Agriculture

5 pages, 156 KiB

Open AccessEditorial

Role of Agriculture in Implementing the Concept of Sustainable Food System

by Anna Kocira and Mariola Staniak

Agriculture 2025, 15(10), 1041; https://doi.org/10.3390/agriculture15101041 (registering DOI) - 12 May 2025

Abstract

Nowadays, agriculture faces problems that threaten its basic function, i [...] Full article

(This article belongs to the Special Issue Role of Agriculture in Implementing Concept of Sustainable Food System)

19 pages, 13155 KiB

Open AccessArticle

An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province

by Yaxing Ma, Yushuo Tan, Wenbin Zhang, Zhipeng Yin, Chunlin Zhao, Panpan Guo, Haijian Wu and Ding Hu

Agriculture 2025, 15(10), 1040; https://doi.org/10.3390/agriculture15101040 (registering DOI) - 11 May 2025

Abstract

The content of the watercore in apples plays a decisive role in their taste and selling price, but there is a lack of methods to accurately assess it. Therefore, this paper proposes an OCRNet-based method for apple watercore content evaluation. A total of [...] Read more.

The content of the watercore in apples plays a decisive role in their taste and selling price, but there is a lack of methods to accurately assess it. Therefore, this paper proposes an OCRNet-based method for apple watercore content evaluation. A total of 720 watercores of apples from Mengzi, Lijiang, and Zhaotong City in Yunnan Province were used as experimental samples. An appropriate watercore extraction model was selected based on different evaluation indicators. The watercore feature images extracted using the optimal model were stacked, and the watercore content of apples in different regions was evaluated by calculating the fitted area of the stacked watercore region. The results show that the OCRNet model is optimal in all evaluation metrics when facing different datasets. The error of OCRNet is also minimized when extracting overexposed as well as underexposed images with 0.15% and 0.38%, respectively, and it can be used to extract the characteristics of the apple watercore. The evaluation result of the watercore content of apples in different regions is that Lijiang apples have the highest watercore content, followed by Mengzi apples, and Zhaotong apples have the least watercore content. Full article

(This article belongs to the Section Digital Agriculture)

23 pages, 4720 KiB

Open AccessArticle

Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models

by Jinfeng Guo, Dong Cui, Jinxing Guo, Umut Hasan, Fengqi Lv and Zixing Li

Agriculture 2025, 15(10), 1039; https://doi.org/10.3390/agriculture15101039 (registering DOI) - 11 May 2025

Abstract

Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll [...] Read more.

Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll content (LCC) is beneficial for precise management in tea plantations. In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. The results indicate that derivative processing enhances the sensitivity of hyperspectral data to tea LCC; furthermore, compared with individual machine learning models, the stacking models demonstrate superior predictive accuracy and generalization ability. Among the 17 constructed stacking configurations, when the meta-model is fixed, the predictive performance of the stacking model improves continuously with an increase in the number and accuracy of the base models and with a decrease in the structural similarity among the selected base models. Therefore, when constructing stacking models, the base model combination should comprise various models with minimal structural similarity while ensuring robust predictive performance, and the meta-model should be chosen as a simple linear or nonlinear model. Full article

(This article belongs to the Section Digital Agriculture)

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13 pages, 892 KiB

Open AccessArticle

Optimized Water Management Strategies: Evaluating Limited-Irrigation Effects on Spring Wheat Productivity and Grain Nutritional Composition in Arid Agroecosystems

by Zhiwei Zhao, Qi Li, Fan Xia, Peng Zhang, Shuiyuan Hao, Shijun Sun, Chao Cui and Yongping Zhang

Agriculture 2025, 15(10), 1038; https://doi.org/10.3390/agriculture15101038 (registering DOI) - 11 May 2025

Abstract

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the [...] Read more.

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article

(This article belongs to the Section Agricultural Water Management)

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20 pages, 8715 KiB

Open AccessArticle

Experimental Study of Liquid Jet Atomization and Penetration in Subsonic Crossflows

by Minmin Wu, Shiqun Dai, Rui Ye, Mingxiong Ou, Guanqun Wang, Chao Hu, Xurui Fan and Weidong Jia

Agriculture 2025, 15(10), 1037; https://doi.org/10.3390/agriculture15101037 (registering DOI) - 11 May 2025

Abstract

This study experimentally investigates the breakup mechanisms and atomization characteristics of liquid jets in subsonic crossflows and develops a penetration depth model that incorporates the incidence angle. Experimental data show that the model fits well, with a minimum R² value of 0.924 [...] Read more.

This study experimentally investigates the breakup mechanisms and atomization characteristics of liquid jets in subsonic crossflows and develops a penetration depth model that incorporates the incidence angle. Experimental data show that the model fits well, with a minimum R² value of 0.924 and an average of 0.969. High-speed imaging techniques were used to systematically analyze the effects of liquid- and gas-phase Weber numbers and incidence angles on the penetration and atomization of liquid jets. The experimental results indicate the following: (1) As the liquid Weber number (We_l) increases, the penetration depth increases, while the gas Weber number (We_a) is inversely related to penetration depth. (2) A decrease in the incidence angle (ranging from 45° to 90°) significantly reduces penetration performance. (3) As We_a increases, the volume median diameter (VMD) of droplets decreases by 61.70% to 83.09%, while smaller incidence angles cause a 42.96% increase in the VMD. The VMD shows a non-linear trend with respect to We_l, reflecting the complex interaction between inertial forces and surface tension. These findings provide a theoretical basis for understanding the atomization behavior of transverse jets and the key parameters affecting penetration and droplet formation. The results are of practical significance for the structural optimization and performance enhancement of air-assisted atomizing nozzles used in precision agricultural spraying systems. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 6871 KiB

Open AccessArticle

Determining the Vibration Parameters for Coffee Harvesting Through the Vibration of Fruit-Bearing Branches: Field Trials and Validation

by Shengwu Zhou, Yingjie Yu, Wei Su, Hedong Wang, Bo Yuan and Yu Que

Agriculture 2025, 15(10), 1036; https://doi.org/10.3390/agriculture15101036 (registering DOI) - 11 May 2025

Abstract

In order to explore the optimal vibration parameters for the selective harvesting of coffee fruits, a high-velocity dynamic photography monitoring system was developed to analyze the vibration-assisted harvesting process. This system identified the optimal vibration position on coffee branches and facilitated theoretical energy [...] Read more.

In order to explore the optimal vibration parameters for the selective harvesting of coffee fruits, a high-velocity dynamic photography monitoring system was developed to analyze the vibration-assisted harvesting process. This system identified the optimal vibration position on coffee branches and facilitated theoretical energy transfer analysis, obtaining a mathematical formula for calculating the total kinetic energy of coffee branches. A single-factor experiment was conducted with the vibration position as the experimental factor and the total kinetic energy of coffee branches as the response variable. The results showed that the total kinetic energy of the branches was the highest at Vibration Position 2 (the position between the third and the fourth Y-shaped bud tips on the branch). Therefore, Vibration Position 2 was determined as the optimal vibration position. Further analysis established a mathematical model linking coffee cherry motion parameters to theoretical detachment force. A factorial experiment was conducted with vibration frequency and amplitude as test factors, using detachment rates of green, semi-ripe, and ripe cherries as indicators. The results showed that at 55 Hz and 10.10 mm amplitude, the detachment rate of ripe cherries was highest (83.33%), while green and semi-ripe cherries detached at 16.67% and 33.33%, respectively. A field validation experiment, with Vibration Position 2, 55 Hz frequency, 10.10 mm amplitude, and 1 s vibration duration, yielded actual detachment rates of 15.86%, 35.17%, and 89.50% for green, semi-ripe, and ripe cherries, respectively. The error margins compared with the theoretical values were all below 10%. These results confirm the feasibility of optimizing vibration harvesting parameters through high-velocity photography dynamic analysis. Full article

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

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17 pages, 4974 KiB

Open AccessArticle

Research on Grain Temperature Detection Based on Rational Sound-Source Signal

by Hongyi Ge, Bo Feng, Yuying Jiang, Yuan Zhang, Chengxin Cai, Chunyan Guo, Heng Wang, Ziyu Liu and Xinxin Liu

Agriculture 2025, 15(10), 1035; https://doi.org/10.3390/agriculture15101035 (registering DOI) - 11 May 2025

Abstract

The selection of sound-source signals is a pivotal aspect of temperature measurement in stored grain using the acoustic method, as their characteristics directly influence the propagation effects of sound waves in grain media and the accuracy of temperature measurement. To identify a sound-source [...] Read more.

The selection of sound-source signals is a pivotal aspect of temperature measurement in stored grain using the acoustic method, as their characteristics directly influence the propagation effects of sound waves in grain media and the accuracy of temperature measurement. To identify a sound-source signal with optimal propagation performance, this study focused on analyzing the signal attenuation levels of typical sound sources, including simulated pulse signals and linear swept signals, during propagation. The results demonstrated that the linear swept signal exhibited superior propagation characteristics in grain media, with significantly lower signal attenuation compared to other sound-source signals. Specifically, a linear swept signal with a duration of 0.5 s and a frequency range of 200 Hz to 1000 Hz showed the best propagation performance. Finally, based on this rational signal, the temperature of grain samples was measured, yielding a mean absolute error of 1.62 °C. Full article

(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition)

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16 pages, 2958 KiB

Open AccessArticle

Simulation and Optimization of Double-Season Rice Yield in Jiangxi Province Based on High-Accuracy Surface Modeling–Agricultural Production Systems sIMulator Model

by Meiqing Zhu, Yimeng Jiao, Chenchen Wu, Wenjiao Shi, Hongsheng Huang, Ying Zhang, Xiaomin Zhao, Xi Guo, Yongshou Zhang and Tianxiang Yue

Agriculture 2025, 15(10), 1034; https://doi.org/10.3390/agriculture15101034 (registering DOI) - 10 May 2025

Abstract

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the [...] Read more.

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm²), MAE decreased by 31.43% (670.92→460.03 kg/hm²), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm²), MAE reduced by 26.37% (718.05→528.72 kg/hm²), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry. Full article

(This article belongs to the Section Digital Agriculture)

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24 pages, 13314 KiB

Open AccessArticle

Real-Time Detection and Instance Segmentation Models for the Growth Stages of Pleurotus pulmonarius for Environmental Control in Mushroom Houses

by Can Wang, Xinhui Wu, Zhaoquan Wang, Han Shao, Dapeng Ye and Xiangzeng Kong

Agriculture 2025, 15(10), 1033; https://doi.org/10.3390/agriculture15101033 (registering DOI) - 10 May 2025

Abstract

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces [...] Read more.

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of P. pulmonarius (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of P. pulmonarius and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of P. pulmonarius houses, offering a more accurate and efficient growth stage perception solution for environmental control. Full article

(This article belongs to the Section Digital Agriculture)

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27 pages, 14459 KiB

Open AccessReview

Disease Detection on Cocoa Crops Based on Computer-Vision Techniques: A Systematic Literature Review

by Joan Alvarado, Juan Felipe Restrepo-Arias, David Velásquez and Mikel Maiza

Agriculture 2025, 15(10), 1032; https://doi.org/10.3390/agriculture15101032 (registering DOI) - 10 May 2025

Abstract

Computer vision in the agriculture field aims to find solutions to guarantee and assure farmers the quality of their products. Therefore, studies to diagnose diseases and detect anomalies in crops, through computer vision, have been growing in recent years. However, crops such as [...] Read more.

Computer vision in the agriculture field aims to find solutions to guarantee and assure farmers the quality of their products. Therefore, studies to diagnose diseases and detect anomalies in crops, through computer vision, have been growing in recent years. However, crops such as cocoa required further attention to drive advances in computer vision to the detection of diseases. As a result, this paper aims to explore the computer vision methods used to diagnose diseases in crops, especially in cocoa. Therefore, the purpose of this paper is to provide answers to the following research questions: (Q1) What are the diseases affecting cocoa crop production? (Q2) What are the main Machine Learning algorithms and techniques used to detect and classify diseases in cocoa? (Q3) What are the types of imaging technologies (e.g., RGB, hyperspectral, or multispectral cameras) commonly used in these applications? (Q4) What are the main Machine Learning algorithms used in mobile applications and other platforms for cocoa disease detection? This paper carries out a Systematic Literature Review approach. The Scopus Digital, Science Direct Digital, Springer Link, and IEEE Explore databases were explored from January 2019 to August 2024. These questions have identified the main diseases that affect cocoa crops and their production. From this, it was identified that mostly Machine Learning algorithms based on computer vision are employed to detect anomalies in cocoa. In addition, the main sensors were explored, such as RGB and hyperspectral cameras, used for the creation of datasets and as a tool to diagnose or detect diseases. Finally, this paper allowed us to explore a Machine Learning algorithm to detect disease deployed in mobile and Internet of Things applications for detecting diseases in cocoa crops. Full article

(This article belongs to the Section Digital Agriculture)

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21 pages, 1515 KiB

Open AccessArticle

Design and Experiment of Dual Flexible Air Duct Spraying Device for Orchards

by Zhu Zhang, Dongxuan Wang, Jianping Li, Peng Wang, Yuankai Guo and Sibo Tian

Agriculture 2025, 15(10), 1031; https://doi.org/10.3390/agriculture15101031 (registering DOI) - 9 May 2025

Abstract

To address uneven airflow distribution and pesticide deposition coverage in orchard pesticide application, we developed a double-flexible duct spraying device. Utilizing FLUENT 2022 software for airflow field simulation, we analyzed various structural parameters to identify optimal configurations for the air duct type, diameter, [...] Read more.

To address uneven airflow distribution and pesticide deposition coverage in orchard pesticide application, we developed a double-flexible duct spraying device. Utilizing FLUENT 2022 software for airflow field simulation, we analyzed various structural parameters to identify optimal configurations for the air duct type, diameter, and nozzle outlet diameter. The results indicated that the nozzle outlet diameter most significantly influences wind field uniformity, followed by the air duct diameter and type. The optimal settings were identified as follows: C-Type air duct, 100 mm duct diameter, and 50 mm nozzle outlet diameter. Validation tests confirmed these settings, with simulated and actual wind speed measurements, showing no more than a 10% relative error, affirming the simulation’s accuracy. Field tests demonstrated an average droplet density of 35.38 droplets/cm² within tree canopies, indicating strong penetration ability. Droplet distribution followed a lower > middle > upper pattern in the canopy’s vertical direction, fulfilling technical requirements for high spindle-shaped fruit trees and providing a foundation for achieving a uniform canopy coverage. Full article

(This article belongs to the Section Agricultural Technology)

18 pages, 5861 KiB

Open AccessArticle

Unveiling the Transformative Effects of Forest Restoration on the Soil Chemistry and Biology of Sandy Soils in Southern Nyírség, Hungary

by István Attila Kocsis, Magdolna Tállai, Ágnes Zsuposné Oláh, Zoltán László, Béla Mokos, Ida Kincses, Evelin Kármen Juhász, Daniel A. Lowy and Zsolt Sándor

Agriculture 2025, 15(10), 1030; https://doi.org/10.3390/agriculture15101030 - 9 May 2025

Abstract

Protecting humankind’s natural resources and soils, including forestry, represents a top priority in agriculture. Addressing climate change should prioritize preserving and enhancing organic carbon, specifically humus, in soils. In this paper, we examine the impact of soil preparation on soil humus and microbial [...] Read more.

Protecting humankind’s natural resources and soils, including forestry, represents a top priority in agriculture. Addressing climate change should prioritize preserving and enhancing organic carbon, specifically humus, in soils. In this paper, we examine the impact of soil preparation on soil humus and microbial life during the reforestation of Southern Nyírség, Hungary. We determined soil plasticity, pH in distilled water solution, the quantity and quality of humus content, the total number of bacteria and microbial fungi, as well as CO₂ production. In addition to stump removal and plowing, the wealthiest layer of organic matter was detached from the surface. A significant decrease in humus content (HU%) was observed at the five experimental sites (loss of 19.20-40.14 HU% at 0–30 cm depth). Soil organic matter is concentrated in the stump depositions. According to the results, the quantity of humus content is strongly correlated with the measured parameters of soil life, specifically with the number of microbial fungi (r = 0.806 **) and the total number of bacteria (r = 0.648 **). Another correlation (r = 0.607 **) was assessed between the humus content and CO₂ production. This study helps to understand the importance of the no-tillage methods used in reforestation. Full article

(This article belongs to the Special Issue New Challenges and Trends in Agri-Environmental Management: Accomplishment of Sustainable Development Goals)

18 pages, 1111 KiB

Open AccessArticle

Design and Exploitation of a Dual-Channel Direct Injection System

by Xiang Dong, Ziyu Li, Mingxiong Ou and Weidong Jia

Agriculture 2025, 15(10), 1029; https://doi.org/10.3390/agriculture15101029 - 9 May 2025

Abstract

Soybean–maize intercropping is a traditional yet high-yield cultivation model that faces technical challenges in weed management due to the different herbicide requirements of soybean and maize. This study presents the design and experiments of the innovative dual-herbicide direct injection system, which can simultaneously [...] Read more.

Soybean–maize intercropping is a traditional yet high-yield cultivation model that faces technical challenges in weed management due to the different herbicide requirements of soybean and maize. This study presents the design and experiments of the innovative dual-herbicide direct injection system, which can simultaneously deliver glyphosate and fomesafen through real-time concentration modulation. The system operates by measuring the relationship between the mixing ratio and the conductivity value, mathematical model, and control algorithm. Experimental validation demonstrated that the correlation coefficient of herbicide mixing ratios and measured conductivity values across pressure ranges of 0.1–0.3 MPa are greater than 0.98, which means that measuring the mixing ratio using conductivity is reliable. Optimal operational performance was achieved at 0.2 MPa spraying pressure, characterized by superior mixing uniformity (CV < 5%) and system stability. This technological advancement provides a practical solution for precision agrochemical application in complex cropping models, with potential applications extending to other crop combinations requiring differential herbicide treatments. Full article

(This article belongs to the Section Agricultural Technology)

20 pages, 132302 KiB

Open AccessArticle

YOLO-MSNet: Real-Time Detection Algorithm for Pomegranate Fruit Improved by YOLOv11n

by Liang Xu, Bing Li, Xue Fu, Zhe Lu, Zelong Li, Bai Jiang and Siye Jia

Agriculture 2025, 15(10), 1028; https://doi.org/10.3390/agriculture15101028 - 9 May 2025

Abstract

In complex orchard environments, rapidly and accurately identifying pomegranate fruits at various growth stages remains a significant challenge. Therefore, we propose YOLO-MSNet, a lightweight and enhanced pomegranate fruit detection model developed using YOLOv11. Firstly, the C3k2_UIB module is elegantly designed by integrating the [...] Read more.

In complex orchard environments, rapidly and accurately identifying pomegranate fruits at various growth stages remains a significant challenge. Therefore, we propose YOLO-MSNet, a lightweight and enhanced pomegranate fruit detection model developed using YOLOv11. Firstly, the C3k2_UIB module is elegantly designed by integrating the Universal Inverted Bottleneck (UIB) structure into the model, while convolutional modules within the model are seamlessly replaced by AKConv units, thereby markedly reducing the overall complexity of the model. Subsequently, a novel parallel cascaded attention module called SSAM is designed as a way to improve the model’s ability to clearly see small details of the fruit against the background of a complex orchard. Additionally, a Dynamic Adaptive Bidirectional Feature Pyramid Network (DA-BiFPN) that employs adaptive sampling strategies to optimize multi-scale feature fusion is designed. The C3k2_UIB module complements this by reinforcing feature interactions and information aggregation across various scales, thereby enhancing the model’s perception of multi-scale objects. Furthermore, integrating VFLoss and ShapeIOU further refines the model’s ability to distinguish between overlapping and differently sized targets. Finally, comparative evaluations conducted on a publicly available pomegranate fruit dataset against state-of-the-art models demonstrate that YOLO-MSNet achieves a 1.7% increase in mAP50, a 21.5% reduction in parameter count, and a 21.8% decrease in model size. Further comparisons with mainstream YOLO models confirm that YOLO-MSNet has a superior detection accuracy despite being significantly lighter, making it especially suitable for deployment in resource-constrained edge devices, effectively addressing real-world requirements for fruit detection in complex orchard environments. Full article

(This article belongs to the Section Digital Agriculture)

15 pages, 7207 KiB

Open AccessArticle

Comparative Analysis of Air Quality in Agricultural and Urban Areas in Korea

by Jeong-Deok Baek, Hung-Soo Joo, Sung-Hyun Bae, Byung-Wook Oh, Min-Wook Kim and Jin-Ho Kim

Agriculture 2025, 15(10), 1027; https://doi.org/10.3390/agriculture15101027 - 9 May 2025

Abstract

Air pollution monitoring in Korea has not yet been implemented in agricultural areas. Documenting air quality in purely agricultural areas is inherently valuable. This study compares agricultural air quality with urban air quality during two periods: (1) the entire measurement period and (2) [...] Read more.

Air pollution monitoring in Korea has not yet been implemented in agricultural areas. Documenting air quality in purely agricultural areas is inherently valuable. This study compares agricultural air quality with urban air quality during two periods: (1) the entire measurement period and (2) high-PM episodes. To ensure broad spatial coverage, eight monitoring stations were installed in Yeoju, Nonsan, Naju, Gimhae, Hongcheon, Danyang, Muan, and Sangju. Real-time measurements of PM₁₀, PM_2.5, SO₂, and NOx were conducted continuously from March 2023 to December 2024. Over the entire measurement period, PM concentrations were similar in both agricultural and urban areas, but gaseous pollutants were lower in agricultural areas. PM levels were higher in agricultural areas during summer, whereas urban areas showed higher concentrations in other seasons. During high-PM episodes (29 days), all pollutants were significantly higher in urban areas, with PM_2.5 showing a greater difference than PM₁₀. Diurnal variations revealed that PM₁₀, PM_2.5, and NO₂ peaked in the morning and reached their lowest levels around 3 PM, with urban levels consistently higher than those in agricultural areas. SO₂ showed a different pattern, reaching its lowest concentration at 6 AM and peaking at noon in urban areas and at 6 PM in agricultural areas. This pattern closely followed temperature and wind speed variations. Full article

(This article belongs to the Topic Environmental Pollution in Modern Agriculture: Causes, Effect, and Control)

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19 pages, 2518 KiB

Open AccessArticle

Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion

by Jiaqi Duan, Hong Wang, Yuhang Yang, Mingwang Cheng and Dan Li

Agriculture 2025, 15(10), 1026; https://doi.org/10.3390/agriculture15101026 - 9 May 2025

Abstract

Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles [...] Read more.

Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles (UAVs) to obtain images, whereby the spectral information is utilized to estimate rice growth parameters. Considering the cost of multispectral sensors and factors influencing rice growth parameters, this study integrated satellite remote sensing images with UAV visible-light images to obtain high-resolution multispectral images during key rice growth stages, thereby determining the rice LAI and SPAD on the same day. The vegetation indices and textural features most correlated with rice LAI and SPAD were selected using Pearson correlation analysis, and based on vegetation indices, textural features, and their combinations, regression models were established. The results indicate the following: (1) The fusion of satellite and UAV images, combined with spectral information and textural features, can significantly improve the estimation accuracy of LAI and SPAD compared to using only spectral information or textural features. (2) Sparrow search algorithm-optimized extreme gradient boosting (SSA-XGBoost) regression achieved the highest accuracy, with R² and RMSE of 0.904 and 0.183 in LAI estimation and 0.857 and 0.882 in SPAD estimation, respectively. This demonstrates that integrating satellite and UAV images, combined with vegetation indices and texture features, can effectively establish rice LAI and SPAD estimation models, using the SSA-optimized XGBoost method, as an effective and feasible solution for precise monitoring of rice growth parameters. Full article

(This article belongs to the Section Digital Agriculture)

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18 pages, 1037 KiB

Open AccessArticle

Modeling of the Physicochemical and Nutritional Composition of Musa paradisiaca (Williams Variety) at Different Ripening Stages in Ecuador

by María Fernanda Garcés-Moncayo, Fabricio Guevara-Viejó, Juan Diego Valenzuela-Cobos, Purificación Galindo-Villardón and Purificación Vicente-Galindo

Agriculture 2025, 15(10), 1025; https://doi.org/10.3390/agriculture15101025 - 9 May 2025

Abstract

The banana (Musa paradisiaca AAA) is a tropical fruit native to Southeast Asia, widely cultivated in over 130 tropical and subtropical countries. It plays a vital role in both rural and urban diets and serves as a key economic resource in producing [...] Read more.

The banana (Musa paradisiaca AAA) is a tropical fruit native to Southeast Asia, widely cultivated in over 130 tropical and subtropical countries. It plays a vital role in both rural and urban diets and serves as a key economic resource in producing regions. This study examined how different ripening stages of bananas (Musa paradisiaca var. Williams) affect their physicochemical properties and nutritional composition. The bananas underwent a controlled ripening process and were classified into eight stages based on pericarp color, ranging from dark green (P1) to yellow with pronounced brown spots (P8). The results showed significant changes during ripening: pH decreased from 5.48 to 4.95, soluble solids (SS) increased from 15.2% to 21.73%, total starch (TS) decreased from 76.15% to 33.92%, and free sugars (FS) increased from 19.78 mg/g to 361.85 mg/g. Vitamin C content rose from 281.4 µg/g to 354.14 µg/g, while oxalic acid and tannins decreased significantly, improving palatability. Statistical analysis using PERMANOVA confirmed significant differences between ripening stages in the evaluated properties (p < 0.001), explaining more than 75% of the observed variability. The HJ-Biplot analysis illustrated the relationships between ripening stages and variables, showing that early stages were correlated with higher starch and acidic compound content, while later stages were associated with increased sugar levels and vitamin C content. These findings demonstrate that ripening stages significantly influence the composition of bananas, providing essential information for optimizing agricultural, industrial, and commercial practices to enhance their nutritional value and meet the demands of consumers seeking healthy foods. Full article

(This article belongs to the Special Issue Analysis of Agricultural Food Physicochemical and Sensory Properties)

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31 pages, 12568 KiB

Open AccessArticle

How to Better Use Canopy Height in Soybean Biomass Estimation

by Yanqin Zhu, Fan Fan, Zhen Zhang, Xun Yu, Tiantian Jiang, Liming Li, Yadong Liu, Yali Bai, Ziqian Tang, Shuaibing Liu, Dameng Yin and Xiuliang Jin

Agriculture 2025, 15(10), 1024; https://doi.org/10.3390/agriculture15101024 - 9 May 2025

Abstract

Soybean, a globally important food and oil crop, requires accurate estimation of above-ground biomass (AGB) to optimize management and prevent yield loss. Despite the availability of various remote sensing methods, systematic research on effectively integrating canopy height (CH) and spectral information for improved [...] Read more.

Soybean, a globally important food and oil crop, requires accurate estimation of above-ground biomass (AGB) to optimize management and prevent yield loss. Despite the availability of various remote sensing methods, systematic research on effectively integrating canopy height (CH) and spectral information for improved AGB estimation remains insufficient. This study addresses this gap using drone data. Three CH utilization approaches were tested: (1) simple combination of CH and spectral vegetation indices (VIs), (2) fusion of CH and VI, and (3) integration of CH, VI, and growing-degree days (GDDs). The results indicate that adding CH always enhances AGB estimation which is based only on VIs, with the fusion approach outperforming simple combination. Incorporating GDD further improved AGB estimation for highly accurate CH data, with the best model achieving a root mean square error (RMSE) of 87.52 ± 5.88 g/m² and a mean relative error (MRE) of 28.59 ± 1.99%. However, for the multispectral data with low CH accuracy, the VIs + GDD fusion (RMSE = 92.94 ± 6.84 g/m², MRE = 30.08 ± 2.29%) surpassed CH + VIs + GDD (RMSE = 97.99 ± 6.71 g/m², MRE = 31.41 ± 2.56%). The findings highlight the role of CH accuracy in AGB estimation and validate the value of growth-stage information in robust modeling. Future research should prioritize the refining of CH prediction and the optimization of composite variable construction to promote the application of this approach in agricultural monitoring. Full article

(This article belongs to the Section Digital Agriculture)

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23 pages, 6603 KiB

Open AccessArticle

Detection of Aflatoxin B1 in Maize Silage Based on Hyperspectral Imaging Technology

by Lina Guo, Haiqing Tian, Daqian Wan, Yang Yu, Kai Zhao, Xinglu Zheng, Haijun Li and Jianying Sun

Agriculture 2025, 15(10), 1023; https://doi.org/10.3390/agriculture15101023 - 9 May 2025

Abstract

Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative prediction method based on hyperspectral imaging technology. Using [...] Read more.

Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative prediction method based on hyperspectral imaging technology. Using the full-spectrum bands after SG, SNV, MSC, FD, SD, and SNV + FD, MSC + FD, SNV + SD, MSC + SD preprocessing, the characteristic wavelengths selected by CARS, BOSS, and RF feature selection methods, and the augmented bands generated by Mixup data augmentation as input features, three models were developed for AFB1 content prediction: a linear WPLSR_SD_Mixup_QPE model, a nonlinear SVR_SD_Mixup_PCA model, and a deep learning CNN_SD_Mixup_WMSE_SA model. The results demonstrated that SD preprocessing was the most suitable for AFB1 detection in maize silage, and the Mixup data augmentation method effectively improved model performance. Among the models, SVR_SD_Mixup_PCA achieved the best performance, with an

R_{p}^{2}

of 0.9458, RMSEP of 3.1259 μg/kg, and RPD of 4.2969, indicating high prediction accuracy and generalization capability. This study fills the gap of hyperspectral image technology fused with artificial intelligence algorithm in the application of quantitative detection of AFB1 content in maize silage and provides a new technical method and theoretical basis for nondestructive testing of corn silage feed. Full article

(This article belongs to the Section Digital Agriculture)

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