Agriculture

21 pages, 21303 KiB

Open AccessArticle

Research on Delineation and Assessment Methods for Cultivated Land Concentration and Contiguity in Southeastern China

by Lei Wang, Rong Zhao, Chun Dong, Chaoying He, Xiaochen Kang, Lina Zhang, Dong Wei, Junsong Zhou, Lihua He, Xiaoding Liu and Yingchun Wang

Agriculture 2025, 15(17), 1803; https://doi.org/10.3390/agriculture15171803 (registering DOI) - 23 Aug 2025

Abstract

Cultivated land concentration and contiguity, as a core element of agricultural modernization development, holds strategic significance for enhancing agricultural production efficiency and ensuring national food security. This study employs vector patches as research units and classifies spatial connections between patches into direct and [...] Read more.

Cultivated land concentration and contiguity, as a core element of agricultural modernization development, holds strategic significance for enhancing agricultural production efficiency and ensuring national food security. This study employs vector patches as research units and classifies spatial connections between patches into direct and indirect connections. We quantify six types of spatial relationships between patches using binary encoding, enabling precise delineation of concentrated contiguous cultivated land. A Patch Connectivity Index is proposed. Combined with the Patch Area Index and Patch Shape Index, an evaluation system for cultivated land concentration and contiguity is established. Using Suixi County as a case study, we investigate the spatiotemporal evolution of its cultivated land concentration and contiguity from 2019 to 2023. Overall, patch connectivity exhibits a “single-element dominant, multi-element complementary” structural pattern, while the evaluation grading of cultivated land concentration and contiguity follows a normal distribution. Between 2019 and 2023, the average patch area decreased while the average number of connections between patches increased, indicating significant improvement in cultivated land concentration and contiguity levels. By adjusting spatial relationships between patches, the effective integration and utilization of cultivated land resources can provide theoretical foundations and practical references for agricultural modernization development. Full article

(This article belongs to the Section Agricultural Technology)

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28 pages, 1187 KiB

Open AccessArticle

An Attention-Enhanced Bottleneck Network for Apple Segmentation in Orchard Environments

by Imran Md Jelas, Nur Alia Sofia Maluazi and Mohd Asyraf Zulkifley

Agriculture 2025, 15(17), 1802; https://doi.org/10.3390/agriculture15171802 (registering DOI) - 23 Aug 2025

Abstract

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard [...] Read more.

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard environments due to factors such as occlusion, background clutter, and varying lighting conditions. This study proposes the Depthwise Asymmetric Bottleneck with Attention Mechanism Network (DABAMNet), an advanced convolutional neural network (CNN) architecture composed of multiple Depthwise Asymmetric Bottleneck Units (DABou), specifically designed to improve apple segmentation in RGB imagery. The model incorporates the Convolutional Block Attention Module (CBAM), a dual attention mechanism that enhances channel and spatial feature discrimination by adaptively emphasizing salient information while suppressing irrelevant content. Furthermore, the CBAM attention module employs multiple global pooling strategies to enrich feature representation across varying spatial resolutions. Through comprehensive ablation studies, the optimal configuration was identified as early CBAM placement after DABou unit 5, using a reduction ratio of 2 and combined global max-min pooling, which significantly improved segmentation accuracy. DABAMNet achieved an accuracy of 0.9813 and an Intersection over Union (IoU) of 0.7291, outperforming four state-of-the-art CNN benchmarks. These results demonstrate the model’s robustness in complex agricultural scenes and its potential for real-time deployment in fruit detection and harvesting systems. Overall, these findings underscore the value of attention-based architectures for agricultural image segmentation and pave the way for broader applications in sustainable crop monitoring systems. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

22 pages, 12018 KiB

Open AccessArticle

Effects of Winter Green Manure Incorporation on Grain Yield, Nitrogen Uptake, and Nitrogen Use Efficiency in Different Ratoon Rice Varieties

by Qiwen Hou, Pufan Shao, Sheng Chen, Zhangzhen Yang, Zhixiong Yuan, Liusheng Zhong, Ziyuan Zhao, Yu Wang, Cuo Ga, Jiarui Tang, Yaoyun Xu, Yanfu Zeng, Cong Yu, Cheng Huang and Ying Xu

Agriculture 2025, 15(17), 1801; https://doi.org/10.3390/agriculture15171801 - 22 Aug 2025

Abstract

This study evaluated the effects of winter green manure incorporation on grain yield, nitrogen uptake, and use efficiency in ratoon rice production. A two-year field experiment (2019–2021) was conducted using a split-plot design, with main plots comprising three cropping systems: fallow–ratoon rice (FA), [...] Read more.

This study evaluated the effects of winter green manure incorporation on grain yield, nitrogen uptake, and use efficiency in ratoon rice production. A two-year field experiment (2019–2021) was conducted using a split-plot design, with main plots comprising three cropping systems: fallow–ratoon rice (FA), rapeseed–ratoon rice (RA), and milk vetch–ratoon rice (MV). In the RA and MV systems, green manures were incorporated in situ, while subplots featured two ratoon rice varieties (Yliangyou 911, YLY911; Liangyou 6326, LY6326). Compared to FA treatment, RA and MV treatments significantly increased main crop yields by 16.37% and 9.31%, respectively, with corresponding annual total yield improvements of 11.34% and 7.78%. Under RA treatment, LY6326 achieved significantly higher yields than YLY911. Biomass accumulation analysis revealed that RA and MV treatments enhanced plant dry matter by 24.40% and 5.63% at heading stage, and 9.83% and 7.47% at maturity, respectively, relative to FA treatment. Green manure incorporation improved plant nitrogen content at maturity (9.42% and 10.29% for RA and MV, respectively) and panicle nitrogen accumulation (11.73% and 38.26%, respectively) compared to fallow treatment. Nitrogen use efficiency metrics demonstrated that RA and MV treatments enhanced nitrogen harvest index by 1.54% and 5.65%, respectively, while nitrogen partial factor productivity increased by 11.34% and 7.78%. Varietal comparison confirmed that LY6326 exhibited superior nitrogen accumulation and utilization compared to YLY911. These findings demonstrate that winter green manure incorporation significantly enhances grain yield and nitrogen use efficiency in ratoon rice systems, providing a scientific foundation for developing sustainable and productive rice cropping practices. Full article

(This article belongs to the Special Issue Innovative Conservation Cropping Systems and Practices—2nd Edition)

24 pages, 5784 KiB

Open AccessArticle

Analysis and Optimization of Seeding Depth Control Parameters for Wide-Row Uniform Seeding Machines for Wheat

by Longfei Yang, Zenglu Shi, Yingxue Xue, Xuejun Zhang, Shenghe Bai, Jinshan Zhang and Yufei Jin

Agriculture 2025, 15(17), 1800; https://doi.org/10.3390/agriculture15171800 - 22 Aug 2025

Abstract

Seeding depth is a critical factor influencing the uniformity and vigor of wheat seedlings. To address inconsistent seeding depth in wide-row uniform seeding agricultural practices, we performed parameter analysis and optimization experiments on the seeding depth device of a wheat wide-row uniform seeding [...] Read more.

Seeding depth is a critical factor influencing the uniformity and vigor of wheat seedlings. To address inconsistent seeding depth in wide-row uniform seeding agricultural practices, we performed parameter analysis and optimization experiments on the seeding depth device of a wheat wide-row uniform seeding machine. The structure and working principle of the device were described, soil movement during operation was analyzed, and the models of rotary tiller blades and soil retention plates were investigated, identifying three key factors affecting seeding quality. Using the discrete element method, a model of the seeding depth device was established, and experiments were conducted, yielding the following conclusions: 1. Single-factor experiments were conducted under different seeding rate conditions, and it was found that the effects of various factors on the two indicators, namely the seeding depth qualification rate and the coefficient of variation for seeding uniformity, were regular. 2. A quadratic orthogonal rotated combination experiment with three factors determined the optimal structural parameters: tillage device penetration depth of 120 mm, rotational speed of 310 rpm, and soil retention plate inclination angle of 27°. Under these parameters, the seed depth qualification rate exceeded 90%, and the coefficient of variation for seed distribution uniformity was below 25%. 3. Field validation tests under optimal parameters confirmed a seed depth qualification rate ≥90% and variation for seed distribution uniformity was below ≤20.69%. 4. The error between simulation and field tests was ≤5%, validating the reliability of the discrete element method-based optimization for the seeding depth device. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 1538 KiB

Open AccessReview

Application of Digital Twin Technology in Smart Agriculture: A Bibliometric Review

by Rajesh Gund, Chetan M. Badgujar, Sathishkumar Samiappan and Sindhu Jagadamma

Agriculture 2025, 15(17), 1799; https://doi.org/10.3390/agriculture15171799 - 22 Aug 2025

Abstract

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, [...] Read more.

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

(This article belongs to the Special Issue Intelligent Sensing and Edge AI-Driven Systems for Precision Agriculture)

19 pages, 3915 KiB

Open AccessArticle

Field Schedule of UAV-Assisted Pollination for Hybrid Rice Based on CFD–DPM Coupled Simulation

by Le Long, Peng Fang, Jinlong Lin, Muhua Liu, Xiongfei Chen, Liping Xiao, Yonghui Li and Yihan Zhou

Agriculture 2025, 15(17), 1798; https://doi.org/10.3390/agriculture15171798 - 22 Aug 2025

Abstract

UAV pollination holds significant promise for enhancing hybrid rice seed production, yet the mechanisms of pollen diffusion under UAV downwash and the lack of theoretical guidance for operational parameter optimization remain critical challenges. To address this, this study employed a coupled Computational Fluid [...] Read more.

UAV pollination holds significant promise for enhancing hybrid rice seed production, yet the mechanisms of pollen diffusion under UAV downwash and the lack of theoretical guidance for operational parameter optimization remain critical challenges. To address this, this study employed a coupled Computational Fluid Dynamics–Discrete Phase Model (CFD–DPM) numerical simulation to systematically investigate the interaction between the UAV-induced wind field and pollen particles. A validated CFD model was first developed to characterize the UAV wind-field distribution, demonstrating good agreement with field measurements. Building upon this, a coupled wind field–pollen CFD–DPM model was established, enabling a detailed visualization and analysis of airflow patterns and pollen transport dynamics under varying flight parameters (speed and height). Using the pollen disturbance area and effective settling range as key evaluation metrics, the optimal pollination parameters were identified as a flight speed of 3 m/s and a height of 4 m. Field validation trials confirmed that UAV-assisted pollination using these optimized parameters significantly increased the seed yield by 21.4% compared to traditional manual methods, aligning closely with simulation predictions. This study establishes a robust three-tier validation framework (“numerical simulation—wind-field verification—field validation”) that provides both theoretical insights and practical guidance for optimizing UAV pollination operations. The framework demonstrates strong generalizability for improving the efficiency and mechanization level of hybrid rice seed production. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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36 pages, 2737 KiB

Open AccessArticle

Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress

by Savoini Guglielmo, De Marinis Pietro, Casson Andrea, Abhishek Dattu Narote, Riccardo Guidetti, Stefano Bocchi and Valentina Vaglia

Agriculture 2025, 15(17), 1797; https://doi.org/10.3390/agriculture15171797 - 22 Aug 2025

Abstract

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a [...] Read more.

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested. Full article

(This article belongs to the Section Agricultural Systems and Management)

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Graphical abstract

17 pages, 556 KiB

Open AccessArticle

The Impact of Cultivars and Biostimulants on the Compounds Contained in Glycine max (L.) Merr. Seeds

by Katarzyna Rymuza, Elżbieta Radzka and Joanna Cała

Agriculture 2025, 15(17), 1796; https://doi.org/10.3390/agriculture15171796 - 22 Aug 2025

Abstract

Background: Soybean (Glycine max (L.) Merr.), a nutrient-rich leguminous crop high in protein, lipids, and minerals, is extensively cultivated worldwide. The chemical composition of soybean seeds depends not only on the genetic characteristics of the cultivar but also on environmental conditions and [...] Read more.

Background: Soybean (Glycine max (L.) Merr.), a nutrient-rich leguminous crop high in protein, lipids, and minerals, is extensively cultivated worldwide. The chemical composition of soybean seeds depends not only on the genetic characteristics of the cultivar but also on environmental conditions and agricultural practices. In recent years, biostimulants have gained increasing importance in crop production due to their ability to enhance physiological processes in plants and potentially influence nutrient accumulation. This study aimed to investigate how cultivar and biostimulant type influence the chemical composition of soybean seeds under varying weather conditions in Central Europe. Methods: A three-year field experiment (2017–2019) was conducted in eastern Poland (Central Europe) using a split-plot design. The experimental factors included three non-GMO soybean cultivars (Abelina, Merlin, and SG Anser) and two foliar biostimulants (Asahi SL and Improver). In addition to classical ANOVA, the multivariate analysis of the impact of the investigated factors included principal component analysis (PCA). Results: The applied factors significantly affected seed contents of fat, protein, dry matter, ash, fibre, and macronutrients (N, P, K). Cv. Merlin had the highest fat (22.65%) and fibre content (9.33%), while Abelina showed the highest protein (37.06%) and dry matter content (94.42%). Biostimulant application increased the accumulation of several seed components. Asahi SL significantly enhanced fat content (by 0.69%), protein content (by over 1.5%), and dry matter content (by nearly 0.2%) compared to the control. Improver was more effective in increasing nitrogen (by 0.24%), phosphorus (by 0.5%), and potassium (by 0.15%) contents. Weather conditions throughout the growing seasons significantly altered the impact of the biostimulants. The PCA analysis revealed distinct relationships among the chemical properties of seeds, meteorological factors, and the applied biostimulants. Full article

(This article belongs to the Special Issue Sustainable Management of Legume Crops)

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17 pages, 1068 KiB

Open AccessArticle

Aspergillus oryzae Pellets as a Biotechnological Tool to Remove 2,4-D in Wastewater Set to Be Reused in Agricultural Ecosystems

by Karen Magnoli, Melisa Eglé Aluffi, Nicolás Benito, Carina Elizabeth Magnoli and Carla Lorena Barberis

Agriculture 2025, 15(17), 1795; https://doi.org/10.3390/agriculture15171795 - 22 Aug 2025

Abstract

Mismanagement of rural wastewater can lead to environmental contamination with the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). Fungi with bioremediating potential constitute a sustainable alternative to decontaminate such wastewater before its reuse. This study evaluated the ability of Aspergillus oryzae pellets to remove 2,4-D from [...] Read more.

Mismanagement of rural wastewater can lead to environmental contamination with the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). Fungi with bioremediating potential constitute a sustainable alternative to decontaminate such wastewater before its reuse. This study evaluated the ability of Aspergillus oryzae pellets to remove 2,4-D from natural and sterile rural wastewater (i.e., with/without native microbiota). The pellets were produced by incubating conidial solutions of A. oryzae strains RCA2, RCA4, RCA5, and RCA10 in synthetic wastewater for 21 days at 25 °C. The wastewater samples were characterized physicochemically and microbiologically upon arrival at the laboratory. Afterwards, they were supplemented with 1, 2.5, or 5 mmol L⁻¹ of 2,4-D and inoculated with the pellets. Physicochemical characterization was repeated throughout the experiment. Herbicide removal and the presence of 2,4-D degradation intermediate, 2,4-dichlorophenol (2,4-DCP), were assessed through high-pressure liquid chromatography with UV/Vis detection (HPLC-UV) and mass spectrometry. At the beginning of the assay, the macro- and micronutrient content in the samples were suitable to sustain fungal growth. By the end, pH had increased and sodium and nitrate levels decreased in comparison with the control. RCA2, RCA4, and RCA10 removed over 80% of 2,4-D after 7 days of incubation, at the three herbicide concentrations tested. Moreover, wet fungal biomass had increased by the end of the assay. These findings demonstrate that RCA2, RCA4, and RCA10 can grow, form pellets, and remove 2,4-D in natural rural wastewater, which makes them potential candidates for bioremediation strategies aimed at improving the quality of water set to be reused. Full article

(This article belongs to the Special Issue Advances in Sustainable Environmental Biotechnology and Bioprocess Engineering for Pollution Control in Different Agroecosystems)

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20 pages, 3484 KiB

Open AccessArticle

Monitoring Fertilizer Effects in Hardy Kiwi Using UAV-Based Multispectral Chlorophyll Estimation

by Sangyoon Lee, Hongseok Mun and Byeongeun Moon

Agriculture 2025, 15(16), 1794; https://doi.org/10.3390/agriculture15161794 - 21 Aug 2025

Abstract

This study addresses the need for efficient and non-destructive monitoring of the nutrient status of hardy kiwi (Actinidia arguta), a plantation crop native to East Asia. Traditional nutrient monitoring methods are labor-intensive and often destructive, limiting their practicality in precision agriculture. [...] Read more.

This study addresses the need for efficient and non-destructive monitoring of the nutrient status of hardy kiwi (Actinidia arguta), a plantation crop native to East Asia. Traditional nutrient monitoring methods are labor-intensive and often destructive, limiting their practicality in precision agriculture. To overcome these challenges, we deployed a rotary-wing unmanned aerial vehicle (UAV) equipped with a multispectral camera to capture monthly images of 10 hardy kiwi orchards in South Korea from June to October 2019. We extracted spectral bands (i.e., red, red-edge, green, and near-infrared) to generate normalized difference vegetation index and canopy chlorophyll content index maps, which were correlated with in situ chlorophyll measurements using a chlorophyll meter. Strong positive correlations were observed between vegetation indexes and actual chlorophyll content, with canopy chlorophyll content index achieving the highest predictive accuracy (average correlation coefficient > 0.84). Regression models based on multispectral data enabled reliable estimation of leaf chlorophyll across months and regions, with an average RMSE of 3.1. Our results confirmed that UAV-based multispectral imaging is an effective, scalable approach for real-time monitoring of nutrient status, supporting timely, site-specific fertilizer management. This method has the potential to enhance fertilizer efficiency, reduce environmental impact, and improve the quality of hardy kiwi cultivations. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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23 pages, 3539 KiB

Open AccessArticle

Design and Experimental Analysis of a Grinding Disc Buckwheat Dehulling Machine

by Ning Zhang, Wang Li, Lihong Li and Decong Zheng

Agriculture 2025, 15(16), 1793; https://doi.org/10.3390/agriculture15161793 - 21 Aug 2025

Abstract

Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine [...] Read more.

Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine was developed, featuring upper and lower discs with alternating deep–shallow composite textures to reduce kernel breakage and improve whole kernel rate. A 0–10 mm adjustable gap mechanism was incorporated to suit different buckwheat varieties and particle sizes, enhancing dehulling efficiency. Buckwheat grains were classified into four size ranges: 4.0–4.5 mm, 4.5–5.0 mm, 5.0–5.3 mm, and 5.3–5.7 mm. For all sizes, the optimal rotational speed was 12 r/min, with corresponding optimal gaps of 2.53 mm, 2.80 mm, 3.20 mm, and 3.40 mm, respectively. The whole-kernel rates under these conditions were 32.9%, 37.5%, 45.6%, and 55.1%, respectively, all above 30%, showing substantial improvement. For the 4.5–5.0 mm fraction, orthogonal tests revealed that a small gap (2.859 mm) achieved a dehulling rate of 89.9% and a whole-kernel rate of 38.03%, making it suitable for mass production. A larger gap (3.288 mm) combined with secondary dehulling increased the cumulative whole kernel rate to 50.26%, which is advantageous for producing high value-added products. The novel grinding disc structure balanced frictional and compressive forces on kernels, while the adjustable gap design improved adaptability. Combined with size classification and parameter optimization, this approach provides precise processing schemes for various buckwheat varieties and offers both theoretical and practical value for industrial application. Full article

(This article belongs to the Section Agricultural Technology)

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14 pages, 1520 KiB

Open AccessArticle

Spatiotemporal Analysis of Ventilation Efficiency in Single-Span Plastic Greenhouses in Hot-Humid Regions of China: Using Validated CFD Modeling

by Song Wang, Naimin Kong, Lirui Liang, Yuexuan He, Wenjun Peng, Xiaohan Lu, Chi Qin, Zijing Luo, Wei Zhao, Chengyao Jiang, Mengyao Li, Yangxia Zheng and Wei Lu

Agriculture 2025, 15(16), 1792; https://doi.org/10.3390/agriculture15161792 - 21 Aug 2025

Abstract

To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum [...] Read more.

To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum relative errors between simulated and measured values were 6% and 9%, respectively. Significant spatial heterogeneity in both temperature and airflow was observed. Vertically, temperature rose with height; horizontally, it declined from the center toward the sidewalls. Under prevailing meteorological conditions, the daily maxima occurred at distinct elevations above the fan-vent outlets. Airflow was most vigorous near the vents, whereas extensive stagnant zones aloft reduced overall ventilation efficiency. These findings provide a quantitative basis for designing single-span plastic film greenhouses in China’s hot–humid regions, informing ventilation improvements, and guiding future optimization efforts. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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18 pages, 2207 KiB

Open AccessArticle

Fermentation Regulation: Revealing Bacterial Community Structure, Symbiotic Networks to Function and Pathogenic Risk in Corn Stover Silage

by Zhumei Du, Shaojuan Cui, Yifan Chen, Yunhua Zhang, Siran Wang and Xuebing Yan

Agriculture 2025, 15(16), 1791; https://doi.org/10.3390/agriculture15161791 - 21 Aug 2025

Abstract

Improving agricultural by-product utilization can alleviate tropical feed shortages. This study used corn stover (CS, Zea mays L.) at the maturity stage as the material, with four silage treatments: control, lactic acid bacteria (LAB, Lactiplantibacillus plantarum), cellulase (AC, Acremonium cellulolyticus), and [...] Read more.

Improving agricultural by-product utilization can alleviate tropical feed shortages. This study used corn stover (CS, Zea mays L.) at the maturity stage as the material, with four silage treatments: control, lactic acid bacteria (LAB, Lactiplantibacillus plantarum), cellulase (AC, Acremonium cellulolyticus), and LAB+AC. After 60 days fermentation in plastic drum silos, the silos were opened for sampling. PacBio single-molecule real-time sequencing technology was used to study bacterial community structure, symbiotic network functionality, and pathogenic risk to clarify CS fermentation regulatory mechanisms. The CS contained 59.9% neutral detergent fiber and 7.1% crude protein. Additive-treated silages showed better quality than the control: higher lactic acid (1.64–1.83% dry matter, DM), lower pH (3.62–3.82), and reduced ammonia nitrogen (0.54–0.81% DM). Before ensiling, the CS was dominated by Gram-negative Rhizobium larrymoorei (16.30% of the total bacterial community). Functional prediction indicated that the microbial metabolism activity in diverse environments was strong, and the proportion of potential pathogens was relatively high (14.69%). After ensiling, Lactiplantibacillus plantarum as Gram-positive bacteria were the dominant species in all the silages (58.39–84.34% of the total bacterial community). Microbial additives facilitated the establishment of a symbiotic microbial network, where Lactiplantibacillus occupied a dominant position (p < 0.01). In addition, functional predictions showed an increase in the activity of the starch and sucrose metabolism and a decrease in the proportion of potential pathogens (0.61–1.95%). Among them, the synergistic effect of LAB and AC inoculants optimized the silage effect of CS. This study confirmed that CS is a potential high-quality roughage resource, and the application of silage technology can provide a scientific basis for the efficient utilization of feed resources and the stable development of animal husbandry in the tropics. Full article

(This article belongs to the Special Issue Silage Preparation, Processing and Efficient Utilization—2nd Edition)

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16 pages, 7723 KiB

Open AccessArticle

Mycorrhizal Regulation of Core ZmSWEET Genes Governs Sugar Accumulation in Maize

by Guang-Xia He, Feng-Ling Zheng, Ying-Ning Zou, Xiu-Bing Gao, Qiang-Sheng Wu and Can Guo

Agriculture 2025, 15(16), 1790; https://doi.org/10.3390/agriculture15161790 - 21 Aug 2025

Abstract

Mycorrhizal symbiosis relies on the host’s supply of carbohydrates, while sugar transport within plants is governed by the SWEET sugar transporter family. Although the symbiotic association between arbuscular mycorrhizal fungi (AMF) and maize is critical for its growth and sugar regulation, different AMF [...] Read more.

Mycorrhizal symbiosis relies on the host’s supply of carbohydrates, while sugar transport within plants is governed by the SWEET sugar transporter family. Although the symbiotic association between arbuscular mycorrhizal fungi (AMF) and maize is critical for its growth and sugar regulation, different AMF species have varying impacts on the host. The aim of this study was to analyze the effects of inoculating six different AMF species [Diversispora epigaea (De), Rhizophagus intraradices (Ri), Paraglomus occultum (Po), Entrophospora etunicata (Ee), Glomus heterosporum (Gh), and Funneliformis mosseae (Fm)] on plant growth, leaf photosynthetic capacity, glomalin-related soil protein content, leaf sugar content, and SWEET gene expression of maize under potted conditions for two months. AMF species colonize maize roots and showed significant species-specific variation, where Ri and Fm colonized treatment had the greatest rates (66~68%). All six fungi significantly increased biomass and stem diameter, with Ee treatment yielding the thickest stems, and enhanced leaf photosynthetic performance and glomalin-related soil protein fractions to some extent, with species-specific enhancements. All AMF species in particular significantly increased leaf sucrose; all except Ri treatment significantly increased fructose; while only Po and Fm treatments significantly increased glucose. AMF inoculations consistently upregulated the expression of ZmSWEET1b/3a/3b/4a/4b/14a and 16 genes, consistently downregulated the expression of ZmSWEET6b/11b/12a/13a/13b/13c and 17b genes, and induced treatment-specific regulation in the other gene expression. Root AMF colonization clustered with sugars and specific ZmSWEETs, with ZmSWEET4a/15b and 14b central to sucrose/glucose based on principal component analysis, indicating that these genes have specific regulatory effects in response to AMF treatments. In short, AMF inoculation reprogrammed ZmSWEET expression in a species-specific manner, with core ZmSWEET genes mediating sugar accumulation to support symbiosis. Full article

(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)

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20 pages, 2008 KiB

Open AccessArticle

Using APSIM Model to Optimize Nitrogen Application for Alfalfa Yield Under Different Precipitation Regimes

by Yanbiao Wang, Haiyan Li, Yuanbo Jiang, Yaya Duan, Yi Ling, Minhua Yin, Yanlin Ma, Yanxia Kang, Yayu Wang, Guangping Qi, Guoyun Shen, Boda Li, Jinxi Chen and Huile Lv

Agriculture 2025, 15(16), 1789; https://doi.org/10.3390/agriculture15161789 - 21 Aug 2025

Abstract

Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there [...] Read more.

Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there remains some disagreement regarding its nitrogen management strategies. This study conducted a three-year field experiment and calibrated the APSIM-Lucerne model. Based on the calibrated model, three typical precipitation year types (dry, normal, and wet years) were selected. Combining field experiments, eight nitrogen application scenarios (0, 80, 120, 140, 160, 180, 200, and 240 kg·ha⁻¹) were set up. With the objectives of increasing alfalfa yield, nitrogen partial productivity, and nitrogen agronomic efficiency, this study investigates the appropriate nitrogen application thresholds for alfalfa under different precipitation year types. The results showed the following: (1) Alfalfa yield increased first and then decreased with the increase in nitrogen application level. The annual yield of the N160 treatment was the highest (13.39 t·ha⁻¹), which was 5.15% to 32.39% higher than that of the other treatments. (2) The APSIM-Lucerne model could well reflect the growth process and yield of alfalfa under different precipitation year types. The R² and NRMSE between the simulated and observed values of the former were 0.85–0.91 and 5.33–7.44%, respectively. The R² and NRMSE between the simulated and measured values of the latter were 0.74–0.96 and 2.73–5.25%, respectively. (3) Under typical dry, normal, and wet years, the optimal nitrogen application rates for alfalfa yield increases were 120 kg·ha⁻¹, 140 kg·ha⁻¹, and 160 kg·ha⁻¹, respectively. This study can provide a basis for precise nitrogen management of alfalfa under different precipitation year types. Full article

(This article belongs to the Special Issue Advancements in Best Management Practices for Enhancing Soil Health and Water Quality)

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19 pages, 7264 KiB

Open AccessArticle

Design and Performance Testing of a Multi-Variety Forage Grass Mixed-Sowing Seed Metering Device

by Wenxue Dong, Anbin Zhang, Qihao Wan, Fei Liu, Yingsi Wu, Yin Qi and Yuxing Ren

Agriculture 2025, 15(16), 1788; https://doi.org/10.3390/agriculture15161788 - 21 Aug 2025

Abstract

Traditional fluted roller seed metering devices exhibit unstable seeding rates during forage seed mixed sowing. To address this issue, a new seed metering device was designed based on the agronomic requirements of forage seed mixing and the structural characteristics of fluted roller mechanisms. [...] Read more.

Traditional fluted roller seed metering devices exhibit unstable seeding rates during forage seed mixed sowing. To address this issue, a new seed metering device was designed based on the agronomic requirements of forage seed mixing and the structural characteristics of fluted roller mechanisms. The discrete element method (DEM) was employed to numerically simulate the movement of particles within the seed metering device. Single-factor experiments identified optimal parameter ranges for the seed metering device: a metering shaft speed of 10–20 r/min, a seed inlet width of 8–24 mm, and a seed outlet height of 10–20 mm. A response surface methodology (RSM) experiment was then designed using Design-Expert 13 software. The results yielded optimal operating parameters: a metering shaft speed of 18.9 r/min, a seed inlet width of 9.3 mm, and a seed outlet height of 14.4 mm. The field experiment validated the seeding performance with the optimal parameter combination. The coefficient of variation (CV) for the first-class seed (CV₁) was 4.16%, and for the second-class seed (CV₂) it was 2.98%, both of which met the requirements for mixed sowing of forage. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 2000 KiB

Open AccessReview

Active Chlorophyll Fluorescence Technologies in Precision Weed Management: Overview and Perspectives

by Jin Hu, Yuwen Xie, Xingyu Ban, Liyuan Zhang, Zhenjiang Zhou, Zhao Zhang, Aichen Wang and Toby Waine

Agriculture 2025, 15(16), 1787; https://doi.org/10.3390/agriculture15161787 - 21 Aug 2025

Abstract

Weeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool [...] Read more.

Weeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool for assessing plant health and stress responses. Active chlorophyll fluorescence technology uses an external light source to excite plant leaves, enabling the rapid acquisition of fluorescence signals for real-time monitoring of vegetation in the field. This technology shows great potential for weed detection, as it allows for accurate discrimination between crops and weeds. Furthermore, since weed-induced stress affects the photosynthetic process of plants, resulting in changes in fluorescence characteristics, chlorophyll fluorescence can also be used to detect herbicide resistance in weeds. This paper reviews the progress in using active chlorophyll fluorescence sensor technology for weed detection. It specifically outlines the principles and structure of active fluorescence sensors and their applications at different stages of field operations, including rapid classification of soil and weeds during the seedling stage, identification of in-row weeds during cultivation, and assessment of herbicide efficacy after application. By monitoring changes in fluorescence parameters, herbicide-resistant weeds can be detected early, providing a scientific basis for precision herbicide application. Full article

(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)

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20 pages, 16392 KiB

Open AccessArticle

PCC-YOLO: A Fruit Tree Trunk Recognition Algorithm Based on YOLOv8

by Yajie Zhang, Weiliang Jin, Baoxing Gu, Guangzhao Tian, Qiuxia Li, Baohua Zhang and Guanghao Ji

Agriculture 2025, 15(16), 1786; https://doi.org/10.3390/agriculture15161786 - 21 Aug 2025

Abstract

With the development of smart agriculture, the precise identification of fruit tree trunks by orchard management robots has become a key technology for achieving autonomous navigation. To solve the issue of tree trunks being hard to see against their background in orchards, this [...] Read more.

With the development of smart agriculture, the precise identification of fruit tree trunks by orchard management robots has become a key technology for achieving autonomous navigation. To solve the issue of tree trunks being hard to see against their background in orchards, this study introduces PCC-YOLO (PENet, CoT-Net, and Coord-SE attention-based YOLOv8), a new trunk detection model based on YOLOv8. It improves the ability to identify features in low-contrast situations by using a pyramid enhancement network (PENet), a context transformer (CoT-Net) module, and a combined coordinate and channel attention mechanism. By introducing a pyramid enhancement network (PENet) into YOLOv8, the model’s feature extraction ability under low-contrast conditions is enhanced. A context transformer module (CoT-Net) is then used to strengthen global perception capabilities, and a combination of coordinate attention (Coord-Att) and SENetV2 is employed to optimize target localization accuracy. Experimental results show that PCC-YOLO achieves a mean average precision (mAP) of 82.6% on a self-built orchard dataset (5000 images) and a detection speed of 143.36 FPS, marking a 4.8% improvement over the performance of the baseline YOLOv8 model, while maintaining a low computational load (7.8 GFLOPs). The model demonstrates a superior balance of accuracy, speed, and computational cost compared to results for the baseline YOLOv8 and other common YOLO variants, offering an efficient solution for the real-time autonomous navigation of orchard management robots. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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25 pages, 1559 KiB

Open AccessArticle

Influence of Information Sources on Technology Adoption in Apple Production in China

by Linjia Yao, Gang Zhao, Changqing Yan, Amit Kumar Srivastava, Qi Tian, Ning Jin, Junjie Qu, Ling Yin, Ning Yao, Heidi Webber, Eike Luedeling and Qiang Yu

Agriculture 2025, 15(16), 1785; https://doi.org/10.3390/agriculture15161785 - 21 Aug 2025

Abstract

China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information [...] Read more.

China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information sources shape adoption behavior. Based on 382 farmer surveys across major apple-producing provinces, the study examines (1) farmers’ preferences for agricultural information sources, (2) the influence of demographic characteristics on those preferences, and (3) the differential effects of specific sources on the adoption of key technologies, including dwarf rootstocks and virus-free seedlings. Results show that agri-chemical dealers (ACDs) and farmer peers (FPs) are the most commonly used information channels. Access to advice from local experts (EXPs) significantly increases the likelihood of adopting dwarf rootstocks, while information from ACDs promotes the use of virus-free seedlings. In contrast, reliance on personal farming experience is negatively associated with technology uptake. These findings highlight the need to strengthen formal information dissemination systems and better integrate trusted local actors like ACDs and EXPs into agricultural extension. Targeted information delivery can improve adoption efficiency, promote evidence-based decision-making, and support the modernization and sustainability of China’s apple sector. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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