Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q2 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.6 (2022)
Latest Articles
Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field
Agriculture 2024, 14(5), 784; https://doi.org/10.3390/agriculture14050784 (registering DOI) - 19 May 2024
Abstract
Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper
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Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.
Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
Open AccessArticle
Research on Rapeseed Seedling Counting Based on an Improved Density Estimation Method
by
Qi Wang, Chunpeng Li, Lili Huang, Liqing Chen, Quan Zheng and Lichao Liu
Agriculture 2024, 14(5), 783; https://doi.org/10.3390/agriculture14050783 (registering DOI) - 19 May 2024
Abstract
The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in
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The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessArticle
Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products
by
Nisa Sansel Tandogan Aktepe and İhsan Erdem Kayral
Agriculture 2024, 14(5), 782; https://doi.org/10.3390/agriculture14050782 (registering DOI) - 19 May 2024
Abstract
This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The
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This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively.
Full article
(This article belongs to the Special Issue Globalisation, Regionalisation, Market Integration and Price Analysis of Agricultural Products)
Open AccessArticle
Coupling Coordination between Agricultural Eco-Efficiency and Urbanization in China Considering Food Security
by
Xiuli He and Wenxin Liu
Agriculture 2024, 14(5), 781; https://doi.org/10.3390/agriculture14050781 (registering DOI) - 18 May 2024
Abstract
When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000
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When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000 to 2020. Based on the concept of agricultural eco-efficiency, an evaluation index system was developed to include undesirable outputs (carbon emissions), and agricultural eco-efficiency scores were calculated using the SBM–DEA model. An urbanization evaluation index system, covering six dimensions and twelve indexes, was constructed. A comprehensive index of urbanization is measured using the entropy method. On this basis, a coupling coordination model was applied to quantify the relationship between agricultural eco-efficiency and urbanization at the provincial scale in China. The results showed that the agricultural eco-efficiency of all provincial units in China exhibited an overall trend of improvement. Average efficiency followed a spatial pattern of majority grain-consuming areas > grain production–consumption balance areas > majority grain-producing areas. The level of coupling between agricultural eco-efficiency and urbanization is generally low. Currently, no regions have reached the stage of synergy or high-level coupling. Most regions are currently in an antagonistic stage with a coupling degree of 0.3 < C ≤ 0.5. The classification of coupling coordination levels changed from four levels of “severe imbalance”, “moderate imbalance”, “mild imbalance”, and “primary coordination” to “moderate imbalance”, “mild imbalance”, “primary coordination”, and “intermediate coordination”. The level of “severe imbalance” disappeared, the level of “intermediate coordination” appeared, and the level of “mild imbalance” became the largest scale level. From the perspective of food security, the proportion of grain production in the categories of “primary coordination” and “intermediate coordination” was less than 10%, and these provinces never achieved self-sufficiency in food production. The proportion of grain production at the “mild imbalance” level reached 62.4%, while the per capita grain production at the “moderate imbalance” level reached 846.7 kg. Provinces with lower levels of coupling coordination have stronger food security capabilities. It can be observed that the weaker the coupling coordination between agricultural eco-efficiency and urbanization, the higher the food self-sufficiency. Based on the research results above, we discussed strategies to enhance agricultural eco-efficiency in majority grain-producing regions by focusing on technological progress and technical efficiency. Additionally, we analyzed approaches to achieve grain self-sufficiency in regions characterized by a high level of coordination between agricultural eco-efficiency and urbanization, considering both production and trade dimensions.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Optimization and Prediction of Operational Parameters for Enhanced Efficiency of a Chickpea Peeling Machine
by
Khaled Abdeen Mousa Ali, Sheng Tao Li, Changyou Li, Elwan Ali Darwish, Han Wang, Taha Abdelfattah Mohammed Abdelwahab, Ahmed Elsayed Mahmoud Fodah and Youssef Fayez Elsaadawi
Agriculture 2024, 14(5), 780; https://doi.org/10.3390/agriculture14050780 (registering DOI) - 18 May 2024
Abstract
Chickpeas hold significant nutritional and cultural importance, being a rich source of protein, fiber, and essential vitamins and minerals. They are a staple ingredient in various cuisines worldwide. Peeling chickpeas is considered a crucial pre-consumption operation due to the undesirability of peels for
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Chickpeas hold significant nutritional and cultural importance, being a rich source of protein, fiber, and essential vitamins and minerals. They are a staple ingredient in various cuisines worldwide. Peeling chickpeas is considered a crucial pre-consumption operation due to the undesirability of peels for some uses. This study aimed to design, test, and evaluate a small chickpea seed peeling machine. The peeling prototype was designed in accordance with the chickpeas’ measured properties; the seeds’ moisture content was determined to be 6.96% (d.b.). The prototype was examined under four different levels of drum revolving speeds (100, 200, 300, and 400 rpm), and three different numbers of brush peeling rows. The prototype was tested with rotors of four, eight, and twelve rows of brushes. The evaluation of the chickpea peeling machine encompassed several parameters, including the machine’s throughput (kg/h), energy consumption (kW), broken seeds percentage (%), unpeeled seeds percentage (%), and peeling efficiency (%). The obtained results revealed that the peeling machine throughput (kg/h) exhibited an upward trend with increases in the rotation speed of the peeling drum. Meanwhile, the throughput decreased as the number of peeling brushes installed on the roller increased. The highest recorded productivity of 71.29 kg/h was achieved under the operational condition of 400 rpm and four peeling brush rows. At the same time, the peeling efficiency increased with the increase in both of peeling drum rotational speed and number of peeling brush rows. The highest peeling efficiency (97.2%) was recorded at the rotational speed of 400 rpm and twelve peeling brush rows. On the other hand, the lowest peeling efficiency (92.85%) was recorded at the lowest drum rotational speed (100 rpm) and number of peeling brush rows (4 rows). In the optimal operational condition, the machines achieved a throughput of 71.29 kg/h, resulting in a peeling cost of 0.001 USD per kilogram. This small-scale chickpea peeling machine is a suitable selection for small and medium producers.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessArticle
Research and Experiment on Airflow Field Control Technology of Harvester Cleaning System Based on Load Distribution
by
Duanxin Li, Qinghao He, Dong Yue, Duanyang Geng, Jianning Yin, Pengxuan Guan and Zehao Zha
Agriculture 2024, 14(5), 779; https://doi.org/10.3390/agriculture14050779 (registering DOI) - 18 May 2024
Abstract
The wind sieve cleaner is widely used in the screening system of combine harvesters due to its compact structure and efficient screening capability. In order to study more deeply the feeding load distribution of the combine harvester and the influence of the airflow
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The wind sieve cleaner is widely used in the screening system of combine harvesters due to its compact structure and efficient screening capability. In order to study more deeply the feeding load distribution of the combine harvester and the influence of the airflow field on the clearing effect, a mechanical analysis method was adopted to analyze the dynamics of the material in the inclined airflow, and a kinetic model was established. At the same time, the motion state of the material in the airflow field was explored, and combined with the actual orthogonal test, the response surface model of factors and indicators was established. Experimental validation was carried out. It provides an important research foundation and theoretical basis for optimizing the structural parameters of the screening system and improving its operational performance.
Full article
(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Dust and Bacterial Air Contamination in a Broiler House in Summer and Winter
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Ivica Ravić, Mario Ostović, Anamaria Ekert Kabalin, Matija Kovačić, Kristina Matković, Željko Gottstein and Danijela Horvatek Tomić
Agriculture 2024, 14(5), 778; https://doi.org/10.3390/agriculture14050778 (registering DOI) - 18 May 2024
Abstract
This study aimed to investigate dust and bacterial air contamination in a broiler house during different seasons. The study was carried out in commercial housing conditions during five weeks of the rearing cycle in summer and winter. The total dust concentration ranged from
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This study aimed to investigate dust and bacterial air contamination in a broiler house during different seasons. The study was carried out in commercial housing conditions during five weeks of the rearing cycle in summer and winter. The total dust concentration ranged from 1.90 to 4.50 mg/m3 in summer and from 2.80 to 5.10 mg/m3 in winter. The total bacterial count ranged from 2.85 × 104 to 1.03 × 105 CFU/m3 in summer and from 2.12 × 104 to 2.28 × 105 CFU/m3 in winter. The study results showed the dust concentration to be increased in winter as compared to summer, yielding a significant correlation (r = 0.602, p < 0.05) with a significantly higher airborne bacterial count in winter (p < 0.001). Furthermore, dust concentration showed significant correlations (p < 0.05) with air temperature (r = −0.418), relative humidity (r = 0.673), and broiler activity (r = 0.709), while bacterial count yielded significant correlations (p < 0.05) with air temperature (r = −0.756), relative humidity (r = 0.831), and airflow rate (r = 0.511). The results obtained in the study can prove useful in the field. Seasonal variability in dust and bacterial air contamination should be considered in the development of guidelines or standards of air quality in broiler housing and evaluation of the effectiveness of remedial strategies.
Full article
(This article belongs to the Special Issue The Influence of Environmental Factors on Farming Animals)
Open AccessArticle
Effects of Unbalanced Incentives on Threshing Drum Stability during Rice Threshing
by
Kexin Que, Zhong Tang, Ting Wang, Zhan Su and Zhao Ding
Agriculture 2024, 14(5), 777; https://doi.org/10.3390/agriculture14050777 - 17 May 2024
Abstract
As a result of the uneven growth of rice, unbalanced vibration of threshing drum caused by stalk entanglement in combine harvester is more and more severe. In order to reveal the influence of unbalanced excitation on the roller axis locus during rice threshing,
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As a result of the uneven growth of rice, unbalanced vibration of threshing drum caused by stalk entanglement in combine harvester is more and more severe. In order to reveal the influence of unbalanced excitation on the roller axis locus during rice threshing, the stability of threshing drum was studied. The dynamic signal test and analysis system are used to test the axial trajectory of threshing drum. At the same time, the influence of the unbalanced excitation caused by the axis winding on the axis trajectory is analyzed by the experimental results. Axis locus rules under no-load and threshing conditions are obtained. In order to simulate the axial and radial distribution of unbalanced excitation along the threshing drum, the counterweight was distributed on the threshing drum instead of the entangled stalk. Then, the definite effect of unbalanced excitation on the rotating stability of threshing drum is analyzed. Results show that the amplitude of stem winding along the grain drum is larger in the vertical direction and smaller in the horizontal direction when compared with the unloaded state under 200 g weight. It was found that the amplitude in both horizontal and vertical directions decreased after 400 g and 600 g counterweights were added, respectively, to simulate the radial distribution of stalk winding along the grain barrel. Finally, it can be seen that with the increase in the weight of the counterweight, the characteristics of the trajectory misalignment of the threshing cylinder axis become more and more obvious. This study can provide reference for reducing the unbalanced excitation signal of threshing drum and improving driving comfort.
Full article
(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Preliminary Results of the Impact of Beneficial Soil Microorganisms on Okra Plants and Their Polyphenol Components
by
Alaa Abdulkadhim A. Almuslimawi, Lívia László, Alhassani Leith Sahad, Ahmed Ibrahim Alrashid Yousif, György Turóczi and Katalin Posta
Agriculture 2024, 14(5), 776; https://doi.org/10.3390/agriculture14050776 - 17 May 2024
Abstract
Okra (Abelmoschus esculentus L.) is a highly nutritious vegetable rich in vitamins, minerals, and bioactive compounds, including polyphenols, offering numerous health benefits. Despite its nutritional value, okra remains underutilized in Europe; however, its cultivation and popularity may rise in the future with
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Okra (Abelmoschus esculentus L.) is a highly nutritious vegetable rich in vitamins, minerals, and bioactive compounds, including polyphenols, offering numerous health benefits. Despite its nutritional value, okra remains underutilized in Europe; however, its cultivation and popularity may rise in the future with increasing awareness of its advantages. In agricultural practices, beneficial soil microorganisms, such as arbuscular mycorrhizal fungi (AMF), Trichoderma spp., Streptomyces spp., and Aureobasidium spp., play crucial roles in promoting plant health, enhancing agricultural productivity together with improved crop nutritional value. This study aimed to investigate the effects of individual and combined inoculation on the polyphenol content of okra fruits, as analyzed by HPLC. Moreover, growth parameters and glutathione-S-transferase enzyme (GST) activities of okra leaves were also estimated. Tested microorganisms significantly increased the yield of okra plants except for A. pullulans strain DSM 14950 applied individually. All microorganisms led to increased GST enzyme activity of leaves, suggesting a general response to biotic impacts, with individual inoculation showing higher enzyme activity globally compared to combined treatments. According to the polyphenol compound analysis, the application of tested microorganisms held various but generally positive effects on it. Only the combined treatment of F. mosseae and Streptomyces strain K61 significantly increased the coumaric acid content, and the application of Aureobasidium strain DSM 14950 had a positive influence on the levels of quercetin and quercetin-3-diglucoside. Our preliminary results show how distinct polyphenolic compound contents can be selectively altered via precise inoculation with different beneficial microorganisms.
Full article
(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)
Open AccessArticle
The Influence and Mechanism of Digital Village Construction on the Urban–Rural Income Gap under the Goal of Common Prosperity
by
Muziyun Liu and Hui Liu
Agriculture 2024, 14(5), 775; https://doi.org/10.3390/agriculture14050775 - 17 May 2024
Abstract
Digital village construction is not only a vital component of the digital China strategy but also a crucial measure by which to realize common prosperity. This study theoretically elaborates the influence of digital village construction on the urban–rural income gap (URIG) and its
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Digital village construction is not only a vital component of the digital China strategy but also a crucial measure by which to realize common prosperity. This study theoretically elaborates the influence of digital village construction on the urban–rural income gap (URIG) and its mechanism and empirically tests it by using a panel fixed-effect model, a mediating-effect model, and a moderating-effect model based on the provincial data of major producing areas from 2011 to 2020. The results show that digital village construction can significantly narrow the URIG, and rural industry revitalization is a vital channel for digital village construction in driving the decline of the URIG. The construction of transportation infrastructure can significantly enhance the inhibition effect of digital village construction on the URIG. Moreover, there is a human capital threshold for the impact of digital village construction on the URIG; after crossing the threshold, digital village construction better suppresses the URIG. So, the government should increase the financial support and technical support for digital village construction, improving the rural production conditions and industrial development environment and establishing a rural digital talent cultivation mechanism so as to achieve the goal of common prosperity.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition
by
Junhong Zhao, Xingzhi Yao, Yu Wang, Zhenfeng Yi, Yuming Xie and Xingxing Zhou
Agriculture 2024, 14(5), 774; https://doi.org/10.3390/agriculture14050774 - 17 May 2024
Abstract
Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is
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Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.
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(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties
by
Hongjun Chen, Muhammad Awais, Linze Li, Wei Zhang, Mukhtar Iderawumi Abdulraheem, Yani Xiong, Vijaya Raghavan and Jiandong Hu
Agriculture 2024, 14(5), 773; https://doi.org/10.3390/agriculture14050773 - 17 May 2024
Abstract
Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient
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Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.
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(This article belongs to the Section Agricultural Soils)
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Open AccessArticle
A Cooperative Scheduling Based on Deep Reinforcement Learning for Multi-Agricultural Machines in Emergencies
by
Weicheng Pan, Jia Wang and Wenzhong Yang
Agriculture 2024, 14(5), 772; https://doi.org/10.3390/agriculture14050772 - 17 May 2024
Abstract
Effective scheduling of multiple agricultural machines in emergencies can reduce crop losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement learning for multi-agricultural machines with deadlines is designed to minimize makespan. With the asymmetric transfer paths among farmlands,
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Effective scheduling of multiple agricultural machines in emergencies can reduce crop losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement learning for multi-agricultural machines with deadlines is designed to minimize makespan. With the asymmetric transfer paths among farmlands, the problem of agricultural machinery scheduling under emergencies is modeled as an asymmetric multiple traveling salesman problem with time windows (AMTSPTW). With the popular encoder-decoder structure, heterogeneous feature fusion attention is designed in the encoder to integrate time windows and asymmetric transfer paths for more comprehensive and better feature extraction. Meanwhile, a path segmentation mask mechanism in the decoder is proposed to divide solutions efficiently by adding virtual depots to assign work to each agricultural machinery. Experimental results show that our proposal outperforms existing modified baselines for the studied problem. Especially, the measurements of computation ratio and makespan are improved by 26.7% and 21.9% on average, respectively. The computation time of our proposed strategy has a significant improvement over these comparisons. Meanwhile, our strategy has a better generalization for larger problems.
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(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition)
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Open AccessArticle
Genome-Wide Identification and Expression Analysis of the Broad-Complex, Tramtrack, and Bric- à-Brac Domain-Containing Protein Gene Family in Potato
by
Aiana, Anita Katwal, Hanny Chauhan, Santosh Kumar Upadhyay and Kashmir Singh
Agriculture 2024, 14(5), 771; https://doi.org/10.3390/agriculture14050771 - 16 May 2024
Abstract
The BTB (broad-complex, tramtrack, and bric-a-brac) domain, also known as the POZ (POX virus and zinc finger) domain, is a conserved protein–protein interaction domain present in various organisms. In this study, we conducted a genome-wide search to identify and characterize BTB genes in
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The BTB (broad-complex, tramtrack, and bric-a-brac) domain, also known as the POZ (POX virus and zinc finger) domain, is a conserved protein–protein interaction domain present in various organisms. In this study, we conducted a genome-wide search to identify and characterize BTB genes in Solanum tuberosum. A total of 57 StBTBs were identified and analyzed for their physicochemical properties, chromosomal distribution, gene structure, conserved motifs, phylogenetic relationships, tissue-specific expression patterns, and responses to hormonal and stress treatments. We found that StBTBs were unevenly distributed across potato chromosomes and exhibited diverse gene structures and conserved motifs. Tissue-specific expression analysis revealed differential expression patterns across various potato tissues, implying their roles in plant growth and development. Furthermore, differential expression analysis under hormonal and stress treatments indicated the involvement of StBTBs in abiotic and biotic stress responses and hormone signaling pathways. Protein–protein interaction analysis identified potential interactions with ribosomal proteins, suggesting roles in translational regulation. Additionally, microRNA target site analysis revealed regulatory relationships between StBTBs and miRNAs. Our study provides a comprehensive understanding of the StBTB gene family in potato, laying the groundwork for further functional characterization and manipulation of these genes to improve stress tolerance and agricultural productivity in potato and related plant species.
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(This article belongs to the Special Issue Abiotic Stress Responses in Horticultural Crops)
Open AccessArticle
Climate Change Risks for the Mediterranean Agri-Food Sector: The Case of Greece
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Elena Georgopoulou, Nikos Gakis, Dimitris Kapetanakis, Dimitris Voloudakis, Maria Markaki, Yannis Sarafidis, Dimitris P. Lalas, George P. Laliotis, Konstantina Akamati, Iosif Bizelis, Markos Daskalakis, Sevastianos Mirasgedis and Iordanis Tzamtzis
Agriculture 2024, 14(5), 770; https://doi.org/10.3390/agriculture14050770 - 16 May 2024
Abstract
The study assesses the direct effects of climate change by 2060, including extreme events, on the productivity of regional crop farming and livestock in Greece, and the broader socio-economic effects on the agri-food and other sectors. Different approaches (i.e., agronomic models, statistical regression
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The study assesses the direct effects of climate change by 2060, including extreme events, on the productivity of regional crop farming and livestock in Greece, and the broader socio-economic effects on the agri-food and other sectors. Different approaches (i.e., agronomic models, statistical regression models, and equations linking thermal stress to livestock output) were combined to estimate the effects on productivity from changes in the average values of climatic parameters, and subsequently the direct economic effects from this long-term climate change. Recorded damages from extreme events together with climatic thresholds per event and crop were combined to estimate the direct economic effects of these extremes. The broader socio-economic effects were then estimated through input–output analysis. Under average levels of future extreme events, the total direct economic losses for Greek agriculture due to climate change will be significant, from EUR 437 million/year to EUR 1 billion/year. These losses approximately double when indirect effects on other sectors using agricultural products as inputs (e.g., food and beverage, hotels, and restaurants) are considered, and escalate further under a tenfold impact of extreme events. Losses in the GDP and employment are moderate at the national level, but significant in regions where the contribution of agriculture is high.
Full article
(This article belongs to the Special Issue Mediterranean Agriculture under Climate Change)
Open AccessSystematic Review
Bacterial Endophytes and Their Contributions to Alleviating Drought and Salinity Stresses in Wheat: A Systematic Review of Physiological Mechanisms
by
Fayha Al-Hawamdeh, Jamal Y. Ayad, Kholoud M. Alananbeh and Muhanad W. Akash
Agriculture 2024, 14(5), 769; https://doi.org/10.3390/agriculture14050769 - 16 May 2024
Abstract
Drought and salinity stresses significantly threaten global wheat productivity, limiting growth and reducing yields, thus endangering food security worldwide. These stresses disrupt physiological processes, impair photosynthesis, and hinder optimal growth and yield by diminishing water uptake, causing osmotic stress, ion toxicity, and oxidative
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Drought and salinity stresses significantly threaten global wheat productivity, limiting growth and reducing yields, thus endangering food security worldwide. These stresses disrupt physiological processes, impair photosynthesis, and hinder optimal growth and yield by diminishing water uptake, causing osmotic stress, ion toxicity, and oxidative stress. In response, various mitigation strategies have been explored, including breeding for stress-tolerant cultivars, improved irrigation techniques, and the application of exogenous osmoprotectants and soil amendments. Among these strategies, the emergence of rhizospheric and endophytic growth-promoting microorganisms has attracted significant attention. Therefore, a systematic review was undertaken to illustrate the role of endophytic bacteria in enhancing wheat tolerance to drought and salinity stresses. This review analyzes physiological mechanisms and research trends, identifies gaps, and discusses implications for sustainable agriculture. An analysis of the literature related to endophytic bacteria in wheat was conducted using databases of major publishers from 2004 to 2023. The review explores their mechanisms, such as phytohormone production and stress-responsive gene induction, emphasizing their contribution to plant growth and stress resilience. The current research trends indicate a growing interest in utilizing endophytic bacteria to mitigate these stresses in wheat cultivation, with studies focusing on understanding their physiological responses and interactions with wheat plants. Future research should concentrate on elucidating the role of endophytic bacteria in enhancing host plant tolerance to multiple stressors, as well as aspects like endophytic mechanism of action , endophytic lifestyle, and transmission pathways. Overall, endophytic bacteria offer promising avenues for sustainable agricultural practices, aiding in crop resilience and food security amid environmental challenges.
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(This article belongs to the Special Issue The Role of Plant Growth-Promoting Bacteria in Crop Improvement)
Open AccessCommunication
The Effect of Combined Application of Biocontrol Microorganisms and Arbuscular Mycorrhizal Fungi on Plant Growth and Yield of Tomato (Solanum lycopersicum L.)
by
Alaa Abdulkadhim A. Almuslimawi, Borbála Kuchár, Susana Estefania Araujo Navas, György Turóczi and Katalin Posta
Agriculture 2024, 14(5), 768; https://doi.org/10.3390/agriculture14050768 (registering DOI) - 16 May 2024
Abstract
Sustainable plant production requires less use of synthetic chemicals in plant nutrition and protection. Microbial products are among the most promising substitutes for chemicals. With the increasing popularity and availability of such products, it has become obligatory to use different microbes together. The
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Sustainable plant production requires less use of synthetic chemicals in plant nutrition and protection. Microbial products are among the most promising substitutes for chemicals. With the increasing popularity and availability of such products, it has become obligatory to use different microbes together. The effect of this has been tested in several studies, but their results have sometimes been contradictory depending on the microbial strains tested and the mode of application. We tested the effect of two commercially available antagonists and Funneliformis mosseae alone and in combination on tomato. Mycorrhizal treatment increased plant growth and yield, both alone and combined with the antagonists; however, mycorrhizal root colonization was not influenced by the antagonist. This treatment also led to a slight decrease in the occurrence of Trichoderma spp. on tomato roots but did not impede the colonization of roots by the applied Trichoderma strain. Our result confirmed that Trichoderma asperellum (T34) and Streptomyces griseoviridis (K61) can be safely combined with arbuscular mycorrhizal fungi (AMF), namely with F. mosseae.
Full article
(This article belongs to the Special Issue Advanced Research of Rhizosphere Microbial Activity—Series II)
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Open AccessArticle
Little Brands, Big Profits? Effect of Agricultural Geographical Indicators on County-Level Economic Development in China
by
Zhuang Zhang, Qiuxia Yan, Hao Zheng, Mengqing Zeng and Youhua Chen
Agriculture 2024, 14(5), 767; https://doi.org/10.3390/agriculture14050767 - 16 May 2024
Abstract
AGIs (agricultural geographical indicators) are effective quality signals that can improve market welfare, but few studies have investigated the impact of AGIs on economic development. To fill this gap, this paper explores the impact of AGIs on per capita GDP and its mechanisms,
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AGIs (agricultural geographical indicators) are effective quality signals that can improve market welfare, but few studies have investigated the impact of AGIs on economic development. To fill this gap, this paper explores the impact of AGIs on per capita GDP and its mechanisms, according to country-level data in China from 2000 to 2018. For every additional AGI in the country, GDP per capita increased by 0.2–0.4%. Our conclusion remained reliable after various robustness tests. These effects were more salient in western areas, the main grain-producing areas, and settled areas. AGIs related to aquatic environments, animal husbandry, and planting products promoted economic development most significantly. For these effects, encouraging an increase in agricultural value (improving the quantity and quality of products) and promoting the agglomeration of populations, capital, and enterprises in the agricultural sector were the main mechanisms.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Effects of Soil Quality Decline on Soil-Dwelling Mesofaunal Communities in Agricultural Lands of the Mollisols Region, China
by
Chen Ma, Xin Yao and Guoming Du
Agriculture 2024, 14(5), 766; https://doi.org/10.3390/agriculture14050766 - 16 May 2024
Abstract
Soil quality decline can adversely affect ecosystem health and land productivity, with soil-dwelling mesofauna considered to potentially fulfill vital functions in accurately predicting these outcomes. However, the current state of research reveals a gap concerning the relationships between soil quality decline and soil-dwelling
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Soil quality decline can adversely affect ecosystem health and land productivity, with soil-dwelling mesofauna considered to potentially fulfill vital functions in accurately predicting these outcomes. However, the current state of research reveals a gap concerning the relationships between soil quality decline and soil-dwelling mesofauna in the Mollisols Region. For a more profound understanding of this issue, we conducted a comprehensive investigation of soil-dwelling mesofaunal communities in the different agricultural lands of the Mollisols Region. In this study, soil-dwelling mesofauna were collected, and 11 soil properties were determined following standard procedures, with soil quality levels quantified by utilizing soil quality index (SQI). Our results revealed that there was a gradient of soil quality across the different agricultural lands, which were divided into five levels, including very strong, strong, medium, weak, and very weak. Subsequently, this investigation provided empirical evidence that the decline in soil quality had implications for soil-dwelling mesofaunal communities in agricultural lands of the Mollisols region. A consistent decrease in the density of soil-dwelling mesofauna was observed with the decline of soil quality. In contrast, a greater richness was observed in areas with relatively weaker soil quality, suggesting that the consequences of soil quality decline on soil-dwelling mesofauna were not exclusively negative. Various taxa of soil-dwelling mesofauna exhibited varying degrees of response to the decline in soil quality. Oribatida was overwhelmingly dominant in the sampling fields with medium soil quality, and most Entomobryidae were found in agricultural lands with very weak soil quality. During soil quality decline, soil nutrients were observed to correlate positively with the density of soil-dwelling mesofauna. Overall, the outcomes of this investigation carry significance for comprehending how soil quality decline relates to soil-dwelling mesofauna, and can provide valuable ecological insights for formulating biodiversity guidelines targeted at preserving soil resources in the Mollisols region.
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(This article belongs to the Special Issue Soil Management for Sustainable Agriculture)
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Open AccessArticle
A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation
by
Yuanyuan Liu, Guangjun Qiu and Ning Wang
Agriculture 2024, 14(5), 765; https://doi.org/10.3390/agriculture14050765 - 16 May 2024
Abstract
The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of
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The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of peanut seed fullness using a non-destructive soft X-ray, which is suitable for the analysis of the surface or a thin layer of a material. The overall grayscale of the peanut is similar to the background, and the edge of the peanut seed is blurred. The inaccuracy of peanut overall and peanut seed segmentation leads to low accuracy of seed plumpness detection. To improve accuracy in detecting the fullness of peanut seeds, a seed plumpness detection method based on level set and multi-threshold segmentation was proposed for peanut images. Firstly, the level set algorithm is used to extract the overall contour of peanuts. Secondly, the obtained binary image is processed by morphology to obtain the peanut pods (the peanut overall). Then, the multi-threshold OTSU algorithm is used for threshold segmentation. The threshold is selected to extract the peanut seed part. Finally, morphology is used to complete the cavity to achieve the segmentation of the peanut seed. Compared with optimization algorithms, in the segmentation of the peanut pods, average random index (RI), global consistency error (GCE) and variation of information (VI) were increased by 10.12% and decreased by 0.53% and 24.11%, respectively. Compared with existing algorithms, in the segmentation of the peanut seed, the average RI, VI and GCE were increased by 18.32% and decreased by 9.14% and 6.11%, respectively. The proposed method is stable, accurate and can meet the requirements of peanut image plumpness detection. It provides a feasible technical means and reference for scientific experimental breeding and testing grading service pricing.
Full article
(This article belongs to the Special Issue Sensing and Imaging for Quality and Safety of Agricultural Products)
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