat cultivars: Kilimanjaro, Hymalaya, and Ostroga. The cultivars were grown in crop rotation after grain maize harvest, using three tillage systems: conventional (C), reduced (R), [...] Read more.
The aim of the study was to compare grain yield, grain quality, and morphophysiological parameters of three winter wheat cultivars: Kilimanjaro, Hymalaya, and Ostroga. The cultivars were grown in crop rotation after grain maize harvest, using three tillage systems: conventional (C), reduced (R), and no-tillage (N). A three-year field experiment was conducted in southeastern Poland. Compared to no-tillage, the use of conventional and reduced systems resulted in higher grain yield, increased leaf area index and relative chlorophyll content, and higher gas exchange parameters. In the conventional system, the highest grain yield was achieved by cvs. Hymalaya and Ostroga, while in no-tillage and reduced, it was cv. Hymalaya. Compared to no-tillage, the conventional system resulted in higher values of grain quality parameters, while simultaneously reducing ash content, and the reduced system promoted a better gluten index. Interactions between cultivar and tillage system demonstrated good grain quality in terms of protein, falling number, and gluten index. Gluten content above 25.0% was found in grains of cvs. Kilimanjaro and Hymalaya in the reduced and conventional systems, and cv. Ostroga in the conventional system. The dry and semi-drought periods in the 2018/2019 season were conducive to more favorable grain quality parameter values: protein, gluten, falling number, and ash. However, the resulting grain was characterized by a lower gluten index and lower physical parameters. Cvs. Hymalaya and Ostroga are recommended for cultivation in conventional and reduced tillage systems, and cv. additionally for no-tillage systems. Growing the cv. Kilimanjaro in no-tillage and reduced tillage systems, and the cv. Ostroga in a no-tillage system, will result in lower grain yields. Full article
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18 pages, 2980 KB  
Article
Deep Learning-Based Identification of Kazakhstan Apple Varieties Using Pre-Trained CNN Models
by Jakhfer Alikhanov, Tsvetelina Georgieva, Eleonora Nedelcheva, Aidar Moldazhanov, Akmaral Kulmakhambetova, Dmitriy Zinchenko, Alisher Nurtuleuov, Zhandos Shynybay and Plamen Daskalov
AgriEngineering 2025, 7(10), 331; https://doi.org/10.3390/agriengineering7100331 - 1 Oct 2025
Abstract
This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on [...] Read more.
This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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15 pages, 1676 KB  
Article
Advancing Intercropping of Drought-Resistant Oilseed Crops: Mechanized Harvesting
by Luca Cozzolino, Simone Bergonzoli, Gian Maria Baldi, Michele Falce and Luigi Pari
AgriEngineering 2025, 7(10), 330; https://doi.org/10.3390/agriengineering7100330 - 1 Oct 2025
Abstract
Adverse climatic dynamics in recent years have intensified the need for resilient and multifunctional agricultural systems that integrate productivity, ecological sustainability, and socio-economic viability. This study evaluates the harvesting performance of three cropping systems: intercropping of cardoon and safflower (IT) and monocultures of [...] Read more.
Adverse climatic dynamics in recent years have intensified the need for resilient and multifunctional agricultural systems that integrate productivity, ecological sustainability, and socio-economic viability. This study evaluates the harvesting performance of three cropping systems: intercropping of cardoon and safflower (IT) and monocultures of cardoon (DC) and safflower (DS). Field trials were conducted during three following growing seasons to assess key harvesting parameters, including working speed, effective field capacity, harvesting costs, biomass yield, seed yield, seed losses, and seed moisture content. DC demonstrated the better performance, with a working speed of 6.35 ha h−1 and a field capacity of 2.56 ha h−1, also resulting in the lowest harvesting cost (EUR 70.24 ha−1). In contrast, IT exhibited the lowest performance and the highest cost (EUR 98.61 ha−1). DS achieved the highest effective seed yield (1.394 Mg ha−1), while IT produced the greatest biomass (22.96 Mg ha−1). Seed losses were lowest in DS (0.020 Mg ha−1) and highest in IT (0.425 Mg ha−1). Moisture content ranged from 5.82% in DC to 9.40% in DS. These findings highlighted the trade-offs between productivity, efficiency, and system complexity, offering valuable insights into the comparative performance and sustainability of innovative cropping systems under changing climatic conditions. Full article
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18 pages, 30918 KB  
Article
Beyond Local Indicators: Integrating Aggregated Runoff into Rainwater Harvesting Potential Mapping
by Christy Mathew Damascene, Irene Pomarico, Aldo Fiori and Antonio Zarlenga
Water 2025, 17(19), 2866; https://doi.org/10.3390/w17192866 - 1 Oct 2025
Abstract
Water scarcity, driven by over-consumption, population growth, climate change, and pollution, poses severe threats to both human health and ecosystems. Rainwater harvesting (RWH) has emerged as a sustainable solution to mitigate these impacts, offering environmental, social, and economic benefits. Traditional RWH site selection [...] Read more.
Water scarcity, driven by over-consumption, population growth, climate change, and pollution, poses severe threats to both human health and ecosystems. Rainwater harvesting (RWH) has emerged as a sustainable solution to mitigate these impacts, offering environmental, social, and economic benefits. Traditional RWH site selection methods rely heavily on GIS-based Multi-Criteria Approaches, such as the Analytical Hierarchy Process, which typically assess runoff potential at the pixel scale using proxy indicators like runoff coefficients or drainage density. However, these methods often overlook horizontal water fluxes and temporal variability, leading to underestimation of the actual runoff available for harvesting. This study introduces an innovative enhancement to AHP/GIS-based methodologies for rainwater harvesting (RWH) site selection by incorporating Aggregated Runoff (AR) as a key criterion. Unlike traditional approaches, the use of AR—representing the total upstream surface water collected at each pixel—enables a more realistic and accurate assessment of RWH potential without increasing data or computational requirements. The proposed criterion is independent of the specific methodology or data layers adopted, making it broadly applicable and easily integrable into existing frameworks. The methodology is applied to the upper Tiber River catchment in Central Italy, demonstrating that AR-based assessments yield more realistic RWH potential maps compared to conventional methods. Additionally, the study proposes a quantile-based scoring system to account for inter-annual hydrological variability, enhancing the robustness of site selection under changing climate conditions. Full article
(This article belongs to the Topic Water Management in the Age of Climate Change)
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18 pages, 2070 KB  
Article
Changes in Soil Physical Quality, Root Growth, and Sugarcane Crop Yield During Different Successive Mechanized Harvest Cycles
by Igor Queiroz Moraes Valente, Zigomar Menezes de Souza, Gamal Soares Cassama, Vanessa da Silva Bitter, Jeison Andrey Sanchez Parra, Euriana Maria Guimarães, Reginaldo Barboza da Silva and Rose Luiza Moraes Tavares
AgriEngineering 2025, 7(10), 325; https://doi.org/10.3390/agriengineering7100325 - 1 Oct 2025
Abstract
Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and [...] Read more.
Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article
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35 pages, 1628 KB  
Review
Production Systems and Feeding Strategies in the Aromatic Fingerprinting of Animal-Derived Foods: Invited Review
by Eric N. Ponnampalam, Gauri Jairath, Ishaya U. Gadzama, Long Li, Sarusha Santhiravel, Chunhui Ma, Mónica Flores and Hasitha Priyashantha
Foods 2025, 14(19), 3400; https://doi.org/10.3390/foods14193400 - 1 Oct 2025
Abstract
Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), [...] Read more.
Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article
(This article belongs to the Special Issue Novel Insights into Food Flavor Chemistry and Analysis)
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19 pages, 2933 KB  
Article
Image-Based Detection of Chinese Bayberry (Myrica rubra) Maturity Using Cascaded Instance Segmentation and Multi-Feature Regression
by Hao Zheng, Li Sun, Yue Wang, Han Yang and Shuwen Zhang
Horticulturae 2025, 11(10), 1166; https://doi.org/10.3390/horticulturae11101166 - 1 Oct 2025
Abstract
The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each [...] Read more.
The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article
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10 pages, 458 KB  
Article
Preliminary Investigation of Nitrogen Rate Influence on Irrigated Bermudagrass Forage Production
by Bronc Finch and Lance Blythe
Nitrogen 2025, 6(4), 88; https://doi.org/10.3390/nitrogen6040088 - 1 Oct 2025
Abstract
Bermudagrass (Cynodon dactylon) forage production recommendations are often developed in natural environments with available water limitations, often resulting in highly variable responses and lower average responses. As farmland ownership changes and agriculture and irrigation technologies become more affordable the amount of [...] Read more.
Bermudagrass (Cynodon dactylon) forage production recommendations are often developed in natural environments with available water limitations, often resulting in highly variable responses and lower average responses. As farmland ownership changes and agriculture and irrigation technologies become more affordable the amount of irrigated hay production has increased. While much of the agronomic management does not differ between rain-fed and irrigated environments, nutrient use and uptake dynamics may. This requires a reevaluation and potential adjustment of current recommendations to allow for increased yield potential of irrigated production systems without detrimental impacts on the system. The objective of this study was to identify the need for further investigation of nitrogen application rates for forage bermudagrass production under irrigated conditions. Nitrogen applications of 0 to 280 kg N ha−1, in 56 kg increments, were applied at spring green-up and following the first and second harvests. Dry matter biomass, crude protein, and total digestible nutrients increased with increasing nitrogen application rate, while yield and profit maximizing rates both exceeded the typical recommended rate for bermudagrass hay production. The responses noted for increased nitrogen application rates indicate the need for further investigation of N requirements of non-moisture-limited hay production. Full article
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18 pages, 1702 KB  
Article
Optimizing Winter Composting of Swine Manure Through Housefly Larva Bioconversion: Mechanisms of Protein Recovery and Enzymatic Nitrogen Regulation
by Nanyang Lu, Yanlai Yao, Chunlai Hong, Weijing Zhu, Leidong Hong, Tao Zhang, Rui Guo, Chengrong Ding, Ying Zhou and Fengxiang Zhu
Agronomy 2025, 15(10), 2324; https://doi.org/10.3390/agronomy15102324 - 30 Sep 2025
Abstract
Sustainable manure recycling in cold climates faces low efficiency and nutrient loss. This study evaluated housefly larva-pretreated manure (HL) for winter swine manure composting in East China, comparing it to sawdust-conditioned (CK2) and untreated manure (CK1). Larval pretreatment converted 12.71% of manure weight [...] Read more.
Sustainable manure recycling in cold climates faces low efficiency and nutrient loss. This study evaluated housefly larva-pretreated manure (HL) for winter swine manure composting in East China, comparing it to sawdust-conditioned (CK2) and untreated manure (CK1). Larval pretreatment converted 12.71% of manure weight into biomass, assimilating 10.69% C, 30.55% N, 8.54% P, and 11.53% K. Harvested larvae contained 53.35% crude protein, with amino acids matching/exceeding fishmeal and soybean meal, while heavy metals were below safety limits. Theoretical annual larval protein yield per unit area (29,530 kg·mu−1·year−1) was 206.5 times higher than soybean crops. During composting, the HL treatment promoted early protease and catalase activation. This enzymatic synergy accelerated organic matter degradation and maturation, achieving a germination index of 147.67% by day 51. Coordinated nitrate and nitrite reductase activity in HL facilitated efficient denitrification, minimizing NO2 accumulation and N2O emissions. Consequently, HL composting achieved faster stabilization, enhanced nutrient retention, and greater protein recovery compared to controls. These findings demonstrate that housefly larval pretreatment offers a climate-resilient and scalable strategy for winter manure management and protein valorization, with strong potential for applications in cold and resource-limited agricultural systems worldwide. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
16 pages, 1292 KB  
Article
Relationship Between the Morphometric and Nutritional Variables of Bananas (Musa AAA, Cavendish cv. Williams Subgroup) and the Formation of Maturity Bronzing
by Ana María Martínez, Aquiles Enrique Darghan, Nair González, Mateo Fandiño and Helber Enrique Balaguera López
Agronomy 2025, 15(10), 2316; https://doi.org/10.3390/agronomy15102316 - 30 Sep 2025
Abstract
Bananas (Musa AAA) can be affected by maturity bronzing, a physiological disorder that appears in the form of bronzing on the fruit’s epidermis, compromising its quality and generating economic losses due to commercial rejection. Since the symptoms are only evidenced at harvest, [...] Read more.
Bananas (Musa AAA) can be affected by maturity bronzing, a physiological disorder that appears in the form of bronzing on the fruit’s epidermis, compromising its quality and generating economic losses due to commercial rejection. Since the symptoms are only evidenced at harvest, it is necessary to identify the associated factors in order to develop preventive strategies. This research analyzed morphometric and nutritional characteristics potentially related to the formation of stains on two banana-exporting farms in Antioquia and Magdalena (Colombia). Monitoring plots were established, in which 310 productive units with the banana Cavendish cv. Williams subgroup were evaluated over 52 weeks. In all units, the height, the pseudostem perimeter, the weight and number of hands of the bunch, and the weight of the affected fruit were recorded. In addition, foliar and soil analyses were conducted in each production unit, and some climatic components were characterized. Through a multiple logistic regression model, it was observed that a pseudostem perimeter smaller than 70 cm (measured 50 cm from the base), together with foliar B and Zn concentrations below 100 and 25 mg/kg, respectively, was associated with a higher probability of bronzing formation. These values should be interpreted as preliminary associations derived under specific conditions, and therefore as requiring validation across different contexts and management systems, before being considered as reference parameters. These findings provide new factors associated with maturity bronzing and open opportunities for future research aimed at its prevention. Full article
(This article belongs to the Special Issue Role of Mineral Nutrition in Alleviation of Abiotic Stress in Crops)
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43 pages, 2854 KB  
Review
Strategies for Enhancing BiVO4 Photoanodes for PEC Water Splitting: A State-of-the-Art Review
by Binh Duc Nguyen, In-Hee Choi and Jae-Yup Kim
Nanomaterials 2025, 15(19), 1494; https://doi.org/10.3390/nano15191494 - 30 Sep 2025
Abstract
Bismuth vanadate (BiVO4) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic [...] Read more.
Bismuth vanadate (BiVO4) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic limitations, including poor charge carrier mobility, short diffusion length, and sluggish oxygen evolution reaction (OER) kinetics. This review critically summarizes recent advancements aimed at enhancing BiVO4 PEC performance, encompassing synthesis strategies, defect engineering, heterojunction formation, cocatalyst integration, light-harvesting optimization, and stability improvements. Key fabrication methods—such as solution-based, vapor-phase, and electrochemical approaches—along with targeted modifications, including metal/nonmetal doping, surface passivation, and incorporation of electron transport layers, are discussed. Emphasis is placed on strategies to improve light absorption, charge separation efficiency (ηsep), and charge transfer efficiency (ηtrans) through bandgap engineering, optical structure design, and catalytic interface optimization. Approaches to enhance stability via protective overlayers and electrolyte tuning are also reviewed, alongside emerging applications of BiVO4 in tandem PEC systems and selective solar-driven production of value-added chemicals, such as H2O2. Finally, critical challenges, including the scale-up of electrode fabrication and the elucidation of fundamental reaction mechanisms, are highlighted, providing perspectives for bridging the gap between laboratory performance and practical implementation. Full article
20 pages, 1991 KB  
Article
EcoWild: Reinforcement Learning for Energy-Aware Wildfire Detection in Remote Environments
by Nuriye Yildirim, Mingcong Cao, Minwoo Yun, Jaehyun Park and Umit Y. Ogras
Sensors 2025, 25(19), 6011; https://doi.org/10.3390/s25196011 - 30 Sep 2025
Abstract
Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce [...] Read more.
Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce EcoWild, a reinforcement learning-driven cyber-physical system for energy-adaptive wildfire detection on solar-powered edge devices. EcoWild combines a decision tree-based fire risk estimator, lightweight on-device smoke detection, and a reinforcement learning agent that dynamically adjusts sensing and communication strategies based on battery levels, solar input, and estimated fire risk. The system models realistic solar harvesting, battery dynamics, and communication costs to ensure sustainable operation on embedded platforms. We evaluate EcoWild using real-world solar, weather, and fire image datasets in a high-fidelity simulation environment. Results show that EcoWild consistently maintains responsiveness while avoiding battery depletion under diverse conditions. Compared to static baselines, it achieves 2.4× to 7.7× faster detection, maintains moderate energy consumption, and avoids system failure due to battery depletion across 125 deployment scenarios. Full article
(This article belongs to the Section Intelligent Sensors)
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