Agriculture

20 pages, 5908 KB

Open AccessArticle

An UAV Direct Seeding Device for Rice Based on EDEM

by Zhijun Wu, Runan Xu, Shengcai Shi, Yu Chen, Dandan Han, Lin Chen and Lijia Xu

Agriculture 2026, 16(5), 584; https://doi.org/10.3390/agriculture16050584 - 4 Mar 2026

UAV-based rice direct seeding offers high operational efficiency and reduced labor demand, yet seed distribution uniformity remains a major limitation for centrifugal spreading devices. This study aims to design and optimize a novel centrifugal drone rice direct seeding device to improve seed lateral [...] Read more.

UAV-based rice direct seeding offers high operational efficiency and reduced labor demand, yet seed distribution uniformity remains a major limitation for centrifugal spreading devices. This study aims to design and optimize a novel centrifugal drone rice direct seeding device to improve seed lateral distribution uniformity. In this study, a centrifugal drone rice direct seeding device was developed with a concave perforated disc and double-arc seed-pushing blades to regulate seed motion and improve lateral distribution uniformity. Discrete element method (DEM) simulations were conducted to examine the effects of disc tilt angle, blade type, and blade number. Single-factor and response-surface simulation results identified an optimal parameter combination of a 29.0° disc tilt angle, double-arc blades with a 110° arc angle, and six blades. Based on these results, the disc structure was further refined, and the simulated lateral coefficient of variation (CV) of seed distribution reached 18.22%. Bench tests yielded a minimum CV of 16.34%, an average CV of 19.36%, and a total discharge coefficient of variation of 0.276%, which agrees with the simulation outcomes and supports the validity of the DEM model. Overall, the proposed device demonstrates improved seeding uniformity and meets agronomic requirements for rice cultivation, offering farmers a high-efficiency planting solution and providing UAV manufacturers with a validated double-arc disc design for equipment optimization. Full article

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

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18 pages, 901 KB

Open AccessArticle

Effects of Different Inoculant Types on the Fermentation Characteristics of Silages from Various Forage Crops

by Jonas Jatkauskas, Anouk Lanckriet, Marianna Gentilini and Vilma Vrotniakiene

Agriculture 2026, 16(5), 583; https://doi.org/10.3390/agriculture16050583 - 3 Mar 2026

Abstract

Silage additives formulated with lactic acid bacteria (LAB) are commonly applied to enhance fermentation efficiency and aerobic stability. However, comparative evaluations across different forage species are still scarce. This in vitro experiment assessed the influence of eleven commercial silage inoculants containing various combinations [...] Read more.

Silage additives formulated with lactic acid bacteria (LAB) are commonly applied to enhance fermentation efficiency and aerobic stability. However, comparative evaluations across different forage species are still scarce. This in vitro experiment assessed the influence of eleven commercial silage inoculants containing various combinations of homo- and heterofermentative LAB on fermentation dynamics, nutrient conservation, and aerobic stability of medium-wilted alfalfa (Medicago sativa L.), perennial ryegrass (Lolium perenne L.), and red clover/perennial ryegrass silages. Experimental silages were prepared in 3 L laboratory silos and stored for 90 days. All inoculated treatments exhibited significantly lower pH values at both 3 and 90 days of ensiling compared with the untreated control (p < 0.05). LAB application increased the concentration of total fermentation acids and lactic acid in all forage types, although responses varied depending on inoculant composition. Inoculants containing Lentilactobacilllus buchneri produced the greatest acetic acid concentrations and resulted in a marked enhancement of aerobic stability. Compared with the control, silage inoculation significantly decreased dry matter losses by 35–64% and ammonia-N proportion by 20–37%, leading to an additional dry matter recovery of 1.29–2.87%. Control silages showed the lowest aerobic stability (97.2 h), while inoculated silages ranged from 126.0 to 200.4 h, with the extent of improvement differing among forage species and LAB formulations. In conclusion, commercial silage inoculants incorporating diverse LAB strains effectively improve fermentation quality, limit nutrient degradation, and enhance aerobic stability of legume and grass silages under controlled experimental conditions. Full article

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

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33 pages, 10075 KB

Open AccessArticle

Comparative Analysis of Image Binarization Algorithms for UAV-Based Soybean Canopy Extraction Across Growth Stages for Image Labelling

by Chi-Yong An, Jinki Park and Chulmin Song

Agriculture 2026, 16(5), 582; https://doi.org/10.3390/agriculture16050582 - 3 Mar 2026

Abstract

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the [...] Read more.

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

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

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21 pages, 4847 KB

Open AccessArticle

Monocular Vision-Based Clamping-Point Determination via Pose Estimation for Walnut Vibration Harvesting

by Ruichao Luo, Xiaopeng Yang, Leilei He, Wulan Mao, Rui Li, Spyros Fountas, Liling Yang and Longsheng Fu

Agriculture 2026, 16(5), 581; https://doi.org/10.3390/agriculture16050581 - 3 Mar 2026

Abstract

Efficient vibration based walnut harvesting relies on the accurate determination of clamping-points on tree trunks. Properly selected clamping points can significantly enhance vibration transmission efficiency while minimizing mechanical damage to trees. However, most existing studies focus on developing generalized vibration models applicable to [...] Read more.

Efficient vibration based walnut harvesting relies on the accurate determination of clamping-points on tree trunks. Properly selected clamping points can significantly enhance vibration transmission efficiency while minimizing mechanical damage to trees. However, most existing studies focus on developing generalized vibration models applicable to multiple trees, often overlooking the structural uniqueness of individual trees in clamping-point determination. This study proposes a monocular vision-based method for clamping-point determination in walnut vibration harvesting. Robustness and applicability in complex orchard environments are enhanced by introducing three keypoint annotation strategies with varying levels of structural constraints, namely 5-key-part annotation (5-KAS), 2-key-part annotation (2-KAS), and single-key-part annotation (1-KAS). Pose estimation models based on the YOLO architecture, including YOLOv8-pose, YOLO11-pose, and YOLOv12-pose, were evaluated to examine the effect of structure assisted annotation, and the results show that introducing structural constraints improves detection accuracy, training stability, and robustness. The YOLOv12-pose model combined with the 5-KAS achieves the best performance, with a precision of 95.8% and mean average precision (mAP) of 95.5%. Field harvesting experiments demonstrate that clamping-point prediction incorporating structural information achieves higher and more stable net harvesting rates. Overall, the proposed method offers a reliable and deployable solution for clamping-point determination using monocular RGB images, facilitating intelligent vibration harvesting in walnut orchards. Full article

(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)

30 pages, 8002 KB

Open AccessArticle

Improved Model and Strategy Optimization for Energy Management of the Power System in Range-Extended Sprayers Based on AVL-CRUISE and MATLAB/Simulink

by He Li, Yudong Guo, Shangshang Cheng, Tan Yao and Gongpei Cui

Agriculture 2026, 16(5), 580; https://doi.org/10.3390/agriculture16050580 - 3 Mar 2026

Abstract

The range-extended sprayer can effectively balance the requirements of economy and power performance, which represents the development and transformation trend of intelligent plant protection machinery in the future. To more intuitively and reliably explore the energy variation rules of the range-extended sprayer under [...] Read more.

The range-extended sprayer can effectively balance the requirements of economy and power performance, which represents the development and transformation trend of intelligent plant protection machinery in the future. To more intuitively and reliably explore the energy variation rules of the range-extended sprayer under different energy management strategies (EMSs) and achieve optimal fuel economy, a co-simulation platform for energy management of the range-extended sprayer under multi-condition cyclic operation was established based on AVL-CRUISE and MATLAB Simulink. Meanwhile, a fuzzy control-based EMS optimized by the particle swarm optimization (PSO) algorithm was proposed. Simulation results show that the comprehensive fuel consumption of the PSO-optimized fuzzy control EMS is 3.68 kg; compared with the conventional fuzzy control strategy, its fuel economy is improved by 4.90%, and by 8.23% compared with the multi-point power following strategy. Subsequently, an energy management test platform for the range-extended sprayer was built, and experimental verification was carried out. The platform test results indicate that the electricity difference between the platform test and the simulation test is 0.38%, and the fuel consumption difference is 1.6%, both within a reasonable range. This further verifies the reliability of the simulation platform for the improved energy management model and the feasibility of the proposed EMSs. The research content and results provide theoretical basis and technical support for the optimization of EMSs and the joint simulation method of energy management for range-extended sprayers. Full article

(This article belongs to the Section Agricultural Technology)

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24 pages, 1251 KB

Open AccessArticle

Ripening Crossroads: How Cultivar and Harvest Timing Shape the Extremadura Virgin Olive Oils

by Manuel A. Martínez-Cañas, Hédia Manai-Djebali, Guido Flamini, Daniel Cortés-Montaña, Isabel García-Corraliza and Ana González-Trejo

Agriculture 2026, 16(5), 579; https://doi.org/10.3390/agriculture16050579 - 3 Mar 2026

Abstract

Virgin olive oil (VOO) quality is strongly influenced by olive cultivar and fruit maturity stage, yet their combined effects remain insufficiently characterized in many traditional olive-growing regions. This study evaluated the physicochemical parameters, phenolic compounds content, antioxidant activity, fatty acid profile, volatile compounds, [...] Read more.

Virgin olive oil (VOO) quality is strongly influenced by olive cultivar and fruit maturity stage, yet their combined effects remain insufficiently characterized in many traditional olive-growing regions. This study evaluated the physicochemical parameters, phenolic compounds content, antioxidant activity, fatty acid profile, volatile compounds, and sensory attributes of VOOs obtained from five autochthonous cultivars of Extremadura (Spain)—‘Corniche’, ‘Manzanilla Cacereña’, ‘Morisca’, ‘Pico Limón’, and ‘Verdial de Badajoz’—harvested at three ripening stages (Green, Verging-on-ripe, and Ripe). Early harvest oils exhibited significantly higher total phenolic content (up to 478 mg/kg expressed by caffeic acid equivalent, CAE), oxidative stability (up to 188 h), intense green-fruity notes dominated by (E)-2-hexenal and (Z)-3-hexenal, and stronger bitterness and pungency. As ripening progressed, phenolic compounds and LOX-derived C6 volatiles markedly decreased, while oil yield, linoleic acid, saturated aldehydes, and oxidation markers increased in most cultivars. Cultivar-specific responses were evident: ‘Corniche’ and ‘Manzanilla Cacereña’ maintained higher oleic acid and stability, whereas ‘Morisca’ and ‘Pico Limón’ were more prone to phenolic compound loss and sensory deterioration at full ripeness. Multivariate analysis confirmed strong genotype × maturity interactions shaping oil quality. Optimal harvest timing must therefore be tailored to each cultivar to maximize phenolic content, oxidative stability, and sensory excellence while balancing industrial yield. Full article

(This article belongs to the Special Issue Advances in Olive and Olive Oil Quality: From Orchard Management to Consumer Products)

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20 pages, 1972 KB

Open AccessArticle

Segmentation Is Not the Purpose: A Wheat Impurity Regression Network Integrating Semantic Segmentation

by Yuhang Bian, Haoze Yu, Xiangdong Li, Xiao Zhang and Dong Li

Agriculture 2026, 16(5), 578; https://doi.org/10.3390/agriculture16050578 - 3 Mar 2026

Abstract

Real-time and accurate acquisition of the wheat impurity rate is a key technology for realizing intelligent cleaning operations, and it directly influences the quality of wheat harvest. This study proposes a novel impurity rate regression network named Segmentation is Not The Purpose (SNTP). [...] Read more.

Real-time and accurate acquisition of the wheat impurity rate is a key technology for realizing intelligent cleaning operations, and it directly influences the quality of wheat harvest. This study proposes a novel impurity rate regression network named Segmentation is Not The Purpose (SNTP). SNTP integrates a semantic segmentation network and an impurity rate regression network into a single neural architecture and replaces the DeepLabV3+ backbone with MobileNetV4, which serves as the segmentation branch of SNTP. Furthermore, a Transformer block is introduced into the regression branch to enable global feature extraction, and a Generalized Categorical Regression head is designed based on Distribution Focal Loss to improve regression accuracy. The SNTP model ultimately achieves an MIoU of 77.7%, an MPA of 83.3%, an MAE of 0.045, and an MSE of 0.005 on the validation set, with only 9.51M parameters and 17.98 GMACs of computation, successfully solving the overfitting problem in impurity rate regression networks and achieving high regression accuracy. SNTP is easy to optimize, requires no additional prior knowledge, and the performance of the SNTP model is unaffected by camera mounting height, making it exceptionally versatile for deployment and enabling real-time impurity rate detection, which is the key technology for intelligent cleaning. Full article

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

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31 pages, 2356 KB

Open AccessReview

Four Decades of Common Vole (Microtus arvalis Pallas 1778) Population Outbreaks in NW Spain: Transition from Environmentally Harmful Practices to Sustainable Integrated Pest Management (IPM)

by Javier Viñuela, Carlos Cuellar-Basterrechea, Miriam Báscones-Reina, Pedro P. Olea, Fernando Jubete, Julio C. Dominguez, Daniel Jareño, Ana E. Santamaría, Lorena Hernández-Garavís, María Calero-Riestra, Fernando Blanca, Paula González-Simón, Alfonso Paz, Jesus T. Garcia and Fernando Garcés

Agriculture 2026, 16(5), 577; https://doi.org/10.3390/agriculture16050577 - 3 Mar 2026

Abstract

The common vole is one of the mammalian pests causing more agricultural damage in Europe. Since the late 1970s, this species has invaded the Duero valley in NW Spain, colonizing ca. 5 million ha of agricultural areas of the valley in about 20 [...] Read more.

The common vole is one of the mammalian pests causing more agricultural damage in Europe. Since the late 1970s, this species has invaded the Duero valley in NW Spain, colonizing ca. 5 million ha of agricultural areas of the valley in about 20 years. Once settled in agricultural landscapes, the species experienced cyclic population outbreaks causing crop damages. The major vole population outbreak of 2006–2007 was managed by the Regional Government (Junta de Castilla y León, JCYL) mainly through large-scale application of anticoagulant rodenticides (ARs) and widespread destruction of field margins, natural vegetation patches, and crop stubbles by burning. These actions caused serious damage to regional agrarian biodiversity, including small game species. The coordinated action of scientific institutions and environmental NGOs, with the support of the main Spanish hunting association at a critical time, led to a progressive shift in pest management strategies during subsequent outbreaks, promoting the adoption of biological control and other management techniques causing less environmental damage. Finally, JCYL implemented an IPM program mainly based on biological control, good farming practices, and habitat management. This program has been increasingly adopted in recent years, leading to a marked reduction in chemical control and the complete elimination of burning as a tool of management. Over this period, the scientific knowledge of the species’ ecology has expanded substantially, providing key insights for the development and refinement of IPM strategies. Here, we synthesize this body of knowledge and provide additional recommendations to further improve the current IPM program, which may serve as a model for rodent pest management in other regions worldwide. Full article

(This article belongs to the Special Issue Integrated Pest Management Systems in Agriculture)

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16 pages, 3865 KB

Open AccessArticle

Oat–Rapeseed Intercropping Enhances Forage Yield and Quality in the Southern Foot of the Greater Khingan Mountains

by Li Han, Haoqi Jin, Zhe Wang, Xiaorong Wu, Xinyao Zhao, Hongjie Zhang, Jinhu Yang, Fang Luo and Lijun Li

Agriculture 2026, 16(5), 576; https://doi.org/10.3390/agriculture16050576 - 3 Mar 2026

Abstract

To address the seasonal forage shortage in the southern foothills of the Greater Khingan Mountains in Inner Mongolia, this study investigated the effects of intercropping forage oat with rapeseed on forage yield, nutritional quality, and resource utilization. The experiment was conducted in Arun [...] Read more.

To address the seasonal forage shortage in the southern foothills of the Greater Khingan Mountains in Inner Mongolia, this study investigated the effects of intercropping forage oat with rapeseed on forage yield, nutritional quality, and resource utilization. The experiment was conducted in Arun Banner, Hulunbuir City during 2023–2024, where a second crop was established after a first cut of forage oat. Three planting patterns were compared: monoculture oat (AO), monoculture rapeseed (BR), and oat–rapeseed intercropping (CO‖CR). The results showed that the yield of the intercropping system was higher than monoculture oat but lower than monoculture rapeseed. However, the system demonstrated an advantage in land use efficiency, with a land equivalent ratio (LER) of 1.161. Compared to their respective monocultures, intercropping significantly increased the dry matter yield of rapeseed by 38.4%, whereas the effect on oat yield was limited. Intercropping significantly increased the crude protein and crude fat content in both crops compared to their monocultures but had no significant effect on soluble sugar content. Furthermore, intercropping significantly reduced the neutral detergent fiber and acid detergent fiber content in both crops, resulting in higher relative feed value. Regarding water and nitrogen utilization, the water use efficiency of monoculture rapeseed was significantly higher than that of the intercropping system and monoculture oat by 20.7–21.5% and 90.2–113.2%. The total nitrogen accumulation in the intercropping system was significantly higher than in monoculture oat by 97.7–117.3% but showed no significant difference from monoculture rapeseed. In conclusion, adopting the oat–rapeseed intercropping pattern can significantly increase forage yield, improve nutritional quality, enhance water and nitrogen uptake and utilization efficiency, and achieve a coordinated improvement in high yield, quality, and efficiency for forage production in the southern foothills of the Greater Khingan Mountains. Full article

(This article belongs to the Special Issue Diversified Cropping Systems: Synergizing Productivity, Carbon Sequestration, and Ecosystem Services)

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34 pages, 4569 KB

Open AccessArticle

Analysis of AI-Based Predictive Models Using Vertical Farming Environmental Factors and Crop Growth Data

by Gwang-Hoon Jung, Hyeon-O Choe and Meong-Hun Lee

Agriculture 2026, 16(5), 575; https://doi.org/10.3390/agriculture16050575 - 3 Mar 2026

Abstract

Vertical farming requires precise environmental control, yet long-term multivariable analyses linking environmental dynamics and crop growth remain limited. This study analyzes a two-year operational dataset from a commercial vertical farm in South Korea to evaluate the suitability of advanced artificial intelligence models for [...] Read more.

Vertical farming requires precise environmental control, yet long-term multivariable analyses linking environmental dynamics and crop growth remain limited. This study analyzes a two-year operational dataset from a commercial vertical farm in South Korea to evaluate the suitability of advanced artificial intelligence models for harvest yield prediction. Conventional machine learning models and recent deep learning architectures were systematically benchmarked under identical conditions. Among them, the patch-based Transformer model achieved the highest predictive accuracy (R² = 0.942; RMSE = 5.81 g per plant). The variable-importance analysis revealed that daily light integral and CO₂ concentration were the dominant drivers of harvest yield variability, jointly accounting for more than 76% of total contribution. These findings demonstrate the effectiveness of Transformer-based architectures for long-term multivariate time series modeling and provide actionable insights for the data-driven optimization of vertical farming systems. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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17 pages, 9511 KB

Open AccessArticle

Effect of Lactobacillus plantarum LP160 with Freeze–Thaw Resistance Characteristics on Fermentation, Bacterial Community, and Metabolomics of Oat Silage in Qinghai–Tibet Plateau

by Haiping Li, Hao Guan, Zhifeng Jia, Wenhui Liu, Youjun Chen, Hui Wang, Qingqing Yang and Qingping Zhou

Agriculture 2026, 16(5), 574; https://doi.org/10.3390/agriculture16050574 - 3 Mar 2026

Abstract

Freeze–thaw cycles on the Qinghai–Tibetan Plateau inhibit microbial activity and challenge silage preservation. This paper aimed to elucidate how an indigenous, freeze–thaw-resistant Lactobacillus plantarum strain (LP160) improves oat silage quality under such stress. Oats were ensiled for 60 days under constant 20 °C [...] Read more.

Freeze–thaw cycles on the Qinghai–Tibetan Plateau inhibit microbial activity and challenge silage preservation. This paper aimed to elucidate how an indigenous, freeze–thaw-resistant Lactobacillus plantarum strain (LP160) improves oat silage quality under such stress. Oats were ensiled for 60 days under constant 20 °C (t) or freeze–thaw cycles (12 h at 20 °C/−5 °C; s) with or without LP160 inoculation. Samples after ensiling and 5-day aerobic exposure were analyzed for fermentation parameters, nutrients, microbiome, and non-targeted metabolomics using liquid chromatography–tandem mass spectrometry (LC-MS/MS). LP160 inoculation improved silage quality, as shown by the lower pH, ammoniacal nitrogen, neutral detergent fiber, acid detergent fiber contents as well as the greater amount of lactic acid. Key findings demonstrated that LP160 inoculation significantly enhanced Lactobacillus dominance, effectively curbed the growth of detrimental bacteria like Mucor, and regulated the microbial structure. During the aerobic exposure phase, the microbial community structures and successions varied under different temperature treatments. When inoculated under freeze–thaw conditions, the genus Bacillus increased, while Paenibacillus was not impeded. A total of 943 metabolites were identified, predominantly comprising amino acids, fatty acids, and the like. The expressions of metabolites with antioxidant and antibacterial properties were upregulated with LP160 inoculation. This led to the inhibition of protein hydrolysis and a reduction in ammonia–nitrogen production. The results of correlation analysis indicated that inoculating LP160 suppressed the proliferation of Mucor and enhanced the abundance of Torulaspora; meanwhile, the expression of L-palmitoylcarnitine involved in the fatty acid degradation pathway and fatty acid metabolism pathway was inhibited along with the generation of ammonia–nitrogen. Consequently, the degradation of fatty acids and proteins was restrained. The results of this paper provided new insights into the silage under freeze–thaw conditions. Full article

(This article belongs to the Section Crop Production)

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14 pages, 3375 KB

Open AccessArticle

Carbon Flux Dynamics and Response to Extreme High Temperature in Rice Ecosystems Across the Lower Reaches of the Yangtze River, China

by Lei Zhang, Anhong Guo, Yanlian Zhou, Yansen Xu, Xiaohui Wu and Zhaozhong Feng

Agriculture 2026, 16(5), 573; https://doi.org/10.3390/agriculture16050573 - 3 Mar 2026

Abstract

Under global climate warming, the impact of extreme high temperatures on carbon exchange in paddy rice ecosystems remains unclear, yet they exert a profound influence on the carbon cycle in agricultural ecosystems. The characteristics of carbon dioxide (CO₂) fluxes and their [...] Read more.

Under global climate warming, the impact of extreme high temperatures on carbon exchange in paddy rice ecosystems remains unclear, yet they exert a profound influence on the carbon cycle in agricultural ecosystems. The characteristics of carbon dioxide (CO₂) fluxes and their response to temperature were explored at two sites (Jurong and Jiangdu) across the lower reaches of the Yangtze River in China using open-path eddy covariance observations in 2021–2024. During the rice-growing season, considerable inter-annual spatial variability in high temperature was observed, with a higher frequency and larger intensity in Jurong relative to Jiangdu and more severe heat stress in 2022 relative to 2023. The jointing–booting stage was identified as the hotspot exposed to the highest frequency and longest duration of high temperature across multiple years. There was obvious variation in net ecosystem CO₂ exchange (NEE) throughout the rice-growing season, with the cumulative values being −462.2 ± 55.2 gC·m⁻² in 2021–2023 at Jurong and −362.4 ± 43.0 gC·m⁻² in 2022–2024 at Jiangdu. The period from jointing to flowering was identified as the most sensitive time slice for NEE variation, with a daily average value of −6.3 ± 0.2 gC·m⁻²·d⁻¹ in jointing–booting and −5.2 ± 2.2 gC·m⁻²·d⁻¹ in booting–flowering at Jurong, as well as −4.0 ± 0.7 gC·m⁻²·d⁻¹ in jointing–booting and −5.7 ± 1.1 gC·m⁻²·d⁻¹ in booting–flowering at Jiangdu. The respective correlation coefficients were −0.59 and −0.37 between periodical NEE and mean air temperature at Jurong and Jiangdu, meaning that NEE showed a decreasing trend as temperature increased, owing to the simultaneous but heterogeneous changes in gross ecosystem CO₂ exchange and ecosystem respiration. When the temperature was lower than 38 °C, the corresponding correlation coefficient reached −0.85 at Jurong and −0.52 at Jiangdu, suggesting that extreme high temperature prevented a decline in NEE. The response of NEE to temperature highlighted that NEE ceased to decrease when temperature surpassed 38 °C, implying that a critical threshold existed for limiting the carbon sink under extreme high temperature. These findings could provide insight for understanding carbon cycling in agricultural systems under an extreme climate. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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18 pages, 5587 KB

Open AccessArticle

The Addition of Artificial Humic Substances to Artificial Seedling Substrate Can Improve Soil Properties and Rice Quality

by Hui Qiao, Fanyu Meng, Husheng Xian, Changyuan Wang, Cheng Chang, Sikai Huang, Yongping Leng, Yibo Lan and Fan Yang

Agriculture 2026, 16(5), 572; https://doi.org/10.3390/agriculture16050572 - 3 Mar 2026

Abstract

The poor quality and scarcity of soil used for raising seedlings are key issues holding back the further development of the rice industry. Artificial humic substances (A-HS) and artificial soils are attracting increasing attention due to their cost-effectiveness and significant potential to improve [...] Read more.

The poor quality and scarcity of soil used for raising seedlings are key issues holding back the further development of the rice industry. Artificial humic substances (A-HS) and artificial soils are attracting increasing attention due to their cost-effectiveness and significant potential to improve rice cultivation. This study used native soil (NS), engineered soil (ES) and rice straw to create artificial substrates (AES and ANS) using humification–hydrothermal carbonization technology (24 h treatment of NS and ES with rice straw at 200 °C and 2 MPa). Experiments on cultivation of the rice seedlings were conducted using initial soils (ES and NS) and artificial soils with addition of A-HS (AES+A-HS and ANS+A-HS). This study examined the nutrient content and microbial environment of the seedling substrates as well as the changes in growth and development of the rice seedlings. The combination of rice straw biochar in artificial soils (AES and ANS) with A-HS significantly increased the content of soil organic carbon (SOC) and enhanced the nutrient levels, such as total nitrogen and available phosphorus. Furthermore, it enhanced the microbial diversity, and it increased the abundance of microorganisms such as Actinomycetota, Chloroflexota, and Basidiomycota, thereby improved the soil microbial environment. An enhanced soil nutrient content and improved microbial environment effectively promoted the rice seedling growth. Compared to the original soils (ES and NS), before transplanting to paddy fields, the stem width of the seedlings increased by 5.1% (AES+A-HS) and 10.2% (ANS+A-HS), and their height increased by 18.7% (AES+A-HS) and 4.5% (ANS+A-HS). The rice seedling emergence increased by 6.1% (AES+A-HS) and 3.9% (ANS+A-HS), and the transplant survival rate also increased by 4.1% (AES+A-HS) and 2.9% (ANS+A-HS). This study provides an effective approach to alleviating the scarcity of rice seedling substrates and improving the quality of rice seedlings, and it provides an effective foundation for increasing the yield of rice. Full article

(This article belongs to the Section Agricultural Soils)

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16 pages, 1696 KB

Open AccessArticle

Effect of Reduced Water Potential on Imbibition Curve and the Triphasic Pattern of Seeds in Solanaceae Species

by Astryani Rosyad, Abdul Qadir, M Rahmad Suhartanto, Okti Syah Isyani Permatasari, Arif Tirtana and Punjung Medaraji Suwarno

Agriculture 2026, 16(5), 571; https://doi.org/10.3390/agriculture16050571 - 2 Mar 2026

Abstract

Seed imbibition and germination under water stress conditions are critical determinants of successful crop establishment; therefore, understanding imbibition responses under osmotic stress is essential for improving seed quality assessment and management strategies for crop production under suboptimal water availability. This study aimed to [...] Read more.

Seed imbibition and germination under water stress conditions are critical determinants of successful crop establishment; therefore, understanding imbibition responses under osmotic stress is essential for improving seed quality assessment and management strategies for crop production under suboptimal water availability. This study aimed to analyze the effect of reduced water potential on the imbibition curve and triphasic pattern of seeds in several Solanaceae species. The study used seeds from three Solanaceae crops—chili (Capsicum annuum L., varieties Simpatik and Sempurna), tomato (Solanum lycopersicum L., varieties Niki and Rempai), and eggplant (Solanum melongena L., varieties Tangguh and Provita). The seeds were subjected to various levels of osmotic stress using polyethylene glycol (PEG 6000) to simulate water potentials of 0.00, −0.30, −1.90, and −4.10 MPa. Lower water potential in the growing medium reduced the seed’s ability to absorb the water. The triphasic pattern consistently appeared only in chili seeds, whereas in tomatoes and eggplants, it varied across varieties and water potential conditions. Lower water potential delayed the end of Phase I and prolonged the duration of Phase II. These findings confirm that the standard imbibition pattern cannot be generalized to all seeds, and therefore, the imbibition response is specific to seed type, variety, and germination environment. Full article

(This article belongs to the Section Seed Science and Technology)

27 pages, 3109 KB

Open AccessArticle

FAL-YOLO: A Keypoint Detection Method for Harvest Crates in Farmland Environments Based on an Improved YOLOv8-Pose Algorithm

by Jing Huang, Shengjun Shi, Shilei Lyu, Zhihui Chen, Yikai Lin and Zhen Li

Agriculture 2026, 16(5), 570; https://doi.org/10.3390/agriculture16050570 - 2 Mar 2026

Abstract

To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection [...] Read more.

To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection model. Using YOLOv8n-Pose as the baseline framework, the model integrates a C2f-ContextGuided backbone and a Slim-Neck feature fusion layer. Furthermore, a LSCD-LQE lightweight detection head is designed, and an Inner-MPDIoU loss function is introduced to enhance keypoint detection performance under complex backgrounds and occluded conditions. Experimental results on the self-constructed farmland harvest crate dataset indicate that FAL-YOLO requires only 1.71 M parameters and 4.5 GFLOPs of computational cost, representing reductions of 44.5% and 45.8% compared to YOLOv8n-Pose, while achieving an mAP@0.5 of 94.9%, corresponding to an improvement of 1.2%. Additionally, by establishing correspondences between keypoints and the 3D model through the PnP algorithm, the 3D pose of the crate can be reconstructed, providing reliable spatial input for robotic arm manipulation. The results demonstrate that FAL-YOLO achieves an effective balance between model lightweightness and detection accuracy, providing an efficient solution for automatic identification and grasping of harvest crates in farmland environments. Full article

(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)

15 pages, 967 KB

Open AccessArticle

Natural Products—Part of a Strategy to Mitigate the Impact of Climate Change on Honey Bees

by Koycho Koev, Mariya Ganeva and Petya Orozova

Agriculture 2026, 16(5), 569; https://doi.org/10.3390/agriculture16050569 - 2 Mar 2026

Abstract

Climate change exerts an increasing impact on the health and resilience of honey bees through a combination of rising temperatures, altered precipitation patterns, and intensified parasitic and infectious pressure. The present study aims to analyze climatic conditions in Bulgaria for the period 2021–2024 [...] Read more.

Climate change exerts an increasing impact on the health and resilience of honey bees through a combination of rising temperatures, altered precipitation patterns, and intensified parasitic and infectious pressure. The present study aims to analyze climatic conditions in Bulgaria for the period 2021–2024 and to evaluate the results of a national survey conducted among beekeepers, focusing on winter colony losses during the 2023/2024 season and the feeding strategies applied. The survey was carried out in 2024 among 70 beekeepers from 20 administrative regions of the country, managing a total of 8935 bee colonies. The data were analyzed using descriptive statistics and stratified by region. The reported average winter mortality was 2.22% (198 colonies), with pronounced territorial variability. The most frequently indicated self-reported probable cause of losses was bee diseases, with varroosis identified as the dominant factor. Analysis of management practices revealed widespread application of combined feeding schemes based on plant-derived supplementary feeds, primarily administered in spring (March–April) and late summer (August–September). The obtained results differ from published national data for 2024, according to which total colony losses reached 16.3%, while losses associated with mortality or severe demographic collapse accounted for 11.6%. Despite the limitations inherent to the survey-based approach and self-reported data, the results suggest that integrated management combining parasite control with targeted nutritional support through the use of Bulgarian herbal supplementary feeds may coincide with the winter survival patterns reported within the surveyed sample. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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27 pages, 1453 KB

Open AccessArticle

Water Regime Effects on Phosphorus Mobility and the Performance of Liquid Phosphorus Fertilizers in Contrasting Soils

by Lucian Raus and Diana Elena Bolohan

Agriculture 2026, 16(5), 568; https://doi.org/10.3390/agriculture16050568 - 2 Mar 2026

Abstract

The behavior of phosphorus (P) fertilizers in soil is governed not only by fertilizer solubility, but also by P mobility and vertical redistribution within the soil profile under contrasting water regime. This study aimed to investigate the combined effects of water regime, fertilizer [...] Read more.

The behavior of phosphorus (P) fertilizers in soil is governed not only by fertilizer solubility, but also by P mobility and vertical redistribution within the soil profile under contrasting water regime. This study aimed to investigate the combined effects of water regime, fertilizer type, and soil properties on the vertical redistribution of ammonium acetate–lactate extractable phosphorus (P-AL) in the surface soil layer under controlled pot conditions. Experiments were conducted using three soils with contrasting chemical properties: AC-LO (acidic loam, pH 5.9), NE-CL (neutral clay loam, pH 6.8), and AL-SL (alkaline sandy loam, pH 8.0). Four simulated rainfall regimes were applied at a constant rate of 25 mm day⁻¹, corresponding to cumulative water inputs of 0 mm (W0), 50 mm (W50), 100 mm (W100), and 150 mm (W150). Fertilizer treatments included an unfertilized control (NF), a liquid NP 4–18 fertilizer applied at a low dose (L1), a liquid NP 4–18 fertilizer applied at a high dose (L2), and a solid NPK 15–15–15 fertilizer (S). Water regime exerted the strongest control on P mobility, with P-AL increasing by approximately 40–60% from W0 to W150, depending on soil type. In AC-LO, strong P fixation under low moisture minimized differences among fertilizer treatments, whereas under higher moisture (W100–W150), liquid fertilizers—particularly L2—resulted in P-AL levels approximately 10–30% higher than those of the solid fertilizer. In NE-CL, P mobility was moderate and, under W100–W150, L2 produced P-AL values approximately 10–15% higher than the solid fertilizer, promoting a more uniform P redistribution within the 2–8 cm layer. In AL-SL, the response under wet conditions depended on the water regime: at W100, L2 generated P-AL values comparable to the solid fertilizer, whereas at W150, L2 increased P-AL by approximately 11% relative to the solid form. Overall, the results indicate that soil chemical properties primarily regulate the extent of phosphorus redistribution, while water regime controls its intensity and fertilizer form influences the initial spatial configuration of P within the surface soil layer. The findings provide mechanistic insight into short-range phosphorus transport in soil, without allowing direct inferences regarding agronomic efficiency or crop response. Full article

(This article belongs to the Special Issue Enhancing Soil Health and Water Use Efficiency in Sustainable Agriculture—2nd Edition)

16 pages, 2683 KB

Open AccessArticle

Entomopathogenic Effects of the Plant-Associated Fungus Ochroconis guangxiensis X22 Strain on the Physiological and Metabolic State of the Rice-Pest Planthopper, Sogatella furcifera

by Yanxin Yu, Fenghua Zeng, Yanyan Long, Zhengxiang Sun, Xinghao Wang, Bixia Qin, Jihui Yu, Wenlong Zhang, Yan Zhang and Ling Xie

Agriculture 2026, 16(5), 567; https://doi.org/10.3390/agriculture16050567 - 2 Mar 2026

Abstract

The white-backed planthopper (Sogatella furcifera) is a major pest in rice-growing regions worldwide. It severely limits rice production through piercing–sucking feeding, oviposition injury, and by efficiently transmitting the Southern Rice Black-Streaked Dwarf Virus (SRBSDV). Previous studies demonstrated that the dark septate [...] Read more.

The white-backed planthopper (Sogatella furcifera) is a major pest in rice-growing regions worldwide. It severely limits rice production through piercing–sucking feeding, oviposition injury, and by efficiently transmitting the Southern Rice Black-Streaked Dwarf Virus (SRBSDV). Previous studies demonstrated that the dark septate endophytic fungus Ochroconis guangxiensis strain X22 exhibits control activity against SRBSDV. To further evaluate its biocontrol potential, this study investigated the effects of the X22 strain on S. furcifera, the primary vector of SRBSDV. In this study, we established an X22–rice symbiotic system to evaluate its effects on the biological traits of S. furcifera. The results showed that, compared with a clear water treatment, the X22 strain significantly reduced the feeding amount (29.02%), egg-laying amount (12.30%), and hatching rate (11.58%) of S. furcifera. Gene expression analysis showed that the relative expression levels of the Target of Rapamycin (TOR) and vitellogenin (Vg) genes in one-day-old S. furcifera from the X22 treatment group were modestly downregulated, although no significant differences were detected compared with the control. Enzyme activity assays revealed that between 72 and 120 h post-treatment, the activities of detoxification enzymes, including carboxylesterase (CarE) and acetylcholinesterase (AChE), generally declined following X22 exposure. In contrast, the activities of protective enzymes, superoxide dismutase (SOD) and catalase (CAT), as well as certain digestive enzymes, α-amylase (α-AL) and trypsin, were induced. Conversely the activities of glutathione peroxidase (GSH-Px) and lipase (LPS) were suppressed. However, the physiological mechanisms underlying its effect on S. furcifera remain unclear. Collectively, these results demonstrate that the O. guangxiensis X22 strain inhibits S. furcifera reproduction by disrupting its physiological metabolism through multiple pathways, providing a mechanistic basis for its development as an environmentally friendly biocontrol agent. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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48 pages, 6461 KB

Open AccessArticle

A Disentangled Prototype-Driven Continual Learning Framework for Fault Diagnosis of Cotton Harvester Picking-Head Drivetrains Under Gradually Expanding Operating Conditions

by Huachao Jiao, Wenlei Sun, Hongwei Wang and Xiaojing Wan

Agriculture 2026, 16(5), 566; https://doi.org/10.3390/agriculture16050566 - 2 Mar 2026

Abstract

The picking-head drivetrain is a critical transmission component of cotton harvesters, and its fault condition monitoring and diagnosis are essential for ensuring stable and reliable operation of the equipment. In practical engineering applications, diagnostic models for picking-head drivetrains are typically initialized using data [...] Read more.

The picking-head drivetrain is a critical transmission component of cotton harvesters, and its fault condition monitoring and diagnosis are essential for ensuring stable and reliable operation of the equipment. In practical engineering applications, diagnostic models for picking-head drivetrains are typically initialized using data collected under a limited number of representative operating conditions. Although sufficient fault samples can often be obtained during the initial training stage, the coverage of operating conditions is inherently restricted. As the model is deployed and used in the field, fault samples collected under new operating conditions are gradually acquired in a stage-wise manner. How to stably update the diagnostic model while the operating-condition coverage continuously expands, and how to avoid performance degradation and catastrophic forgetting, remain critical challenges. To address these issues, this paper proposes a continual learning method, termed DP-CL (Disentangled Prototype-Driven Continual Learning), for fault diagnosis of cotton harvester picking-head drivetrains under gradually expanding operating conditions. The proposed method is built upon an explicit disentanglement of condition-invariant features and condition-specific features. Within a unified framework, three types of structured prototypes, including class prototypes, condition prototypes, and condition-aware class prototypes, are constructed to form a multi-level representation hierarchy. A prototype-driven structured update mechanism is then employed to impose stable constraints on fault-discriminative semantics across different operating conditions. In addition, an operating-condition similarity measurement based on condition-specific features is introduced, based on which a proportion-adaptive sample selection strategy is designed. This strategy enables controlled knowledge transfer and preservation of discriminative structures during multi-stage model updates. Experimental results obtained under a laboratory-constructed cumulative operating-condition expansion scenario demonstrate that the proposed method achieves superior performance in terms of overall performance retention, cross-stage stability, and resistance to performance degradation. Moreover, as the number of operating conditions increases, the proposed method maintains a relatively smooth performance variation trend, while preserving clear class structures and a controllable level of confusion. These results validate the effectiveness of the proposed approach for stable fault diagnosis under expanding operating-condition coverage. Full article

(This article belongs to the Section Agricultural Technology)

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