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Agriculture
Volume 16
Issue 10
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Agriculture, Volume 16, Issue 10 (May-2 2026) – 114 articles

Cover Story (view full-size image): Italy’s Mediterranean climate is shifting, and with it, the country’s agricultural landscape. In recent years, southern regions, in particular Sicily, Calabria and Sardinia, have witnessed a rapid expansion in avocado cultivation. Driven by high market demand and profitability, growers enthusiastically invested in this tropical fruit, turning it into the new "green gold" of Italian agriculture. However, as avocado orchards expanded, several and severe diseases began to affect tree health and productivity. In this study, 22 species of pathogenic fungi and oomycetes, including 14 new host–pathogen records in Italy, were isolated, often simultaneously from the same plants, which exhibited a complex of symptoms. The findings sound an alarm bell for the entire European exotic fruit sector. View this paper
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29 pages, 113680 KB  
Article
Tomato-Adaptive Attention YOLOv8 for Accurate and Interpretable Maturity Detection Across Diverse Environments
by Umme Fawzia Rahim, Md. Mushibur Rahman and Hiroshi Mineno
Agriculture 2026, 16(10), 1130; https://doi.org/10.3390/agriculture16101130 - 21 May 2026
Viewed by 743
Abstract
Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and [...] Read more.
Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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26 pages, 4167 KB  
Article
An Intelligent Fertilization Decision Model for Cereal Crops Integrating Explainable Ensemble Learning and Hybrid Optimization: A Case Study in Wensu County, Xinjiang, China
by Jiahao Ye, Chao Xu, Biao Cao, Tianyuan Feng, Tengyan Feng, Jun Sun and Lei Zhang
Agriculture 2026, 16(10), 1129; https://doi.org/10.3390/agriculture16101129 - 21 May 2026
Viewed by 557
Abstract
Optimizing fertilizer management is crucial for increasing crop yields while reducing environmental impact. However, traditional methods rely on extensive field trials, which are costly and limit their scalability. To overcome these limitations, this study developed data-driven yield prediction models (YPM) for wheat, rice, [...] Read more.
Optimizing fertilizer management is crucial for increasing crop yields while reducing environmental impact. However, traditional methods rely on extensive field trials, which are costly and limit their scalability. To overcome these limitations, this study developed data-driven yield prediction models (YPM) for wheat, rice, and maize by integrating multiple feature selection and machine learning algorithms with explainable ensemble learning, namely stacking regression (SR) and voting mean (VM). The optimal YPM was subsequently combined with the hybrid optimization strategy to construct an intelligent fertilization decision model (IFDM), and the economic–environmental benefits were subsequently evaluated. The best-performing models were SHAP-SR for wheat and rice and GBM-SR for maize, achieving R2 values of 0.79, 0.69, and 0.67, and RMSEs of 681.69, 725.35, and 1091.49 kg ha−1, respectively. Based on the IFDM, the recommended application ranges for nitrogen (N), phosphorus (P2O5), and potassium (K2O) were as follows: for wheat, 122.1–256.3, 45.4–98.2, and 30.6–60.7 kg ha−1; for rice, 170.8–261.2, 55.1–91.4, and 40.6–98.5 kg ha−1; and for maize, 157.5–293.4, 84.2–156.4, and 30.1–62.7 kg ha−1. Simulation-based evaluation suggested that adopting these recommendations could potentially increase average yields by 9.2–12.4% and enhance economic–environmental benefits by 32.86–97.73% across the three crops. This study indicates that coupling interpretable ensemble learning with a hybrid optimization strategy can support efficient decision-making for field-scale fertilization and provides a data-driven and cost-effective approach for precision fertilization, with potential applicability to arid agricultural regions under similar agro-ecological conditions. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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23 pages, 1503 KB  
Article
Digital Inclusive Finance, Rural Industrial Integration, and Agricultural Economic Resilience in China: A Threshold Mediation Analysis
by Zhiheng Sun, Adul Supanut, Jianxu Liu and Polpat Kotrajaras
Agriculture 2026, 16(10), 1128; https://doi.org/10.3390/agriculture16101128 - 21 May 2026
Cited by 3 | Viewed by 730
Abstract
Digital inclusive finance has grown rapidly in China in recent years, yet its effect on agricultural economic resilience remains debated. This study investigates the effect of digital inclusive finance on agricultural economic resilience, focusing on the mediating role of rural industry integration. Using [...] Read more.
Digital inclusive finance has grown rapidly in China in recent years, yet its effect on agricultural economic resilience remains debated. This study investigates the effect of digital inclusive finance on agricultural economic resilience, focusing on the mediating role of rural industry integration. Using annual panel data covering 29 Chinese provinces from 2011 to 2021, we employ two-way fixed-effect panel regressions, mediation analysis, threshold analysis, instrumental variable estimation, and spatial econometric models. The results show that digital inclusive finance has a significant negative effect on agricultural economic resilience, and this finding is robust across alternative specifications and instrumental variable estimations. Rural industry integration serves as an important transmission channel, with the indirect effect accounting for approximately one-third of the total effect. The two stages of this mediation pathway are moderated by distinct threshold variables: rural digital infrastructure positively moderates the effect of digital inclusive finance on rural industry integration, while government fiscal support negatively moderates the effect of rural industry integration on agricultural economic resilience. The spatial analysis further reveals that digital inclusive finance generates negative spatial spillovers onto neighboring provinces. Based on these findings, we suggest that the government continue to invest in rural digital infrastructure, guide digital finance toward rural industry integration in underdeveloped regions, and maintain fiscal support at an appropriate level to preserve the vitality of integrated industries. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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17 pages, 2132 KB  
Article
Research on a Portable Multispectral Imaging System for Starch Content Detection in Watermelon–Pumpkin Grafted Seedling Leaves
by Shengyong Xu, Honglei Yang, Yu Zeng, Shaodong Wang, Shuo Yang, Zhilong Bie and Yuan Huang
Agriculture 2026, 16(10), 1127; https://doi.org/10.3390/agriculture16101127 - 21 May 2026
Viewed by 420
Abstract
Plant leaf starch content is a critical indicator of metabolic status, yet traditional enzymatic methods are destructive, labor-intensive, and costly. This study proposes a novel non-destructive detection method using watermelon–pumpkin grafted seedlings. To optimize hardware design, 12 characteristic wavelengths were identified via competitive [...] Read more.
Plant leaf starch content is a critical indicator of metabolic status, yet traditional enzymatic methods are destructive, labor-intensive, and costly. This study proposes a novel non-destructive detection method using watermelon–pumpkin grafted seedlings. To optimize hardware design, 12 characteristic wavelengths were identified via competitive adaptive reweighted sampling (CARS). A portable multispectral imaging system was developed, featuring narrowband LEDs and integrated human–computer interaction software for real-time visualization. We constructed a multimodal deep learning architecture that integrates a convolutional neural network (CNN) for spatial feature extraction from RGB images, a fully connected neural network (FCNN) for spectral data, and a Transformer network for high-level feature fusion. Experimental results showed that the ShuffleNet v2-Transformer model achieved an R2 of 0.956 (RMSE = 0.036) for watermelon leaves, while the EfficientNet b1-Transformer model reached an R2 of 0.967 (RMSE = 0.052) for pumpkin leaves. This multimodal approach significantly outperformed conventional PLSR and single-modal CNN models, demonstrating superior ability in processing long-range dependencies within spectral–spatial data. The system enables accurate detection with a throughput of 120 samples per hour at a hardware cost approximately 90% lower than commercial multispectral cameras. This provides an efficient, low-cost solution for large-scale monitoring of plant physiological indicators in precision breeding. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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22 pages, 2973 KB  
Article
A Feature-Enhanced and Edge-Refined Network for Cropland Parcel Extraction from Sentinel-2 Imagery
by Beibei Gao, Liejun Wang and Jinkai Qiu
Agriculture 2026, 16(10), 1126; https://doi.org/10.3390/agriculture16101126 - 21 May 2026
Viewed by 499
Abstract
Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of [...] Read more.
Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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23 pages, 25652 KB  
Article
Spatiotemporal Distribution of Highland Barley Yield Potential and Its Response to Climate Change in the Yarlung Zangbo River and Its Two Tributaries, Tibet
by Tingting Lang, Yuanqing Wang, Ying Liu, Xinzhe Song and Yanzhao Yang
Agriculture 2026, 16(10), 1125; https://doi.org/10.3390/agriculture16101125 - 21 May 2026
Viewed by 409
Abstract
The yield of highland barley is not only related to the food security of Tibet but also to the social stability and development in the frontier region. This study revealed the spatiotemporal distribution of highland barley yield potential using the DSSAT model and [...] Read more.
The yield of highland barley is not only related to the food security of Tibet but also to the social stability and development in the frontier region. This study revealed the spatiotemporal distribution of highland barley yield potential using the DSSAT model and GIS technology in the Yarlung Zangbo River and its two tributaries (YZTT) of Tibet from 1981 to 2020, and analyzed its response relationship to climate factors. The results show that the highland barley yield potential ranged from 4284.75 to 7341.15 kg/ha in the YZTT region during 1981 to 2020, with an average of 6719.87 kg/ha. Under the climate change, the highland barley yield potential was on a downward trend of −14.49 kg/ha·a over the past 40 years. In terms of the response of highland barley yield potential to climate change, the highland barley yield potential decreased by 2.90 kg/ha for every 1 MJ/m2 decrease in solar radiation. For every 1 °C increase in the maximum temperature, the highland barley yield potential increased by 219.68 kg/ha. Meanwhile, for every 1 °C increase in the minimum temperature, the highland barley yield potential increased by 91.40 kg/ha. These findings aim to provide reference for decision-making in agricultural policy and spatial allocation of agricultural resources. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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21 pages, 18893 KB  
Article
Soil Water Content Distribution and Maize Yield Stability Under Conventional and Conservation Tillage Systems on a Silty Gleysol
by Monika Marković, Irena Jug, Danijel Jug, Boris Đurđević, Bojana Brozović, Vedran Lederer and Željko Barač
Agriculture 2026, 16(10), 1124; https://doi.org/10.3390/agriculture16101124 - 21 May 2026
Viewed by 389
Abstract
Structural and functional soil degradation under conventional tillage has reached a critical point, requiring a shift towards conservation practices to mitigate the negative effects of climate change. This study evaluated the multi-year effects (2021–2024) of conventional tillage (CT), conservation deep tillage (CD), and [...] Read more.
Structural and functional soil degradation under conventional tillage has reached a critical point, requiring a shift towards conservation practices to mitigate the negative effects of climate change. This study evaluated the multi-year effects (2021–2024) of conventional tillage (CT), conservation deep tillage (CD), and conservation shallow tillage (CS) on soil physical properties (density, air capacity, and water content), water distribution, infiltration rate, and maize yield in a silty Gleysol. Soil water content (SWC), i.e., distribution, was monitored using PR2 profile probes at depths of 10, 20, 30, and 40 cm. CT treatment resulted in impaired soil physical properties, characterized by a significant increase in air capacity (+233.9%) and with a significant decrease in volumetric water content (qw, ≈40%). In contrast to CT (47.91 cm h−1), the CS treatment resulted in more favorable hydraulic properties, i.e., and infiltration rate of 102.29 cm h−1, by 2024. Statistical analysis (R2, RMSE) confirmed that CS provides the most reliable and consistent environment for monitoring SWC. While maize yields were significantly higher in CT during the initial year (2021; 9.5 t ha−1 vs. 8.4 t ha−1 in CS), no significant differences were observed by 2024, and all tillage systems reached yields of ≈13.0 t ha−1. The results suggest that after the four-year study period, CS tillage stabilized soil hydraulic properties and pore continuity, thereby resulting in maize yields equivalent to those of CT. Therefore, CS has proven to be a more resilient and effective strategy for sustainable water management in silty Gleysols. Full article
(This article belongs to the Special Issue Climate Change in Agriculture: An Interdisciplinary Approach to Adaptation and Mitigation)
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26 pages, 4664 KB  
Article
Attitude Stabilization Control Methods for a Tracked Agricultural Transport Platform in Hilly and Mountainous Terrain Based on Adaptive Kalman Filtering
by Yongjun Sun, Yaqin Tong, Jiachen Ding, Yejun Zhu, Weihua Wei, Maohua Xiao and Guosheng Geng
Agriculture 2026, 16(10), 1123; https://doi.org/10.3390/agriculture16101123 - 21 May 2026
Cited by 2 | Viewed by 351
Abstract
This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic [...] Read more.
This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article
(This article belongs to the Section Agricultural Technology)
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16 pages, 2071 KB  
Article
Siraitia grosvenorii Vine Biochar for Enhancing Organic Carbon Content and Carbon Dioxide Release from Soils: Insights into Process and Mechanism
by Lening Hu, Songqi Zhu, Xuehui Liu, Hua Deng, Anyu Li, Linxuan Li, Limei Pan and Yuan Huang
Agriculture 2026, 16(10), 1122; https://doi.org/10.3390/agriculture16101122 - 21 May 2026
Viewed by 418
Abstract
The soil of Siraitia grosvenorii (LHG) farmland often suffers from acidification, compaction, and declining organic matter content. As biochar helps improve soil quality and enhance soil carbon sequestration capacity, an increasing number of studies are utilizing biochar for soil quality improvement. To address [...] Read more.
The soil of Siraitia grosvenorii (LHG) farmland often suffers from acidification, compaction, and declining organic matter content. As biochar helps improve soil quality and enhance soil carbon sequestration capacity, an increasing number of studies are utilizing biochar for soil quality improvement. To address the soil degradation problem in LHG farmland and achieve the goals of soil organic carbon (SOC) sequestration and nutrient increase, we conducted a 100-day indoor constant-temperature incubation experiment by adding different proportions of LHG vine biochar. We analyzed the changes in SOC mineralization, different carbon fractions, and soil nutrient content in LHG farmland. The main results showed that, compared with the control group, the cumulative mineralization (CumulMine) of SOC increased by 3% to 51%, and organic carbon content increased by 52.43% to 193.87%. As the LHG vine biochar application rate increased, the metabolic entropy (qCO2) rose, whereas the microbial entropy (qMBC) showed an opposite trend. Similarly, compared with the control group, the addition of 1.0%, 2.0%, and 4.0% LC increased water-soluble organic carbon by 45.87 mg·kg−1, 67.00 mg·kg−1, and 81.73 mg·kg−1, respectively, and soil nutrients also increased, but microbial biomass carbon (MBC) and readily oxidizable organic carbon (ROC) contents decreased. The main conclusions indicate that adding LHG vine biochar increases SOC content, which is associated with reduced microbial activity. Biochar-derived DOC may serve as a substrate for microbial respiration, thereby contributing to increased CO2 release and accelerated nutrient release. The application of LHG vine biochar enhanced the carbon sequestration capacity of LHG farmland soil while improving soil nutrient content, with the 4% application rate treatment performing the best. Full article
(This article belongs to the Section Agricultural Soils)
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26 pages, 646 KB  
Article
The Debate on Mega-Dam Impacts: A Stakeholder-Based Exploration of Merowe Dam, Sudan
by Al-Noor Abdullah, Sanzidur Rahman and Rita Goyal
Agriculture 2026, 16(10), 1121; https://doi.org/10.3390/agriculture16101121 - 21 May 2026
Viewed by 526
Abstract
Climate change, depleting fossil fuel reserves, and instability in petroleum prices are driving developing economies to explore cost-effective, efficient, and sustainable energy sources such as hydropower. However, there is an ongoing debate regarding the relevance, suitability, and impact of mega-dams. Much of the [...] Read more.
Climate change, depleting fossil fuel reserves, and instability in petroleum prices are driving developing economies to explore cost-effective, efficient, and sustainable energy sources such as hydropower. However, there is an ongoing debate regarding the relevance, suitability, and impact of mega-dams. Much of the existing research on mega-dams examines this debate through the lens of development theories. However, mega-dams impact a wide range of stakeholders at local, national, regional, and global levels, necessitating exploration of their role from a socioeconomic perspective. This interdisciplinary case study draws knowledge from management, sociology, and economics and provides a comprehensive account of multi-stakeholder perspectives on the impact of a mega-dam and addresses the research question: How do stakeholders perceive the impact of the Merowe Dam on agricultural livelihoods, and how do they interpret the role of governance processes? Participants included farmers, a focus group with 10 members from the affected communities, and 32 key informant interviews from non-governmental organizations, political actors, academics, businessmen and leaders in the catchment areas of the Merowe Dam, Sudan. The findings suggest that despite some concerns about motivations and processes of mega-dam commissioning, these projects are perceived as beneficial for long-term and sustainable socioeconomic growth and gaining support for renewable energy use in developing economies. The participants reported that modernization of agriculture, following the establishment of the dam, increased crop yields, e.g., wheat production has increased per hectare. Farmers’ income and irrigated land have increased substantially per family due to an increase in land sizes allocated to relocated communities, leading to an overall increase in land size. Therefore, with improved processes in both pre- and post-commissioning stages, transparency, accountability, and deeper stakeholder engagement, mega-dams can facilitate a smoother transition from fossil fuels to large-scale hydropower on one hand and livelihood enhancement through agriculture and other income generating activities on the other. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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26 pages, 3635 KB  
Article
Bayesian Additive Regression Trees for Multi-Depth Soil Moisture Modeling
by Dimitrios Koulouris and Nikolaos Malamos
Agriculture 2026, 16(10), 1120; https://doi.org/10.3390/agriculture16101120 - 21 May 2026
Viewed by 494
Abstract
Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm [...] Read more.
Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm depths in the Arta plain, northwestern Greece, using combinations of in situ soil moisture observations from other depths together with Sentinel-2-derived NDVI and NDMI. BART was trained with 2020–2021 data and evaluated using 2022 observations. Model performance was generally high, with Nash–Sutcliffe efficiency often exceeding 0.90 and RMSE remaining below nominal sensor uncertainty. The best results were obtained when soil moisture from two additional depths was used as predictor information, confirming the strong vertical dependence of profile moisture dynamics. NDVI and NDMI did not systematically improve point prediction accuracy but provided complementary information by improving the estimation of predictive uncertainty and generating more reliable credible intervals within the probabilistic formulation. Residuals were normally distributed and showed no evident systematic bias. Preliminary external validation at an independent site showed moderate skill, with most cases still producing errors below nominal sensor accuracy. Finally, a comparison between BART and Multiple Linear Regression (MLR) showed that BART outperformed MLR, particularly in cases where both machine learning models performed weakly. Overall, BART proved to be a robust framework for multi-depth soil moisture estimation. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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17 pages, 7108 KB  
Article
Assessment of Portable X-Ray Fluorescence for Six Elements in Albic Luvisol Soils: Comparison with Aqua-Regia-Extractable ICP-MS
by Magdalena Szymańska, Bożena Smreczak, Pavel Čermák and Tomasz Sosulski
Agriculture 2026, 16(10), 1119; https://doi.org/10.3390/agriculture16101119 - 21 May 2026
Viewed by 558
Abstract
Portable X-ray fluorescence (pXRF) is increasingly used as a rapid and cost-effective technique for soil analysis; however, its comparability with laboratory-based methods remains uncertain. This study aimed to evaluate the applicability of pXRF for determining the concentrations of six elements (K, Ca, Fe, [...] Read more.
Portable X-ray fluorescence (pXRF) is increasingly used as a rapid and cost-effective technique for soil analysis; however, its comparability with laboratory-based methods remains uncertain. This study aimed to evaluate the applicability of pXRF for determining the concentrations of six elements (K, Ca, Fe, Pb, Mn, and Zn) in agricultural soils classified as Albic Luvisols with a loamy sand texture. A total of 96 dried, ground soil samples from a long-term fertilization experiment were analyzed using pXRF and compared with inductively coupled plasma mass spectrometry (ICP-MS) following aqua regia digestion. Association and agreement between methods were assessed using correlation analysis, Deming regression, Lin’s concordance correlation coefficient (CCC), and Bland–Altman analysis. Substantial differences were observed between the two methods. The mean pXRF/ICP-MS ratios were approximately 25 for K, 4.0 for Ca, 1.43 for Fe, 1.41 for Mn, 1.21 for Pb, and 1.06 for Zn. The observed discrepancies are attributed to methodological factors. In particular, ICP-MS after aqua regia digestion represents pseudo-total concentrations, whereas pXRF measures total solid-phase content. Bland–Altman analysis revealed substantial systematic differences between methods. The largest biases were observed for K (−13,110 mg kg−1) and Ca (−2904 mg kg−1), indicating differences spanning several orders of magnitude. Smaller biases were found for Fe (−1179 mg kg−1), Mn (−50.0 mg kg−1), Pb (−2.37 mg kg−1), and Zn (−1.30 mg kg−1). The limits of agreement were particularly wide for K and Ca, whereas Zn exhibited the narrowest range. CCC values confirmed poor agreement for most elements (0.00049–0.36), with Zn showing the highest concordance (0.89). Overall, in the study condition, Zn demonstrated the best agreement between methods. Moreover, the results highlight that correlation-based metrics alone are insufficient for comparing methods and should be complemented by agreement-based approaches. Full article
(This article belongs to the Special Issue Multiple Soil Health Assessment Methods for Changing Agricultural Environment)
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35 pages, 2319 KB  
Article
Visitor Perceptions of Tea Agricultural Heritage Systems in Fujian, China: A Landsenses Ecology Perspective
by Qinjie Huang, Linchao Wang, Yong Chen, Qiqi Zhang, Shumin Li, Yuchen Lin, Jing Ye and Shuisheng Fan
Agriculture 2026, 16(10), 1118; https://doi.org/10.3390/agriculture16101118 - 20 May 2026
Viewed by 557
Abstract
As Agricultural Heritage Systems (AHS) shift from recognition toward dynamic conservation and revitalization, understanding how visitors perceive heritage values is essential for improving interpretation and management. Guided by landsenses ecology, this study provides one of the first comparative assessments of visitor perceptions across [...] Read more.
As Agricultural Heritage Systems (AHS) shift from recognition toward dynamic conservation and revitalization, understanding how visitors perceive heritage values is essential for improving interpretation and management. Guided by landsenses ecology, this study provides one of the first comparative assessments of visitor perceptions across different types of Tea Agricultural Heritage Systems (TAHS), using three representative cases in Fujian, China. A visitor-oriented framework integrating physical, psychological, and cultural perceptions was developed, and 600 questionnaire responses were analyzed through entropy-weighted fuzzy comprehensive evaluation. The results show that visitors generally perceived the three TAHS positively, but perception levels differed significantly across dimensions and heritage types (p < 0.01). Psychological perceptions, especially sense of safety, sense of space, and sense of belonging, were more readily formed, whereas deeper cultural perceptions, such as understanding of heritage cultural content and community cultural connections, remained weaker. These findings reveal a hierarchical pattern in which immediate sensory and psychological experiences precede deeper cultural cognition. Practically, the study suggests that TAHS conservation should move beyond resource protection by translating heritage values into identifiable, contextualized, and participatory visitor experiences through interpretation systems, community-based participation, and experiential presentation. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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20 pages, 460 KB  
Article
Governance of Agricultural Data Spaces in the European Union: Legal and Policy Implications for the Agri-Food Sector in Spain
by María Luisa Lara Ruiz and Rosa Gallardo-Cobos
Agriculture 2026, 16(10), 1117; https://doi.org/10.3390/agriculture16101117 - 20 May 2026
Cited by 1 | Viewed by 877
Abstract
The rapid digitalisation of the agri-food sector has generated unprecedented volumes of farm and value chain data, but also highly fragmented data ecosystems and asymmetric power relations between farmers, technology providers, and public authorities. In response, the European Union has developed a comprehensive [...] Read more.
The rapid digitalisation of the agri-food sector has generated unprecedented volumes of farm and value chain data, but also highly fragmented data ecosystems and asymmetric power relations between farmers, technology providers, and public authorities. In response, the European Union has developed a comprehensive data governance architecture—including the Data Governance Act, the Data Act, the GDPR and the EU Code of Conduct on Agricultural Data Sharing—and is building a Common European Agricultural Data Space (CEADS). This article examines that governance framework and explores its implications for the agri-food sector in Spain. Through a qualitative legal policy review, we map the regulatory landscape, analyse five major European and Spanish initiatives (CEADS/AgriDataSpace, AgData, Agdatahub, RegenAg-X, and DADS), and use Spain as a national case study. A multi-level actor model (meta-governance, data originators, transformation intermediaries, and data users) structures the comparative analysis. On this basis, six design principles for responsible agri-food data spaces are identified: clarity of use cases, inclusive multi-stakeholder governance, data life cycle mapping, privacy and sovereignty by design, a fair economic model, and regulatory compliance as a trust factor. The article identifies open research questions on anonymisation of georeferenced data, data sovereignty, and equitable value distribution, and outlines an agenda for future empirical and legal research. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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23 pages, 2725 KB  
Article
Public Perceptions of Critical Issues in Meat Production: An Importance–Urgency Analysis with Consumer Segmentation
by Kevan W. Lamm, Haoming Fan, Alexa J. Lamm and Masoud Yazdanpanah
Agriculture 2026, 16(10), 1116; https://doi.org/10.3390/agriculture16101116 - 20 May 2026
Viewed by 438
Abstract
Ensuring global food security is one of the greatest challenges facing humanity and meat production is a critical source for protein; however, there are many critical issues facing the industry. This study focused on consumer perceptions of four key issues facing the meat [...] Read more.
Ensuring global food security is one of the greatest challenges facing humanity and meat production is a critical source for protein; however, there are many critical issues facing the industry. This study focused on consumer perceptions of four key issues facing the meat industry: (1) the public perception of the animal industry, (2) environmental sustainability, (3) animal health and well-being, and (4) ensuring human health and well-being (e.g., food safety, nutrition). Analyzing the data from an importance and urgency perspective, the results indicated most respondents tended to perceive ensuring human health and well-being as most important and urgent relative to the other items. However, after calculating the criticality index (a measure of within-person concordance), environmental sustainability had the highest observed mean criticality score, followed by public perception. Lastly, a cluster analysis was undertaken. Four distinct clusters emerged: (1) Health-Focused/Environment-Skeptic, (2) High Engagement, (3) Low Engagement, and (4) Important But Not Urgent. Overall, results indicate a range of consumer perspectives regarding critical issues facing the meat industry; however, human health and well-being was consistently identified as the most important and urgent issue from a consumer perspective which can help inform more targeted communication strategies and effective policy development. Full article
(This article belongs to the Special Issue Innovative Approaches to Improving the Quality of Meat from Farm Animals: Consumer Behavior and Market Determinants)
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22 pages, 7903 KB  
Article
Predicting Yield in Tomato Infected with Tomato Yellow Leaf Curl Virus (TYLCV) Using Regression Models Based on Physiological Traits
by Jeong-Eun Sim, Yun-Ha Lee, Min-Seok Gang, Ju-Yeon Ahn, Han-Kyeol Park, Jae-Kyung Kim, Won-Kyung Lee, Si-Hong Kim and Ho-Min Kang
Agriculture 2026, 16(10), 1115; https://doi.org/10.3390/agriculture16101115 - 20 May 2026
Viewed by 650
Abstract
Tomato yellow leaf curl virus (TYLCV) is one of the most destructive viral diseases causing severe yield losses in tomato production worldwide. This study investigated the effects of TYLCV infection on plant growth, photosynthetic physiological responses, and yield formation in greenhouse-grown tomatoes and [...] Read more.
Tomato yellow leaf curl virus (TYLCV) is one of the most destructive viral diseases causing severe yield losses in tomato production worldwide. This study investigated the effects of TYLCV infection on plant growth, photosynthetic physiological responses, and yield formation in greenhouse-grown tomatoes and evaluated the applicability of physiological trait-based yield prediction models. Two large-fruited tomato cultivars widely cultivated in Korean protected horticulture systems, ‘Daphnis’ and ‘Pink Star’, were inoculated with TYLCV under greenhouse conditions, and their growth, physiological responses, and yield characteristics were compared under high- and low-temperature growing seasons. TYLCV infection significantly reduced leaf length, leaf width, and leaf area index (LAI), and decreased both flowering truss number and fruit-setting truss number, resulting in reduced total yield. Physiological analyses showed that infected plants exhibited decreases in the OJIP fluorescence rise curve and Fv/Fm values, indicating a reduced photochemical efficiency in photosystem II. In addition, A–Ci response curve analysis revealed a reduction in net photosynthetic rate, suggesting limited carbon assimilation capacity. Total yield showed significant positive correlations with maximum net photosynthetic rate (Amax), Fv/Fm, and Ci300. GGE and GT biplot analyses further indicated that yield was closely associated with photosynthetic performance and canopy development traits. A multiple regression model based on physiological traits and virus infection status explained a significant proportion of the variation in tomato yield (R2 = 0.367), indicating that TYLCV infection acts as a key limiting factor for yield reduction. These findings demonstrate that TYLCV infection restricts tomato productivity through reduced photosynthetic efficiency and altered canopy structure. Moreover, physiological trait-based yield prediction approaches may provide a useful framework for evaluating productivity under viral infection conditions and for developing data-driven crop management strategies in greenhouse tomato production systems. Full article
(This article belongs to the Special Issue Research on Plant Production in Greenhouse and Plant Factory Systems—2nd Edition)
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14 pages, 1735 KB  
Review
Microbial Ecology and Amelioration Potential of Albic Soils: From Understanding Communities to Sustainable Management
by Xilun Zhang, Jing Wang, Yalong Liu, Ping Wang, Bin Ma, Qiuju Wang and Jingkuan Wang
Agriculture 2026, 16(10), 1114; https://doi.org/10.3390/agriculture16101114 - 20 May 2026
Viewed by 583
Abstract
Albic soils are a typical problematic soil type distributed worldwide. These soils are characterized by a thin humus layer, low organic matter content, nutrient insufficiency, and weak microbial activity. Therefore, microbial-based approaches hold great potential for the amelioration of Albic soils. This review [...] Read more.
Albic soils are a typical problematic soil type distributed worldwide. These soils are characterized by a thin humus layer, low organic matter content, nutrient insufficiency, and weak microbial activity. Therefore, microbial-based approaches hold great potential for the amelioration of Albic soils. This review synthesizes microbial characteristics, influencing factors, amelioration mechanisms, and related technical efficacy of Albic soils. Microbial communities of Albic soils exhibit distinct regional characteristics, with Acidobacteriota and Proteobacteria dominating the bacterial community. Reasonable agricultural management practices—including deep plowing and subsoil mixing, combined organic fertilization and straw return—can increase microbial biomass by 62–248% and enhance enzyme activities by 12–303%, ultimately increasing crop yield by 1.5–13%. Such practices drive fertility enhancement and ecological functional improvement in Albic soils. Inoculation with functional microbes (e.g., Arbuscular Mycorrhizal Fungi, Trichoderma) alleviates Albic soil acidification by 1.1–3.8%, activates recalcitrant nutrients, and accelerates Soil Organic Matter (SOM) decomposition. Through extracellular polymeric substance secretion, such inoculation promotes aggregate formation, improving soil permeability and structural stability. However, challenges remain for current research, including difficult microbial agent colonization, unstable amelioration effects, and a lack of long-term field studies. Future research should utilize bio-omics technologies, artificial intelligence, and big data technologies to analyze microbial functions and regulate soil quality for cultivated land improvement and sustainable agriculture development. Full article
(This article belongs to the Special Issue The Impact of Carbon and Nitrogen Cycles on Agricultural Soil Ecology)
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38 pages, 8453 KB  
Article
Design and Experimental Investigation of an Anti-Frost Smoke Machine Using a Fuzzy PID Control Strategy Optimized by the Hippopotamus Optimization Algorithm
by Wenbin Zhang, Yue Lu, Yong Lin, Zikang Cao, Haijian Wu, Liju Liu, Yingyin Chen, Ding Hu and Quan Lu
Agriculture 2026, 16(10), 1113; https://doi.org/10.3390/agriculture16101113 - 19 May 2026
Viewed by 544
Abstract
Late spring frost poses a serious threat to orchard production, especially in mountainous orchards where timely frost monitoring and adaptive protection are difficult to implement. To address this problem, this study developed an intelligent smoke-based anti-frost machine integrating a LoRa-based wireless temperature monitoring [...] Read more.
Late spring frost poses a serious threat to orchard production, especially in mountainous orchards where timely frost monitoring and adaptive protection are difficult to implement. To address this problem, this study developed an intelligent smoke-based anti-frost machine integrating a LoRa-based wireless temperature monitoring system, a smoke actuation unit, and a closed-loop control terminal. To overcome the slow response and large overshoot of conventional PID control under nonlinear field conditions, a fuzzy PID control strategy optimized by the Hippopotamus Optimization Algorithm (HOA) was proposed to regulate smoke release in real time. Comparative simulations were conducted using conventional PID, fuzzy PID, and HOA-fuzzy PID controllers, and field experiments were performed in an apple orchard. The results showed that the HOA-fuzzy PID controller achieved the best dynamic performance. Compared with conventional PID, the overshoot, rise time, and settling time were reduced by 60.12%, 33.21%, and 61.94%, respectively; compared with fuzzy PID, they were reduced by 22.85%, 50.45%, and 60.11%, respectively. Disturbance simulation further indicated improved control robustness. Field experiments showed that the prototype increased the orchard canopy temperature by 0.9–3.0 K, and the PM2.5 distribution in the operational area indicated improved smoke coverage. The adaptive regulation strategy also avoided continuous fixed-output operation, suggesting its potential to improve energy-use efficiency. Overall, the proposed system provides a feasible field-operable approach for improving canopy thermal conditions and reducing frost-risk exposure in mountainous orchards, although further biological validation is still required. Full article
(This article belongs to the Section Agricultural Technology)
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23 pages, 1497 KB  
Article
Glyphosate Interactions with Actinobacteria Under Phosphate Starvation: Physiological, Ultrastructural and Molecular Insights from Streptomyces sp. Z38
by Teresa Ana Lía Ocante, Fernando Gabriel Martinez, Federico Zannier, Angeles Prieto-Fernandez, Juliana María Saez and Analía Álvarez
Agriculture 2026, 16(10), 1112; https://doi.org/10.3390/agriculture16101112 - 19 May 2026
Viewed by 481
Abstract
Glyphosate [N-(phosphonomethyl)glycine] is the most widely used herbicide worldwide, and its environmental persistence has prompted increasing interest in microbial processes that may contribute to its dissipation. This study evaluated a collection of 15 soil-derived actinobacterial strains for plant growth-promoting traits, extracellular enzymatic activities, [...] Read more.
Glyphosate [N-(phosphonomethyl)glycine] is the most widely used herbicide worldwide, and its environmental persistence has prompted increasing interest in microbial processes that may contribute to its dissipation. This study evaluated a collection of 15 soil-derived actinobacterial strains for plant growth-promoting traits, extracellular enzymatic activities, glyphosate tolerance, and glyphosate removal under nutrient-sufficient and phosphate-starved conditions. Herbicide tolerance evaluated on agar plates was widespread across the collection, with all strains sustaining growth at 10 and 50 g L−1 of glyphosate. Under nutrient-sufficient conditions glyphosate removal remained limited, with maximum values of 16.15 ± 2.08% (Streptomyces sp. Con7.16) and 15.34 ± 2.89% (Streptomyces sp. Z38). In contrast, prior phosphate starvation markedly enhanced removal efficiency, reaching 42.21 ± 3.59% in Streptomyces sp. Z38 and 39.46 ± 1.94% in Streptomyces sp. Con7.16. Transmission electron microscopy coupled with X-ray microanalysis in the selected Streptomyces sp. Z38 revealed starvation-associated depletion of intracellular polyphosphate granules, followed by partial replenishment when glyphosate was supplied as the sole phosphorus source, consistent with indirect evidence of glyphosate-derived phosphorus acquisition. Genome mining of Streptomyces sp. Z38 identified candidate genes potentially consistent with a non-canonical, C-P lyase-independent phosphonate utilization route; however, these assignments are based exclusively on bioinformatic evidence and require experimental validation. Collectively, these findings indicate that phosphate limitation enhances glyphosate removal in the selected actinobacteria, and the physiological and genomic data are consistent with a starvation-triggered shift toward alternative phosphorus scavenging strategies. Because this strain is intended for future phytoremediation applications in glyphosate-contaminated agricultural soils, elucidating the underlying phosphorus dynamics is essential for anticipating its functional behavior and environmental relevance. Full article
(This article belongs to the Special Issue Contaminant Behavior and Remediation Strategies in Agricultural Soils)
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25 pages, 1146 KB  
Article
LV-3DGS: A High-Quality Reconstruction Method Based on 3D Gaussian Splatting for Precise Phenotypic Measurement of Leafy Vegetables
by Xuejun Yang, Jinbiao Zhong, Kaiyan Lin, Junhui Wu, Jie Chen and Huajun Zhu
Agriculture 2026, 16(10), 1111; https://doi.org/10.3390/agriculture16101111 - 19 May 2026
Cited by 1 | Viewed by 1030
Abstract
High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS [...] Read more.
High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination (R2) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 4618 KB  
Article
Lightweight and High-Precision Visual Detection of Cherry Cracking Defects Based on Improved YOLO11 with Enhanced Feature Fusion
by Yifei Sun, Xinying Miao, Yi Zhang, Zhipeng He, Xinyue Tao, Zhenghan Wang, Tianwen Hou, Ping Ren and Wei Wang
Agriculture 2026, 16(10), 1110; https://doi.org/10.3390/agriculture16101110 - 19 May 2026
Viewed by 572
Abstract
Sweet cherry cracking severely impairs its commercial value and causes huge economic losses, and the accurate real-time detection of fine cracking defects remains a challenging small-target detection task. Traditional manual sorting and conventional machine vision methods suffer from low efficiency and poor robustness, [...] Read more.
Sweet cherry cracking severely impairs its commercial value and causes huge economic losses, and the accurate real-time detection of fine cracking defects remains a challenging small-target detection task. Traditional manual sorting and conventional machine vision methods suffer from low efficiency and poor robustness, while existing YOLO-based models have limitations in multi-scale feature fusion, local feature discrimination and spatial information retention for cherry cracking detection, and their effectiveness in natural production environments has not been statistically validated. To address these issues, this study proposes YOLO-CY for cherry cracking defect detection. Three key modules were optimized: the C3k2_AdditiveBlock was designed to enhance multi-scale feature extraction, the C2PSA_CGLU module improved the discriminability of local crack features via refined channel attention, and the Efficient Up-Convolution Block replaced traditional upsampling to reduce spatial information loss. Experiments were conducted on a self-constructed dataset of 3662 cherry images acquired on a real sorting line under natural ambient light. The results showed that YOLO-CY achieved an mAP50 of 94.88% and an mAP50-95 of 64.92%, with precision and recall reaching 93.90% and 90.81%, respectively, significantly outperforming mainstream lightweight YOLO models and two-stage detectors. Ablation experiments verified the synergistic effect of the three improved modules, and the model only had a marginal increase in parameters (2.62 M) and GFLOPs (6.60), maintaining lightweight characteristics. YOLO-CY can accurately detect fine, low-contrast and pedicel-overlapping cracks and is suitable for real-time detection on automated cherry-sorting lines, providing a technical solution for intelligent cherry quality inspection. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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24 pages, 29973 KB  
Article
CornCare: A Knowledge-Graph-Enhanced Multimodal Diagnostic Reporting System for Corn Diseases
by Yang Liu, Yushan Xie, Xue Wu and Qi Wang
Agriculture 2026, 16(10), 1109; https://doi.org/10.3390/agriculture16101109 - 18 May 2026
Viewed by 659
Abstract
Accurate and actionable crop disease diagnosis requires not only visual recognition of disease symptoms but also the ability to generate grounded reports that integrate symptom interpretation with agronomic knowledge. Existing image-based plant disease diagnosis methods mainly focus on disease classification and often lack [...] Read more.
Accurate and actionable crop disease diagnosis requires not only visual recognition of disease symptoms but also the ability to generate grounded reports that integrate symptom interpretation with agronomic knowledge. Existing image-based plant disease diagnosis methods mainly focus on disease classification and often lack fine-grained symptom description, evidence retrieval, and decision-oriented report generation. To address these limitations, we propose CornCare, a multimodal framework for corn disease diagnosis and diagnostic report generation that combines visual recognition, phenotype captioning, document retrieval, and knowledge-graph-based recommendation support. Given a field corn image, CornCare first localizes disease-relevant leaf regions to reduce background interference. The localized leaf image is then used for disease classification and phenotype caption generation, producing both a disease category and a fine-grained symptom description. These outputs jointly support hierarchical knowledge retrieval, where the disease category narrows the search to relevant expert documents and the phenotype caption retrieves symptom-consistent evidence. The retrieved evidence is further combined with a structured agricultural knowledge graph to generate diagnostic reports with symptom interpretation, likely causes, and management suggestions. Experiments show that CornCare achieves competitive performance in disease identification and phenotype description generation while improving the groundedness, completeness, and practical usefulness of generated diagnostic reports. These results suggest that combining multimodal perception with symptom-grounded knowledge retrieval provides a promising path toward more practical and explainable crop disease diagnosis. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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32 pages, 30028 KB  
Article
A Multi-Class Crop Field Identification Method Based on Semantic–SAM Fusion and UAV RGB Imagery
by Haoran Yang, Xinjun Wang, Qingfu Liang, Shuhan Huang, Panfeng Wang and Jiandong Sheng
Agriculture 2026, 16(10), 1108; https://doi.org/10.3390/agriculture16101108 - 18 May 2026
Cited by 1 | Viewed by 799
Abstract
Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based [...] Read more.
Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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30 pages, 10634 KB  
Article
Numerical Simulation of Nozzles in Fluent-Based Cotton Impurity Removal Machines
by Chao Ma, Ling Zhao, Junjie Ma, Fenglei Wang, Jun Qian and Xinjun Li
Agriculture 2026, 16(10), 1107; https://doi.org/10.3390/agriculture16101107 - 18 May 2026
Viewed by 569
Abstract
This paper conducts numerical simulations of nozzles with different structural parameters based on fluid mechanics, computational fluid dynamics and jet theory. The structural parameters of the nozzles were optimised by analysing flow field characteristics such as the pressure distribution within the nozzle chamber, [...] Read more.
This paper conducts numerical simulations of nozzles with different structural parameters based on fluid mechanics, computational fluid dynamics and jet theory. The structural parameters of the nozzles were optimised by analysing flow field characteristics such as the pressure distribution within the nozzle chamber, velocity distribution, curves of the outlet cross-sectional area and external axial velocity, and velocity uniformity. Combining the results of orthogonal experiments, the optimal combination of factors was determined, and the impurity removal efficiency of the optimised nozzle was tested in the field, providing a reference for subsequent optimisation design. The results indicate that adding a fillet transition to the nozzle can mitigate sudden pressure drops and suppress the generation of vortices; when the fillet transition radius is 80 mm, the flow performance approaches the optimum; the optimal combination of the three factors was determined to be a contraction angle of 13°, λ of 0.65 (corresponding to an outlet height of 27 mm and an inlet diameter of 41 mm), and a nozzle length of 15 mm; this configuration yields the best external flow field characteristics and velocity uniformity; Analysis of the orthogonal test results indicates that the contribution of each structural parameter to velocity uniformity, in descending order, is: contraction angle (77.16%), λ (outlet height/inlet diameter) (18.25%), and nozzle length (0.73%); Field tests confirmed that the removal efficiency of foreign fibres using the optimal parameter combination remained consistently above 95%, with an overall average removal rate of 96.31%. This represents an improvement of approximately 7.5 percentage points compared to the original nozzle (88.83%). The optimised nozzle reduced the number of false rejections of cotton by 57%, demonstrating excellent and highly stable overall removal performance. The influence of the nozzle’s vertical height and its angle relative to the cotton on the removal efficiency requires further investigation. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 688 KB  
Article
The Role of Direct Payments in Shaping the Production Potential and Financial Performance of Dairy Farms: An Assessment for 2014–2023 in the Dominant Milk-Producing EU Countries
by Andrzej Parzonko, Anna Justyna Parzonko, Tomasz Wojewodzic and Marta Czekaj
Agriculture 2026, 16(10), 1106; https://doi.org/10.3390/agriculture16101106 - 18 May 2026
Viewed by 702
Abstract
The primary objective of this study was to present and assess the effects of direct payments and other subsidies targeted at dairy farms under the EU’s Common Agricultural Policy (CAP) guidelines implemented in 2014–2023 on their financial performance and changes in equity. To [...] Read more.
The primary objective of this study was to present and assess the effects of direct payments and other subsidies targeted at dairy farms under the EU’s Common Agricultural Policy (CAP) guidelines implemented in 2014–2023 on their financial performance and changes in equity. To narrow the focus on the research problem, the scope of the analysis was limited to dairy farms from the five EU countries with the highest milk production. To achieve this objective, the study employed economic measures and indicators used to evaluate the resources and outcomes of agricultural activity. The empirical material used in the analysis consisted of farm-level accounting data collected within the European Farm Accountancy Data Network (FADN). The results indicate that direct payments and other subsidies had a very substantial impact on farm income in the analysed countries. The average share of direct payments in dairy farm income in 2014–2023 in the five analysed EU countries ranged from 19.7% in Italy to 88.4% in France. Without direct payments, the average dairy farm would have incurred financial losses from its activity during periods of unfavourable economic conditions on the milk market. The new model for distributing direct payments and other subsidies introduced in 2023, whose main modification compared with the previous system was a stronger alignment of direct payments with environmental objectives, did not result in substantial changes in either the level of payments or their impact on dairy farms’ financial performance. In 2023, the average payment per hectare of agricultural land in the analysed farms amounted to EUR 461.34, which was EUR 19.88 less than in 2022. Full article
(This article belongs to the Special Issue Economics of Milk Production and Processing—2nd Edition)
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18 pages, 7647 KB  
Article
WS-DINO: A DINOv2-Based Weed Segmentation Method with Feature Priors and Spatial Fusion
by Hongsheng Zhou, Jiangping Liu, Rigeng Wu and Baoping Zhao
Agriculture 2026, 16(10), 1105; https://doi.org/10.3390/agriculture16101105 - 18 May 2026
Viewed by 798
Abstract
Weed segmentation is a fundamental task in precision agriculture, essential for targeted intervention and sustainable farming. However, achieving accurate segmentation remains challenging due to the high visual similarity between weeds and crops, as well as the ambiguous, fine-grained boundaries often present in complex [...] Read more.
Weed segmentation is a fundamental task in precision agriculture, essential for targeted intervention and sustainable farming. However, achieving accurate segmentation remains challenging due to the high visual similarity between weeds and crops, as well as the ambiguous, fine-grained boundaries often present in complex field environments. To address this, we present WS-DINO, a novel weed segmentation network built upon the DINOv2 vision foundation model. Our framework introduces two key innovations: (1) a Feature Prior Module that leverages a Canny-guided refinement process to extract and inject fine-grained cues related to weed texture, morphology, and boundaries into specific blocks of the Vision Transformer; and (2) a Spatial Feature Fusion Module that leverages convolutional layers to generate multi-scale spatial features, which are then fused with the semantically rich token features from DINOv2, effectively compensating for the Transformer’s limitations in capturing local spatial details. Comprehensive evaluation on the public PhenoBench dataset shows that WS-DINO achieves an mIoU of 88.67% and outperforms the evaluated benchmark methods. Moreover, on the challenging MotionBlurred dataset, WS-DINO reaches 88.75% mIoU, showing stable performance under motion blur and degraded visual conditions. Full article
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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23 pages, 1293 KB  
Article
Does Crop–Livestock Integration Enhance Economic Resilience in Organic Farming? Evidence from Polish FADN During the 2020–2022 Multi-Crisis Period
by Andrzej Madej and Adam Kleofas Berbeć
Agriculture 2026, 16(10), 1104; https://doi.org/10.3390/agriculture16101104 - 17 May 2026
Viewed by 778
Abstract
Agriculture, as a production sector, is exposed to external shocks. The instability of agricultural markets, changes in prices of inputs, dropping crop prices, or changes in climate patterns put their economic resilience to the test. Agroecological diversification of production is widely cited as [...] Read more.
Agriculture, as a production sector, is exposed to external shocks. The instability of agricultural markets, changes in prices of inputs, dropping crop prices, or changes in climate patterns put their economic resilience to the test. Agroecological diversification of production is widely cited as a key adaptive strategy to increase farms’ resilience to these shocks. At the same time, empirical evidence linking crop diversity to economic stability across different production systems remains limited. The aim of the study was to assess whether the integration of more complex crop rotations and livestock production increases the economic resilience of organic farms compared to stockless organic farms and conventional farms. The analysis utilized data from the Polish FADN covering the multi-crisis period of 2020–2022, which included the COVID-19 pandemic, Russia’s war against Ukraine, and the sharp rise in fertilizer and energy prices. Farms were grouped by production type. Crop diversity was assessed using the Shannon–Wiener index (H′) and the Pielou evenness index (J′). The economic resilience of tested farms was determined based on their income, income variability during the study period, and the ability to maintain income above the parity threshold. The results indicated the existence of different pathways for building resilience. Organic farms with permanent crops and field crops were characterized by the highest crop diversity on arable land, while organic farms with dairy cows had the highest overall economic resilience, despite relatively low crop diversity on arable land. This phenomenon can be explained by the high proportion of permanent grasslands, which promoted feed self-sufficiency and the internal circulation of nutrients. The results indicate that in organic systems, the integration of crop and livestock production, based on permanent grassland, may be a more effective way to strengthen economic resilience than crop diversification on arable land alone. Full article
(This article belongs to the Special Issue Agroecological Pathways Toward Resilient and Sustainable Organic Farming Systems)
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27 pages, 5267 KB  
Article
Analysis of Pulping Performance and Multi-Objective Optimization of Pulping Parameters for Seed Melon Based on Optimal Latin Hypercube Sampling Method
by Qi Luo, Fangxin Wan, Xiaobin Mou, Guojun Ma, Xiaoping Yang, Fengwei Zhang, Zepeng Zang and Xiaopeng Huang
Agriculture 2026, 16(10), 1103; https://doi.org/10.3390/agriculture16101103 - 17 May 2026
Viewed by 525
Abstract
Seed melon pulping is a critical process in the full utilization of seed melon. However, controlling the performance during the pulping process presents several challenges, particularly the unclear relationship between pulping performance and process parameters. This study proposes an optimization of seed melon [...] Read more.
Seed melon pulping is a critical process in the full utilization of seed melon. However, controlling the performance during the pulping process presents several challenges, particularly the unclear relationship between pulping performance and process parameters. This study proposes an optimization of seed melon pulping process parameters based on the optimal Latin hypercube sampling (OLHS) method. The seed melon pulping rate and the large-particle ratio after pulping were selected as performance indicators, with process parameters including the feeding rate of rind–flesh, the rotational speed of first-channel pulping knife roller, and the rotational speed of second-channel pulping knife roller. The OLHS method was combined with the discrete element method (DEM) of pulping to derive the input parameters required for training the radial basis function neural network (RBFNN). Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to find the optimal solution for the pulping performance approximation model, followed by validation through comparison experiments. The multi-objective optimization results showed that the optimal process parameters were rind–flesh feeding rate of 175.69 kg/min−1, first-channel pulping knife roller rotational speed of 797.71 r/min−1, and second-channel pulping knife roller rotational speed of 708.34 r/min−1. Under these parameters, the seed melon pulping rate reached 92.81%, and the large-particle ratio after pulping was 2.19%. Furthermore, the RBFNN-trained approximation model demonstrated a high degree of model fit for the process parameters and performance indicators, as well as strong predictive ability for the macroscopic behavior of the pulping process parameters. Further verification through seed melon pulping experiments showed consistent results with the simulation outcomes, indicating that the optimization results can effectively improve seed melon pulping performance and further confirm the reliability of the method. Full article
(This article belongs to the Section Agricultural Technology)
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13 pages, 1988 KB  
Article
Near-Infrared Transmittance Spectroscopy for Early Screening of Alternaria Contamination and Alternariol Risk in Durum Wheat
by Alessandro Cammerata, Viviana Del Frate, Angela Iori and Francesco Gallucci
Agriculture 2026, 16(10), 1102; https://doi.org/10.3390/agriculture16101102 - 17 May 2026
Cited by 1 | Viewed by 516
Abstract
Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic [...] Read more.
Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic secondary metabolites such as alternariol (AOH), whose early detection remains analytically challenging. The aim of this study was to evaluate the potential of near-infrared transmittance (NIT) spectroscopy as a rapid, non-destructive pre-screening tool for the early identification of Alternaria-contaminated durum wheat lots and associated AOH risk. Samples from three durum wheat cultivars were artificially inoculated with Alternaria spp. and monitored over time. NIT spectra (570–1100 nm) were acquired in transmittance mode and analyzed using partial least squares (PLS) regression, focusing on the 870–1100 nm spectral region. Clear and time-dependent spectral differences were observed between inoculated and control samples, with the strongest discriminative features at 834 and 966 nm. Classification performance was high, with area under the curve (AUC) values between 0.96 and 0.97. ELISA analysis confirmed progressive AOH accumulation in inoculated kernels, consistent with the observed spectral changes, while control experiments excluded autoclaving and visual grain damage as confounding factors. From an applied perspective, the results indicate that NIT spectroscopy can support post-harvest decision-making as a rapid pre-screening approach, enabling the prioritization of suspect wheat lots for confirmatory analytical testing. Multivariate analysis further confirmed the consistency of spectral differences across datasets. Full article
(This article belongs to the Topic Determinants and Methods of Quality Management in Agriculture and Food Processing)
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Article
Calibration of Discrete Element Parameters for Cassava Seed Stems Using the Tavares Model and GA-BP-GA Method
by Lintao Chen, Zeyu Chen, Xiangwei Mou, Ying Lan, Yucan Huang, Xu Ma and Xiangwu Deng
Agriculture 2026, 16(10), 1101; https://doi.org/10.3390/agriculture16101101 - 16 May 2026
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Abstract
Accurate discrete element method (DEM) simulations are essential for elucidating the precision seeding mechanisms and collision damage characteristics of cassava seed stem (CSS); however, such simulations are often limited by a lack of precise contact parameters. In this study, “Guire No. 7” CSS [...] Read more.
Accurate discrete element method (DEM) simulations are essential for elucidating the precision seeding mechanisms and collision damage characteristics of cassava seed stem (CSS); however, such simulations are often limited by a lack of precise contact parameters. In this study, “Guire No. 7” CSS was selected as the research object to calibrate discrete element (DE) parameters by integrating physical experiments with DEM simulations. A stem model was constructed in EDEM software (Altair EDEM 2022) using three-dimensional scanning technology combined with SolidWorks 2024 modeling functions to investigate the influence of the model’s mesh face count on simulation accuracy. Physical experiments measured the average repose angle (RA) of the stems (30.28° ± 1.09°), along with parameters including the restitution coefficient for stem-stem and stem-steel plate collisions, and the coefficient of static friction between the stem and steel plate. The Plackett-Burman Design experiment was employed to screen parameters affecting the RA, and the steepest ascent experiment was conducted to determine their optimal value ranges. Using the RA as the response value, a Central Composite Design experiment combined with machine learning regression models was applied to optimize the influencing parameters and compare model performance. The results indicated that the GA-BP algorithm exhibited superior predictive capability compared to Support Vector Regression (SVR) and the BP neural network. Through optimization using a genetic algorithm (GA), the calibrated parameters were obtained: a stem-steel plate static friction coefficient (SFC) of 0.488, a stem-stem SFC of 0.489, and a stem-stem rolling friction coefficient of 0.131. The resulting simulated RA was 30.73°, yielding a relative error of 1.49% compared to the physically measured value. The GA-BP-GA method demonstrated better optimization performance than the central composite design experiment, thereby validating the accuracy of the calibrated contact parameters between stems. Furthermore, parameters for the Tavares model were calibrated through physical experiments on CSS, using collision damage force and collision damage energy (CDE) as validation indicators. The results showed that the relative errors for both collision damage force and CDE were less than 3%, which is within the acceptable error range, thereby confirming the validity of the calibrated DE parameters for the cassava seed stem. Full article
(This article belongs to the Section Agricultural Technology)
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