Agriculture

10 pages, 960 KiB

Open AccessArticle

Study on the Vectoring Potential of Halyomorpha halys for Pantoea stewartii subsp. stewartii, the Pathogen Causing Stewart’s Disease in Maize

by Francesca Costantini, Agostino Strangi, Fabio Mosconi, Leonardo Marianelli, Giuseppino Sabbatini-Peverieri, Pio Federico Roversi and Valeria Scala

Agriculture 2025, 15(15), 1671; https://doi.org/10.3390/agriculture15151671 (registering DOI) - 2 Aug 2025

Abstract

Pantoea stewartii subsp. stewartii (Pss) is a Gram-negative bacterium first documented in North America, and is the causal agent of Stewart’s disease in maize (Zea mays), especially in sweet corn. First identified in North America, it is primarily spread by insect [...] Read more.

Pantoea stewartii subsp. stewartii (Pss) is a Gram-negative bacterium first documented in North America, and is the causal agent of Stewart’s disease in maize (Zea mays), especially in sweet corn. First identified in North America, it is primarily spread by insect vectors like the corn flea beetle (Chaetocnema Pulicaria) in the United States. However, Pss has since spread globally—reaching parts of Africa, Asia, the Americas, and Europe—mainly through the international seed trade. Although this trade is limited, it has still facilitated the pathogen’s global movement, as evidenced by numerous phytosanitary interceptions. Recent studies in Italy, as indicated in the EFSA journal, reported that potential alternative vectors were identified, including Phyllotreta spp. and the invasive Asian brown marmorated stink bug (Halyomorpha halys); the latter tested positive in PCR screenings, raising concerns due to its broad host range and global distribution. This information has prompted studies to verify the ability of Halyomorpha halys to vector Pss to assess the risk and prevent the further spread of Pss in Europe. In this study, we explored the potential transmission of Pss by the brown marmorated stink bugs in maize plants, following its feeding on Pss-inoculated maize, as well as the presence of Pss within the insect’s body. Full article

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

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27 pages, 1382 KiB

Open AccessReview

Application of Non-Destructive Technology in Plant Disease Detection: Review

by Yanping Wang, Jun Sun, Zhaoqi Wu, Yilin Jia and Chunxia Dai

Agriculture 2025, 15(15), 1670; https://doi.org/10.3390/agriculture15151670 (registering DOI) - 1 Aug 2025

Abstract

In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on [...] Read more.

In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field. Full article

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

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22 pages, 3023 KiB

Open AccessArticle

Improving Grain Safety Using Radiation Dose Technologies

by Raushangul Uazhanova, Meruyert Ametova, Zhanar Nabiyeva, Igor Danko, Gulzhan Kurtibayeva, Kamilya Tyutebayeva, Aruzhan Khamit, Dana Myrzamet, Ece Sogut and Maxat Toishimanov

Agriculture 2025, 15(15), 1669; https://doi.org/10.3390/agriculture15151669 (registering DOI) - 1 Aug 2025

Abstract

Reducing post-harvest losses of cereal crops is a key challenge for ensuring global food security amid the limited arable land and growing population. This study investigates the effectiveness of electron beam irradiation (5 MeV, ILU-10 accelerator) as a physical decontamination method for various [...] Read more.

Reducing post-harvest losses of cereal crops is a key challenge for ensuring global food security amid the limited arable land and growing population. This study investigates the effectiveness of electron beam irradiation (5 MeV, ILU-10 accelerator) as a physical decontamination method for various cereal crops cultivated in Kazakhstan. Samples were irradiated at doses ranging from 1 to 5 kGy, and microbiological indicators—including Quantity of Mesophilic Aerobic and Facultative Anaerobic Microorganisms (QMAFAnM), yeasts, and molds—were quantified according to national standards. Experimental results demonstrated an exponential decline in microbial contamination, with a >99% reduction achieved at doses of 4–5 kGy. The modeled inactivation kinetics showed strong agreement with the experimental data: R² = 0.995 for QMAFAnM and R² = 0.948 for mold, confirming the reliability of the exponential decay models. Additionally, key quality parameters—including protein content, moisture, and gluten—were evaluated post-irradiation. The results showed that protein levels remained largely stable across all doses, while slight but statistically insignificant fluctuations were observed in moisture and gluten contents. Principal component analysis and scatterplot matrix visualization confirmed clustering patterns related to radiation dose and crop type. The findings substantiate the feasibility of electron beam treatment as a scalable and safe technology for improving the microbiological quality and storage stability of cereal crops. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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24 pages, 3888 KiB

Open AccessArticle

Agronomic Biofortification: Enhancing the Grain Nutritional Composition and Mineral Content of Winter Barley (Hordeum vulgare L.) Through Foliar Nutrient Application Under Different Soil Tillage Methods

by Amare Assefa Bogale, Zoltan Kende, István Balla, Péter Mikó, Boglárka Bozóki and Attila Percze

Agriculture 2025, 15(15), 1668; https://doi.org/10.3390/agriculture15151668 (registering DOI) - 1 Aug 2025

Abstract

Enhancing the nutritional content of crops is crucial for safeguarding human health and mitigating global hunger. A viable method for attaining this goal is the planned implementation of various agronomic practices, including tillage and nutrient provision. A field experiment was executed at the [...] Read more.

Enhancing the nutritional content of crops is crucial for safeguarding human health and mitigating global hunger. A viable method for attaining this goal is the planned implementation of various agronomic practices, including tillage and nutrient provision. A field experiment was executed at the Hungarian University of Agriculture and Life Sciences in Gödöllő in the 2023 and 2024 growing seasons. The study aimed to assess the effects of foliar nutrient supply and soil tillage methods on the grain nutritional composition and mineral content of winter barley. Employing a split-plot design with three replications, the experiment included four nutrient treatments (control, bio-cereal, bio-algae, and MgSMnZn blend) and two soil tillage types (i.e., plowing and cultivator). The results indicated that while protein content was not influenced by the main effects of nutrients and tillage, the levels of β-glucan, starch, crude ash, and moisture content were significantly (p < 0.05) affected by the nutrient treatments and by growing year, treated as a random factor. Notably, bio-algae and bio-cereal nutrients, combined with cultivator tillage, enhanced β-glucan content. All applied nutrient treatments increased the level of starch compared to the control. With regard to grain mineral content, the iron and zinc content responded to the nutrient supply, tillage, and growing year. However, applying a multiple-nutrient composition-based treatment did not increase iron and zinc levels, suggesting that individual applications may be more effective for increasing the content of these minerals in grains. Cultivator tillage improved iron and zinc levels. Moreover, manganese (Mn) and copper (Cu) were predominantly affected by nutrient availability and by growing seasons as a random factor. Therefore, to improve grain quality, this study emphasizes the significance of proper nutrient and tillage methods by focusing on the intricate relationships between agronomic techniques and environmental factors that shape barley’s nutritional profile. Full article

(This article belongs to the Special Issue Strategies to Improve the Security and Nutritional Quality of Crop Species—2nd Edition)

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16 pages, 2656 KiB

Open AccessArticle

Plastic Film Mulching Regulates Soil Respiration and Temperature Sensitivity in Maize Farming Across Diverse Hydrothermal Conditions

by Jianjun Yang, Rui Wang, Xiaopeng Shi, Yufei Li, Rafi Ullah and Feng Zhang

Agriculture 2025, 15(15), 1667; https://doi.org/10.3390/agriculture15151667 (registering DOI) - 1 Aug 2025

Abstract

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but [...] Read more.

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems. Full article

(This article belongs to the Topic Advances in Water and Soil Management Towards Climate Change Adaptation)

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18 pages, 1618 KiB

Open AccessArticle

Native Grass Enhances Bird, Dragonfly, Butterfly and Plant Biodiversity Relative to Conventional Crops in Midwest, USA

by Steven I. Apfelbaum, Susan M. Lehnhardt, Michael Boston, Lea Daly, Gavin Pinnow, Kris Gillespie and Donald M. Waller

Agriculture 2025, 15(15), 1666; https://doi.org/10.3390/agriculture15151666 (registering DOI) - 1 Aug 2025

Abstract

Conspicuous declines in native grassland habitats have triggered sharp reductions in grassland birds, dragonflies, butterflies, and native plant populations and diversity. We compared these biotic groups among three crop type treatments: corn, alfalfa, and a perennial native grass, Virginia wild rye, (Elymus [...] Read more.

Conspicuous declines in native grassland habitats have triggered sharp reductions in grassland birds, dragonflies, butterflies, and native plant populations and diversity. We compared these biotic groups among three crop type treatments: corn, alfalfa, and a perennial native grass, Virginia wild rye, (Elymus virginicus L.) or VWR. This crop type had 2-3X higher bird, dragonfly, butterfly and plant species richness, diversity, and faunal abundance relative to alfalfa and corn types. VWR crop fields also support more obligate grassland bird species and higher populations of dragonfly and butterfly species associated with grasslands and wet meadows. In contrast, the corn and alfalfa types support few or no obligatory grassland birds and mostly non-native insects such as the white cabbage looper (Artogeia rapae L.), the common yellow sulfur butterfly (Colias philodice Godart.), and the mobile and migratory common green darner dragonfly (Anax junius Drury.). In sum, the VWR perennial native grass crop type offers a special opportunity to improve the diversity and abundance of grassland bird species, beneficial insect species, and many native plant species within agricultural landscapes. Full article

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

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20 pages, 998 KiB

Open AccessArticle

Colony Nutrition Enhances Bee Resilience to Fungicides, While the Benefit of Propolis Supplementation Depends on Stress Conditions

by Yara Martins Molina Ferraz, Aline Yukari Kato, Tainá Angelica de Lima Freitas, Cássia Regina de Avelar Gomes, Thais Regina Ramos Alves, Matheus Franco Trivellato, Samir Moura Kadri, Ricardo de Oliveira Orsi, David De Jong, Jaqueline Dalbello Biller and Daniel Nicodemo

Agriculture 2025, 15(15), 1665; https://doi.org/10.3390/agriculture15151665 (registering DOI) - 1 Aug 2025

Abstract

Enhanced colony nutrition can support brood development, resulting in better physiological conditions and increased resilience in adult honey bees, particularly under stress. This study investigated the effects of colony nutrition and adult dietary supplementation with green propolis on bee health under fungicide exposure. [...] Read more.

Enhanced colony nutrition can support brood development, resulting in better physiological conditions and increased resilience in adult honey bees, particularly under stress. This study investigated the effects of colony nutrition and adult dietary supplementation with green propolis on bee health under fungicide exposure. Colonies were managed under food restriction or nutritional supplementation for 22 weeks. Newly emerged bees from each colony were then caged and fed protein diets consisting of honey-pollen patties contaminated or not with fungicide, and sucrose sugar syrup with or without aqueous green propolis extract. Bees from supplemented colonies showed greater body weight, higher hemolymph protein levels, and higher consumption of protein food after seven days in cages. Fungicide exposure reduced hemolymph protein levels, altered the expression of detoxification and immune-related genes, and significantly decreased bee survival. Interestingly, propolis supplementation alone changed gene expression patterns and slightly reduced longevity compared to bees not exposed to propolis or fungicide. However, under fungicide stress, bees that ingested propolis survived longer, indicating a protective effect. While colony nutritional supplementation clearly promotes honey bee resilience against fungicide exposure, feeding propolis also showed promising effects, though further studies are needed to determine an optimal dietary concentration. Full article

(This article belongs to the Special Issue Honey Bees and Wild Pollinators in Agricultural Ecosystems)

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Graphical abstract

32 pages, 9914 KiB

Open AccessReview

Technology Advancements and the Needs of Farmers: Mapping Gaps and Opportunities in Row Crop Farming

by Rana Umair Hameed, Conor Meade and Gerard Lacey

Agriculture 2025, 15(15), 1664; https://doi.org/10.3390/agriculture15151664 (registering DOI) - 1 Aug 2025

Abstract

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the [...] Read more.

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

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

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16 pages, 3001 KiB

Open AccessArticle

Tractor Path Tracking Control Method Based on Prescribed Performance and Sliding Mode Control

by Liwei Zhu, Weiming Sun, Qian Zhang, En Lu, Jialin Xue and Guohui Sha

Agriculture 2025, 15(15), 1663; https://doi.org/10.3390/agriculture15151663 - 1 Aug 2025

Abstract

In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is [...] Read more.

In addressing the challenges of low path tracking accuracy and poor robustness during tractor autonomous operation, this paper proposes a path tracking control method for tractors that integrates prescribed performance with sliding mode control (SMC). A key feature of this control method is its inherent immunity to system parameter perturbations and external disturbances, while ensuring path tracking errors are constrained within a predefined range. First, the tractor is simplified into a two-wheeled vehicle model, and a path tracking error model is established based on the reference operation trajectory. By defining a prescribed performance function, the constrained tracking control problem is transformed into an unconstrained stability control problem, guaranteeing the boundedness of tracking errors. Then, by incorporating SMC theory, a prescribed performance sliding mode path tracking controller is designed to achieve robust path tracking and error constraint for the tractor. Finally, both simulation and field experiments are conducted to validate the method. The results demonstrate that compared with the traditional SMC method, the proposed method effectively mitigates the impact of complex farmland conditions, reducing path tracking errors while enforcing strict error constraints. Field experiment data shows the proposed method achieves an average absolute error of 0.02435 m and a standard deviation of 0.02795 m, confirming its effectiveness and superiority. This research lays a foundation for the intelligent development of agricultural machinery. Full article

(This article belongs to the Section Agricultural Technology)

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28 pages, 4026 KiB

Open AccessArticle

Multi-Trait Phenotypic Analysis and Biomass Estimation of Lettuce Cultivars Based on SFM-MVS

by Tiezhu Li, Yixue Zhang, Lian Hu, Yiqiu Zhao, Zongyao Cai, Tingting Yu and Xiaodong Zhang

Agriculture 2025, 15(15), 1662; https://doi.org/10.3390/agriculture15151662 - 1 Aug 2025

Abstract

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based [...] Read more.

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

(This article belongs to the Section Crop Production)

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32 pages, 444 KiB

Open AccessArticle

Does Digital Literacy Increase Farmers’ Willingness to Adopt Livestock Manure Resource Utilization Modes: An Empirical Study from China

by Xuefeng Ma, Yahui Li, Minjuan Zhao and Wenxin Liu

Agriculture 2025, 15(15), 1661; https://doi.org/10.3390/agriculture15151661 - 1 Aug 2025

Abstract

Enhancing farmers’ digital literacy is both an inevitable requirement for adapting to the digital age and an important measure for promoting the sustainable development of livestock and poultry manure resource utilization. This study surveyed and obtained data from 1047 farm households in Ningxia [...] Read more.

Enhancing farmers’ digital literacy is both an inevitable requirement for adapting to the digital age and an important measure for promoting the sustainable development of livestock and poultry manure resource utilization. This study surveyed and obtained data from 1047 farm households in Ningxia and Gansu, two provinces in China that have long implemented livestock manure resource utilization policies, from December 2023 to January 2024, and employed the binary probit model to analyze how digital literacy influences farmers’ willingness to adopt two livestock manure resource utilization modes, as well as to analyze the moderating role of three policy regulations. This paper also explores the heterogeneous results in different village forms and income groups. The results are as follows: (1) Digital literacy significantly and positively impacts farmers’ willingness to adopt both the “household collection” mode and the “livestock community” mode. For every one-unit increase in a farmer’s digital literacy, the probability of farmers’ willingness to adopt the “household collection” mode rises by 22 percentage points, and the probability of farmers’ willingness to adopt the “livestock community” mode rises by 19.8 percentage points. After endogeneity tests and robustness checks, the conclusion still holds. (2) Mechanism analysis results indicate that guiding policy and incentive policy have a positive moderation effect on the link between digital literacy and the willingness to adopt the “household collection” mode. Meanwhile, incentive policy also positively moderates the relationship between digital literacy and the willingness to adopt the “livestock community” mode. (3) Heterogeneity analysis results show that the positive effect of digital literacy on farmers’ willingness to adopt two livestock manure resource utilization modes is stronger in “tight-knit society” rural areas and in low-income households. (4) In further discussion, we find that digital literacy removes the information barriers for farmers, facilitating the conversion of willingness into behavior. The value of this study is as follows: this paper provides new insights for the promotion of livestock and poultry manure resource utilization policies in countries and regions similar to the development process of northwest China. Therefore, enhancing farmers’ digital literacy in a targeted way, strengthening the promotion of grassroots policies on livestock manure resource utilization, formulating diversified ecological compensation schemes, and establishing limited supervision and penalty rules can boost farmers’ willingness to adopt manure resource utilization models. Full article

(This article belongs to the Special Issue Application of Biomass in Agricultural Circular Economy)

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19 pages, 4152 KiB

Open AccessArticle

Optimization of Greenhouse Structure Parameters Based on Temperature and Velocity Distribution Characteristics by CFD—A Case Study in South China

by Xinyu Wei, Yizhi Ou, Ziwei Li, Jiaming Guo, Enli Lü, Fengxi Yang, Yanhua Liu and Bin Li

Agriculture 2025, 15(15), 1660; https://doi.org/10.3390/agriculture15151660 - 1 Aug 2025

Abstract

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse [...] Read more.

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China. 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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1 pages, 135 KiB

Open AccessCorrection

Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224

by Haipeng Lin, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie and Huhu Chen

Agriculture 2025, 15(15), 1659; https://doi.org/10.3390/agriculture15151659 - 1 Aug 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)

13 pages, 1189 KiB

Open AccessArticle

Positive Effects of Reduced Tillage Practices on Earthworm Population Detected in the Early Transition Period

by Irena Bertoncelj, Anže Rovanšek and Robert Leskovšek

Agriculture 2025, 15(15), 1658; https://doi.org/10.3390/agriculture15151658 - 1 Aug 2025

Abstract

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when [...] Read more.

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article

(This article belongs to the Section Agricultural Soils)

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20 pages, 1379 KiB

Open AccessArticle

Combined Effects of Polyethylene and Bordeaux Mixture on the Soil–Plant System: Phytotoxicity, Copper Accumulation and Changes in Microbial Abundance

by Silvia Romeo-Río, Huguette Meta Foguieng, Antía Gómez-Armesto, Manuel Conde-Cid, David Fernández-Calviño and Andrés Rodríguez-Seijo

Agriculture 2025, 15(15), 1657; https://doi.org/10.3390/agriculture15151657 - 1 Aug 2025

Abstract

Greenhouses have positively impacted plant production by allowing the cultivation of different crops per year. However, the accumulation of agricultural plastics, potentially contaminated with agrochemicals, raises environmental concerns. This work evaluates the combined effect of Bordeaux mixture and low-density polyethylene (LDPE) microplastics (<5 [...] Read more.

Greenhouses have positively impacted plant production by allowing the cultivation of different crops per year. However, the accumulation of agricultural plastics, potentially contaminated with agrochemicals, raises environmental concerns. This work evaluates the combined effect of Bordeaux mixture and low-density polyethylene (LDPE) microplastics (<5 mm) on the growth of lettuce (Lactuca sativa L.) and soil microbial communities. Different levels of Bordeaux mixture (0, 100 and 500 mg kg⁻¹), equivalent to Cu(II) concentrations (0, 17 and 83 mg kg⁻¹), LDPE microplastics (0, 1% and 5%) and their combination were selected. After 28 days of growth, biometric and photosynthetic parameters, Cu uptake, and soil microbial responses were evaluated. Plant germination and growth were not significantly affected by the combination of Cu and plastics. However, individual Cu treatments influenced root and shoot length and biomass. Chlorophyll and carotenoid concentrations increased with Cu addition, although the differences were not statistically significant. Phospholipid fatty acid (PLFA) analysis revealed a reduction in microbial biomass at the highest Cu dose, whereas LDPE alone showed limited effects and may reduce Cu bioavailability. These results suggest that even at the highest concentration added, Cu can act as a plant nutrient, while the combination of Cu–plastics showed varying effects on plant growth and soil microbial communities. Full article

(This article belongs to the Special Issue Impacts of Emerging Agricultural Pollutants on Environmental Health)

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25 pages, 2515 KiB

Open AccessArticle

Solar Agro Savior: Smart Agricultural Monitoring Using Drones and Deep Learning Techniques

by Manu Mundappat Ramachandran, Bisni Fahad Mon, Mohammad Hayajneh, Najah Abu Ali and Elarbi Badidi

Agriculture 2025, 15(15), 1656; https://doi.org/10.3390/agriculture15151656 - 1 Aug 2025

Abstract

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the [...] Read more.

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

(This article belongs to the Section Agricultural Technology)

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23 pages, 7315 KiB

Open AccessArticle

Nonlinear Narrowband Active Noise Control for Tractors Based on a Momentum-Enhanced Volterra Filter

by Tao Zhang, Zhixuan Guan, Shuai Zhang, Kai Song and Boyan Huang

Agriculture 2025, 15(15), 1655; https://doi.org/10.3390/agriculture15151655 - 1 Aug 2025

Abstract

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise [...] Read more.

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 3489 KiB

Open AccessArticle

Impact of Nitrogen Fertilisation and Inoculation on Soybean Nodulation, Nitrogen Status, and Yield in a Central European Climate

by Waldemar Helios, Magdalena Serafin-Andrzejewska, Marcin Kozak and Sylwia Lewandowska

Agriculture 2025, 15(15), 1654; https://doi.org/10.3390/agriculture15151654 - 1 Aug 2025

Abstract

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate [...] Read more.

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate climates, where variable weather conditions can significantly affect nodulation and yield. This study evaluated the effects of three nitrogen fertilisation doses (0, 30, and 60 kg N·ha⁻¹), applied in the form of ammonium nitrate (34% N) and two commercial rhizobial inoculants—HiStick Soy (containing Bradyrhizobium japonicum strain 532C) and Nitragina (including a Polish strain of B. japonicum)—on nodulation, nitrogen uptake, and seed yield. A three-year field experiment (2017–2019) was conducted in southwestern Poland using a two-factor randomized complete block design. Nodulation varied significantly across years, with the highest values recorded under favourable early-season moisture and reduced during drought. In the first year, inoculation with HiStick Soy significantly increased nodule number and seed yield compared to Nitragina and the uninoculated control. Nitrogen fertilisation consistently improved seed yield, although it had no significant effect on nodulation. The highest nitrogen use efficiency was observed with moderate nitrogen input (30 kg N·ha⁻¹) combined with inoculation. These findings highlight the importance of integrating effective rhizobial inoculants with optimized nitrogen fertilisation to improve soybean productivity and nitrogen efficiency under variable temperate climate conditions. Full article

(This article belongs to the Special Issue Strategies to Enhance Nutrient Use Efficiency and Crop Nutrition)

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24 pages, 10190 KiB

Open AccessArticle

MSMT-RTDETR: A Multi-Scale Model for Detecting Maize Tassels in UAV Images with Complex Field Backgrounds

by Zhenbin Zhu, Zhankai Gao, Jiajun Zhuang, Dongchen Huang, Guogang Huang, Hansheng Wang, Jiawei Pei, Jingjing Zheng and Changyu Liu

Agriculture 2025, 15(15), 1653; https://doi.org/10.3390/agriculture15151653 - 31 Jul 2025

Abstract

Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision [...] Read more.

Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP₅₀ for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation. Full article

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

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