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 - Q1 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.2 days after submission; acceptance to publication is undertaken in 2.3 days (median values for papers published in this journal in the first half of 2024).
- 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:
3.3 (2023);
5-Year Impact Factor:
3.5 (2023)
Latest Articles
Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)
Agriculture 2024, 14(9), 1578; https://doi.org/10.3390/agriculture14091578 (registering DOI) - 11 Sep 2024
Abstract
Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous
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Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted ) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg−1, respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making.
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(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning for Digital Soil Mapping)
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Open AccessArticle
Cotton Disease Recognition Method in Natural Environment Based on Convolutional Neural Network
by
Yi Shao, Wenzhong Yang, Jiajia Wang, Zhifeng Lu, Meng Zhang and Danny Chen
Agriculture 2024, 14(9), 1577; https://doi.org/10.3390/agriculture14091577 (registering DOI) - 11 Sep 2024
Abstract
As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing
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As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Design of a 2R Open-Chain Plug Seedling-Picking Mechanism and Control System Constrained by a Differential Non-Circular Planetary Gear Train
by
Maile Zhou, Tingbo Xu, Guibin Wang, Herui Dong, Shiyu Yang and Zeliang Wang
Agriculture 2024, 14(9), 1576; https://doi.org/10.3390/agriculture14091576 - 10 Sep 2024
Abstract
With a focus on the problems of complex structure and accumulated lateral clearance in the single degree of freedom non-circular wheel system seedling-picking mechanism, which leads to poor motion accuracy, trajectory, and attitude, this study developed a 2R open-chain chili plug seedling-picking mechanism
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With a focus on the problems of complex structure and accumulated lateral clearance in the single degree of freedom non-circular wheel system seedling-picking mechanism, which leads to poor motion accuracy, trajectory, and attitude, this study developed a 2R open-chain chili plug seedling-picking mechanism (SPM) constrained by a differential non-circular wheel system. The picking arm was driven by a single-stage non-uniform speed transmission mechanism to reproduce the seedling-picking trajectory and attitude. A protruding seedling-picking device, SPM control system, and test bench were designed. A kinematic model of a differential non-circular gear system was established, and an optimization design software for the SPM was developed based on kinematic analysis. The kinematic characteristics of the SPM were analyzed under optimal parameters. This study completed the seedling-picking performance test of the SPM on the control panel. The test showed that the designed chili SPM can sequentially complete the processes of seedling picking, conveying, retracting, pushing, and returning under the automatic control of the test bench without damaging the main root. The lateral root damage rate was 15.7%, effectively ensuring the integrity of the seedling bowl substrate.
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(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Scientific and Technological Innovation Effects on High-Quality Agricultural Development: Spatial Boundaries and Mechanisms
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Shuai Qin and Hong Chen
Agriculture 2024, 14(9), 1575; https://doi.org/10.3390/agriculture14091575 - 10 Sep 2024
Abstract
This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major
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This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major grain-producing regions spanning from 2004 to 2021, we revealed significant spatiotemporal variations in the impact of STI on HQA in both local and adjacent cities. Our findings remained robust after rigorous testing. The study identified the spillover range of STI to be 420 km, displaying a distinctive inverted U-shaped trend around 170 km. Mechanism analysis indicates that both agricultural industry upgrades and human capital levels within 420 km amplify the influence of STI on local HQA, with only the latter demonstrating spillover effects. Within 170 km, both factors effectively regulate HQA in adjacent cities, while beyond this distance, only human capital regulatory impact continues to exhibit spillover effects. These insights offer theoretical guidance for designing effective agricultural scientific and technology promotion policies aimed at elevating the quality of HQA.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
A Hydroponic System to Study the Effects of Root and Meristem Night Temperature on Growth, Photosynthesis Carbon Balance, and Antioxidant Enzymes in Rice
by
Alejandro J. Pieters, Sabine Stürz, Julia Asch and Folkard Asch
Agriculture 2024, 14(9), 1574; https://doi.org/10.3390/agriculture14091574 - 10 Sep 2024
Abstract
Nocturnal root and meristem temperature (RMT) can have a strong effect on rice growth and yield. However, underlying mechanisms are not well understood. To investigate the effects of night-time RMT on photosynthesis biomass allocation and activities of antioxidant enzymes, we designed a hydroponic
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Nocturnal root and meristem temperature (RMT) can have a strong effect on rice growth and yield. However, underlying mechanisms are not well understood. To investigate the effects of night-time RMT on photosynthesis biomass allocation and activities of antioxidant enzymes, we designed a hydroponic system that maintained the following daily patterns of day/night temperature: 18/28 °C (HNT) or 28/18 °C (LNT). Rice plants cv. IR64 were grown in the greenhouse and subjected to either HNT or LNT. HNT stimulated growth and tillering but did not affect biomass allocation. HNT plants increased total biomass by 16 and 35%, depending on time of exposure. HNT increased rates of photosynthesis (Pn) compared to LNT plants in leaves of different ages. Overnight carbohydrate remobilisation was larger in HNT than in LNT plants, particularly at 16 days after treatment (dat), when Pn and relative growth rates were highest. Leaf soluble protein concentrations and specific leaf area were not affected by RMT, indicating higher photosynthetic nitrogen use efficiency in HNT plants. Super Oxide Dismutase, Ascorbate Peroxidase, and Glutathione Reductase activities did not respond to RMT, indicating no change in the production of reactive oxygen species in LNT plants despite lower photosynthesis rates. HNT increased sink demand by stimulating tillering, the increased sink demand upregulated the source activity through a larger leaf area per plant and a higher Pn throughout the canopy. The hydroponic system described here was able to control the temperature of the nutrient solution effectively, the installation of a second pump directly circulating the nutrient solution from and back to the reservoir through the cooling system allowed reaching the target temperature within 1 h. This system opens new opportunities to characterise plant responses to RMT alone or in combination with other environmental drivers.
Full article
(This article belongs to the Special Issue Innovative Hydroponic Systems for Sustainable Agriculture)
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Open AccessArticle
Hyperspectral Imaging and Machine Learning: A Promising Tool for the Early Detection of Tetranychus urticae Koch Infestation in Cotton
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Mariana Yamada, Leonardo Vinicius Thiesen, Fernando Henrique Iost Filho and Pedro Takao Yamamoto
Agriculture 2024, 14(9), 1573; https://doi.org/10.3390/agriculture14091573 - 10 Sep 2024
Abstract
Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This
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Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Foliar H2O2 Application Improve the Photochemical and Osmotic Adjustment of Tomato Plants Subjected to Drought
by
Gustavo Ribeiro Barzotto, Caroline Pardine Cardoso, Letícia Galhardo Jorge, Felipe Girotto Campos and Carmen Sílvia Fernandes Boaro
Agriculture 2024, 14(9), 1572; https://doi.org/10.3390/agriculture14091572 - 10 Sep 2024
Abstract
Water limits may have a disastrous impact on agricultural productivity, and the current climate change scenario presents additional problems for crops that rely on regular rainfall. Reactive oxygen species, such as hydrogen peroxide (H2O2), are a recognized stress-sensing mechanism
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Water limits may have a disastrous impact on agricultural productivity, and the current climate change scenario presents additional problems for crops that rely on regular rainfall. Reactive oxygen species, such as hydrogen peroxide (H2O2), are a recognized stress-sensing mechanism in plants, and may be investigated as an approach for reducing stress impact via systemic acquired acclimation. Here, we looked at how H2O2 foliar application impacts tomato plants’ photosynthetic activity, antioxidant system, sugar chemical profile, and osmotic adjustment during drought and recovery. The experiment was in randomized blocks, 3 × 2 factorial design, with no, one, or two foliar application of 1 mM H2O2, on plants that were either continually watered or subjected to drought. The plants were tested both during the drought period and after they had resumed irrigation (recovered). Leaf water potential, chlorophyll a fluorescence, gas exchange, lipid peroxidation, H2O2 concentrations, phenols, proline, antioxidant enzyme activity, and sugar chemical profile were all measured. Our findings showed that H2O2 application generated metabolic alterations in tomato plants independent of water status, and that two applications in drought plants resulted in a 30% decrease in oxidative stress during drought and faster recovery following irrigation return, with greater production of defence-related molecules such as the APX enzyme, phenols, arabinose, and mannose. Continually watered plants also benefited from H2O2 application, which increased carbon assimilation by 35%.
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(This article belongs to the Topic Plant Responses to Environmental Stress)
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Open AccessArticle
Monocular Visual Pig Weight Estimation Method Based on the EfficientVit-C Model
by
Songtai Wan, Hui Fang and Xiaoshuai Wang
Agriculture 2024, 14(9), 1571; https://doi.org/10.3390/agriculture14091571 - 10 Sep 2024
Abstract
The meat industry is closely related to people’s daily lives and health, and with the growing global population and increasing demand for meat, the development of efficient pig farming technology is particularly important. However, China’s pig industry still faces multiple challenges, such as
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The meat industry is closely related to people’s daily lives and health, and with the growing global population and increasing demand for meat, the development of efficient pig farming technology is particularly important. However, China’s pig industry still faces multiple challenges, such as high labor costs, high biosecurity risks, and low production efficiency. Therefore, there is an urgent need to develop a fast, accurate, and non-invasive method to estimate pig body data to increase production efficiency, enhance biosecurity measures, and improve pig health. This study proposes EfficientVit-C model for image segmentation and cascade several models to estimate the weight of pigs. The EfficientVit-C network uses a cascading group attention module and improves computational efficiency through parameter redistribution and structured pruning. This method uses only one camera for weight estimation, reducing equipment costs and maintenance expenses. The results show that the improved EfficientVit-C model can segment pigs accurately and efficiently the mAP50 curve convergence is 98.2%, the recall is 92.6%, and the precision is 96.5%. The accuracy of pig weight estimation is 100 kg +/− 3.11 kg. On the Jetson Orin NX platform, the average time to complete image segmentation for each 640*480 resolution image was 4.1 ms, and the average time required to complete pig weight estimation was 31 ms. The results show that this method can quickly and accurately estimate the weight of pigs and provide guidance for the subsequent weight evaluation procedures of pigs.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Efficiency of Backwashing in Removing Solids from Sand Media Filters for Drip Irrigation Systems
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Miquel Duran-Ros, Joan Pujol, Toni Pujol, Sílvia Cufí, Jonathan Graciano-Uribe, Gerard Arbat, Francisco Ramírez de Cartagena and Jaume Puig-Bargués
Agriculture 2024, 14(9), 1570; https://doi.org/10.3390/agriculture14091570 - 10 Sep 2024
Abstract
Sand media filters are especially recommended to prevent emitter clogging with loaded irrigation waters, but their performances rely on backwashing. Despite backwashing being a basic procedure needed to restore the initial filtration capacity, there is a lack of information about the solid removal
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Sand media filters are especially recommended to prevent emitter clogging with loaded irrigation waters, but their performances rely on backwashing. Despite backwashing being a basic procedure needed to restore the initial filtration capacity, there is a lack of information about the solid removal efficiency along the media bed depth. An experimental filter with a 200 mm silica sand bed height was used to assess the effect of two operation velocities (30/45 and 60/75 (filtration/backwashing) m h−1) and two clogging particles (inorganic sand dust and organic from a reclaimed effluent) on the efficiency of backwashing for removing the total suspended solids retained in different media bed slices. The average solid removal backwashing efficiency was greater with organic particles (78%) than with inorganic ones (64%), reaching its maximum at a 5–15 mm bed depth. A higher operation velocity increased the solid removal efficiency by 16%, using organic particles, but no significant differences were observed with inorganic particles. The removal efficiencies across the media bed were more uniform with organic particles (63–89%) than with inorganic (40–85%), which makes it not advisable to reduce the media height when reclaimed effluents are used. This study may contribute to future improvements in sand media filter design and management.
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(This article belongs to the Section Agricultural Water Management)
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Open AccessArticle
Integrated Scale-Adaptive Adjustment Factor-Enhanced BlendMask Method for Pineapple Processing System
by
Haotian Wang, Haojian Zhang, Yukai Zhang, Jieren Deng, Chengbao Liu and Jie Tan
Agriculture 2024, 14(9), 1569; https://doi.org/10.3390/agriculture14091569 - 10 Sep 2024
Abstract
This study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, we developed an integrated
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This study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, we developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a Pose Correction Planning (PCP) method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.
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(This article belongs to the Section Agricultural Technology)
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Open AccessSystematic Review
Exploring Community-Supported Agriculture through Maslow’s Hierarchy: A Systematic Review of Research Themes and Trends
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Xiaofan Tian, Ruifang Zhang, Zifan Wang, Xinna Kang and Zhixin Yang
Agriculture 2024, 14(9), 1568; https://doi.org/10.3390/agriculture14091568 - 10 Sep 2024
Abstract
Community-supported agriculture (CSA) has emerged as a pivotal model for sustainable and humanistic agricultural practices, emphasizing the symbiotic relationship between food production, consumption, and sustainable ecosystems. Despite the growing interest, a comprehensive analysis of research themes and trends within the CSA framework remains
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Community-supported agriculture (CSA) has emerged as a pivotal model for sustainable and humanistic agricultural practices, emphasizing the symbiotic relationship between food production, consumption, and sustainable ecosystems. Despite the growing interest, a comprehensive analysis of research themes and trends within the CSA framework remains sparse. This paper undertakes a systematic review of CSA literature from 1999 to 2023, identifying evolving research hotspots, dominant themes, and prospective directions by keyword analysis to corroborate Maslow’s hierarchy of needs theory. The research analysis location is categorized into four temporal phases, revealing a geographical expansion from North America to encompass Asia, Africa, and other continents. This expansion corroborates Maslow’s theory, illustrating a global shift from fulfilling basic physiological needs towards recognizing sustainable practices, particularly in developing regions. The results of temporal trends (5 phases) and the hotspots of keyword analysis support each other by showing a societal shift from basic sustenance to a deeper understanding of nutrition and diet. Most of the recent research keywords are grouped into the “environment” and “health and education” categories, indicating an increasing emphasis on transforming the food system and nutrition education. This review suggests conducting an integrated analysis that links the various stages of the food supply chain with the criteria outlined in the Sustainable Agriculture Matrix (SAM). It highlights that the “environment” theme is a stage of building up esteem and self-realization that needs to be unfolded in the future, given that most research on community-supported agriculture (CSA) focuses on the “economy and society” aspect and consumption stage, which burnish self-morality in the theory of Maslow. Overall, this review proposes an analysis of the relevance among different subject categories and between food supply chain stages, which reveals that the trend of research under CSA development is accorded to the theory of Maslow’s hierarchy of needs and calls for a more holistic approach to agricultural research that considers ecological, health, and social imperatives.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN
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Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad and Mohammad Aufa Mhd Bookeri
Agriculture 2024, 14(9), 1567; https://doi.org/10.3390/agriculture14091567 - 10 Sep 2024
Abstract
Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper (Nilaparvata lugens; BPH), whitebacked planthopper (Sogatella furcifera; WBPH), zigzag leafhopper (Maiestas dorsalis; ZIGZAG), and green
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Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper (Nilaparvata lugens; BPH), whitebacked planthopper (Sogatella furcifera; WBPH), zigzag leafhopper (Maiestas dorsalis; ZIGZAG), and green leafhopper (Nephotettix malayanus and Nephotettix virescens; GLH). Most of the available automated counting methods are limited to populations of a small density and often do not consider those with a high density, which require more complex solutions due to overlapping objects. Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. The results indicated that Faster R-CNN VGG 16 outperformed other models, achieving a mean average precision (mAP) score of 97.69% and exhibiting exceptional accuracy in classifying all planthopper categories. The correctness of the model was verified by entomologists, who confirmed a classification and counting accuracy rate of 98.84%. Nevertheless, the model fails to recognise certain samples because of the high density of the population and the significant overlap among them. This research effectively resolved the issue of low- to medium-density samples by achieving very precise and rapid detection and counting.
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(This article belongs to the Special Issue Advanced Image Processing in Agricultural Applications)
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Open AccessArticle
Biological and Physiological Changes in Spodoptera frugiperda Larvae Induced by Non-Consumptive Effects of the Predator Harmonia axyridis
by
Zeyun Fan, Weizhen Kong, Xiaotong Ran, Xiaolu Lv, Chongjian Ma and He Yan
Agriculture 2024, 14(9), 1566; https://doi.org/10.3390/agriculture14091566 - 10 Sep 2024
Abstract
The effects of predatory natural enemies on their prey or hosts involve both consumption and non-consumptive effects. This study investigated the non-consumptive effects of the predator, Harmonia axyridis (Coleoptera: Coccinellidae) on 1st, 2nd and 3rd instar larvae of Spodoptera frugiperda. We exposed
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The effects of predatory natural enemies on their prey or hosts involve both consumption and non-consumptive effects. This study investigated the non-consumptive effects of the predator, Harmonia axyridis (Coleoptera: Coccinellidae) on 1st, 2nd and 3rd instar larvae of Spodoptera frugiperda. We exposed larvae of different instars to the predator and assessed various parameters using a combination of biological and biochemical methods. Exposure to the predator significantly affected the growth and development of the S. frugiperda caterpillars. Firstly, the developmental duration of S. frugiperda larvae in the 1st–3rd instars and the pupal stage were notably prolonged. Moreover, we observed significant effects on pupal mass, pupal abnormality rate and emergence rate. These non-consumptive effects were gradually weakened with an increase in the larval stage exposed to the predator. Antioxidant enzyme activities including catalase (CAT) peroxidase (POD) and superoxide dismutase (SOD) activity increased significantly. Additionally, organismal triglyceride, trehalose and glycogen content were significantly altered by non-consumptive effects, while protein content showed no significant change. Spodoptera frugiperda larvae increased the activity of antioxidant enzymes in response to potential predators to mitigate oxidative stress and reduce cellular and tissue damage. This resources redistribution towards survival may inhibit growth and development of the species and further exacerbate these non-consumptive effects. These findings highlight the importance of considering non-consumptive effects in pest-management strategies to optimize control measures in agricultural systems.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessArticle
Named Entity Recognition for Crop Diseases and Pests Based on Gated Fusion Unit and Manhattan Attention
by
Wentao Tang, Xianhuan Wen and Zelin Hu
Agriculture 2024, 14(9), 1565; https://doi.org/10.3390/agriculture14091565 - 10 Sep 2024
Abstract
Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and
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Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and enhances it using bidirectional long short-term memory (BiLSTM) to extract features from the context. It uses a gated unit to perform weighted fusion between the outputs of RoBERTa and BiLSTM, thereby enriching the information flow. The fused output is then fed into a novel Manhattan attention mechanism to capture the long-range dependencies. The global optimum tagging sequence is obtained using the conditional random fields layer. To enhance the model’s robustness, we incorporate adversarial training using the fast gradient method. This introduces adversarial examples, allowing the model to learn more disturbance-resistant feature representations, thereby improving its performance against unknown inputs. GatedMan achieved F1 scores of 93.73%, 94.13%, 93.98%, and 96.52% on the AgCNER, Peoples_daily, MSRA, and Resume datasets, respectively, thereby outperforming the other models. Experimental results demonstrate that GatedMan accurately identifies entities related to crop diseases and pests and exhibits high generalizability in other domains.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Intercropping in Coconut Plantations Regulate Soil Characteristics by Microbial Communities
by
Chaoqun Tong, Ruoyun Yu, Siting Chen, An Hu, Zhiguo Dong, Longxiang Tang, Lilan Lu, Weibo Yang and Rongshu Dong
Agriculture 2024, 14(9), 1564; https://doi.org/10.3390/agriculture14091564 - 10 Sep 2024
Abstract
Intercropping is a commonly employed agricultural technique that offers numerous advantages, such as increasing land productivity, enhancing soil health, and controlling soil-borne pathogens. In this study, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped with coconuts and compared with naturally growing weeds (
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Intercropping is a commonly employed agricultural technique that offers numerous advantages, such as increasing land productivity, enhancing soil health, and controlling soil-borne pathogens. In this study, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped with coconuts and compared with naturally growing weeds (Bidens pilosa), respectively. The regulatory mechanism of intercropping was examined by analyzing the variability in soil properties and microbial community structure across different intercropping modes and soil depths (0–20 cm, 20–40 cm, and 40–60 cm). The results indicate that intercropping can increase the diversity of soil bacteria and fungi. Moreover, as soil depth increases, the changes in microbial communities weaken. Intercropping reduced soil SOM and increased pH, which is directly related to the changes in the abundance of Acidobacteria in the soil. In various intercropping systems, the disparities resulting from intercropping with A. pintoi are particularly pronounced. Specifically, intercropping with A. pintoi leads to an increase in soil potassium and phosphorus levels, as well as an enhancement in the abundance of Bacillus sp., which plays a crucial role in the suppression of plant pathogenic fungi within the soil ecosystem. The results of the correlation analysis and structural equation modeling (SEM) suggest that the impacts of three intercropping systems on microbial composition and soil indicators exhibit considerable variation. However, a common critical factor influencing these effects is the soil phosphorus content. Furthermore, our findings indicate that intercropping resulted in lower soil nitrogen levels, exacerbating nitrogen deficiency and masking its impact on the microbial community composition.
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(This article belongs to the Section Agricultural Soils)
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Open AccessArticle
Research on the Jet Distance Enhancement Device for Blueberry Harvesting Robots Based on the Dual-Ring Model
by
Wenxin Li, Hao Yin, Yuhuan Li, Xiaohong Liu, Jiang Liu and Han Wang
Agriculture 2024, 14(9), 1563; https://doi.org/10.3390/agriculture14091563 - 9 Sep 2024
Abstract
In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate
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In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle’s convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements.
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(This article belongs to the Special Issue Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery Design and Development)
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Open AccessArticle
Varying Tolerance to Diesel Toxicity Revealed by Growth Response Evaluation of Petunia grandiflora Shoot Lines Regenerated after Diesel Fuel Treatment
by
Solomon Peter Wante, David W. M. Leung and Hossein Alizadeh
Agriculture 2024, 14(9), 1562; https://doi.org/10.3390/agriculture14091562 - 9 Sep 2024
Abstract
Continuous efforts are required to find ways to protect crop production against the toxicity of petroleum hydrocarbons, such as diesel, and contamination of soils. There is a need for identification of candidate plants that are tolerant to diesel toxicity that might also have
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Continuous efforts are required to find ways to protect crop production against the toxicity of petroleum hydrocarbons, such as diesel, and contamination of soils. There is a need for identification of candidate plants that are tolerant to diesel toxicity that might also have the potential for remediation of diesel-contaminated soils. In this study, petunia, a popular ornamental plant and a model experimental plant in research on phytoremediation of environmental pollutants, was used to evaluate a novel method for rapidly assessing diesel toxicity based on the tolerance of shoots generated through in vitro plant cell culture selection. Petunia shoot lines (L1 to L4) regenerated from diesel-treated callus were compared with those from non-diesel-treated callus (control). Significant morphological differences were observed among the tested lines and control, notably with L1 and L4 showing superior growth. In particular, L4 exhibited remarkable adaptability, with increased root development and microbial counts in a diesel-contaminated potting mix, suggesting that the shoots exhibited enhanced tolerance to diesel exposure. Here, this rapid bioassay has been shown to effectively identify plants with varying levels of tolerance to diesel toxicity and could therefore assist accelerated selection of superior plants for phytoremediation. Further research is needed to understand the genetic and physiological mechanisms underlying tolerance traits, with potential applications beyond petunias to other environmentally significant plants.
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(This article belongs to the Section Crop Production)
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Open AccessArticle
Hybrid Percolation–Ultrasound Method for Extracting Bioactive Compounds from Urtica dioica and Salvia officinalis
by
Ana-Maria Tăbărașu, Florin Nenciu, Dragoș-Nicolae Anghelache, Valentin-Nicolae Vlăduț and Iuliana Găgeanu
Agriculture 2024, 14(9), 1561; https://doi.org/10.3390/agriculture14091561 - 9 Sep 2024
Abstract
Enhancing the efficacy of biofertilizers and biopesticides for horticultural applications presents numerous challenges, given the need to balance effectiveness with environmental and economic factors. Achieving these goals requires rigorous research into advanced technologies and formulations capable of effectively replacing or complementing traditional chemical
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Enhancing the efficacy of biofertilizers and biopesticides for horticultural applications presents numerous challenges, given the need to balance effectiveness with environmental and economic factors. Achieving these goals requires rigorous research into advanced technologies and formulations capable of effectively replacing or complementing traditional chemical inputs, without compromising crop yield or quality. The present study aimed to develop a versatile and yet accessible hybrid percolation–sonication system and method, designed to optimize polyphenol extraction from nettle and sage plants. The resulting extracts were combined and applied on organic tomato crops, to evaluate their biofertilizer and biopesticide effectiveness, in comparison to conventional chemical inputs. Operating the equipment in a hybrid percolation–sonication system led to a 36% increase in polyphenols extraction for nettle and a 29% increase from sage. Regarding the effect on tomatoes, data showed that plants treated with biofertilizer extracts were over 42.88% more productive than control samples and 17.67% more productive than tomatoes treated with chemical fertilizers. Tomato plants treated with biofertilizers grew approximately 10% taller and developed stems that were 5% thicker compared to those treated with chemical fertilizers.
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(This article belongs to the Special Issue Organic Management Approaches and Practices to Support Sustainable Horticultural and Fruit Plants Production)
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Open AccessArticle
Exploring the Fermentation Products, Microbiology Communities, and Metabolites of Big-Bale Alfalfa Silage Prepared with/without Molasses and Lactobacillus rhamnosus
by
Baiyila Wu, Tong Ren, Changqing Li, Songyan Wu, Xue Cao, Hua Mei, Tiemei Wu, Mei Yong, Manlin Wei and Chao Wang
Agriculture 2024, 14(9), 1560; https://doi.org/10.3390/agriculture14091560 - 9 Sep 2024
Abstract
The influence of molasses (M) and Lactobacillus rhamnosus (LR) on fermentation products, microbial communities, and metabolites in big-bale alfalfa silage was investigated. Alfalfa (Medicago sativa L.) was harvested at the third growth stage during the flowering stage in the experimental field of
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The influence of molasses (M) and Lactobacillus rhamnosus (LR) on fermentation products, microbial communities, and metabolites in big-bale alfalfa silage was investigated. Alfalfa (Medicago sativa L.) was harvested at the third growth stage during the flowering stage in the experimental field of Linhui Grass Company from Tongliao City, Inner Mongolia. An alfalfa sample without additives was used as a control (C). M (20 g/kg) and LR (106 cfu/g) were added either alone or in combination. Alfalfa was fermented for 7, 14, and 56 d. Lactic acid content in the M, LR, and MLR groups increased, whereas the pH value and butyric acid, 2,3-butanediol, and ethanol contents decreased compared to those of C group after 7, 14, and 56 d of fermentation. A two-way analysis of variance (ANOVA) was performed to estimate the results. The LR group exhibited increased Lactobacillus abundance, whereas the M and MLR groups showed increased Weissella abundance compared to the C group. The relative contents of amino acids (tyrosine, isoleucine, threonine, arginine, valine, and citrulline) in the M and MLR groups were higher than those in the C group. During fermentation, the M, LR, and MLR groups showed decreased phenylalanine, isoleucine, and ferulic acid contents. Amino acids such as isoleucine and L-aspartic acid were positively correlated with Lactobacillus but negatively correlated with Weissella. In conclusion, combining high-throughput sequencing and liquid chromatography–mass spectrometry during anaerobic alfalfa fermentation can reveal new microbial community compositions and metabolite profiles, supporting the application of M, LR, and MLR as feed fermentation agents.
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(This article belongs to the Section Farm Animal Production)
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An In Vitro Study on the Antioxidant Properties of Cistus incanus Extracts
by
Małgorzata Olszowy-Tomczyk and Dorota Wianowska
Agriculture 2024, 14(9), 1559; https://doi.org/10.3390/agriculture14091559 - 9 Sep 2024
Abstract
This paper concerns the evaluation of the antioxidant activity (AA) of extracts obtained from cistus herbs grown in Albania and Turkey. The extracts were prepared in a Soxhlet apparatus, as well as by the maceration and infusion methods, similar to the home method
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This paper concerns the evaluation of the antioxidant activity (AA) of extracts obtained from cistus herbs grown in Albania and Turkey. The extracts were prepared in a Soxhlet apparatus, as well as by the maceration and infusion methods, similar to the home method of preparing herbal teas. AA was determined using the DPPH (2,2′-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), FRAP (ferric reducing antioxidant power) and β-carotene methods. It was proven that the AA of extracts depends not only on the extraction technique and AA assessment method but also on the place of plant cultivation. The smallest activity was determined using the β-carotene bleaching method, while the highest values were obtained using the FRAP method. On the other hand, the ABTS method showed a greater activity of the Albanian herb prepared using the Soxhlet technique. In addition, the antioxidant properties of extracts were compared with those of rutin standard solutions, showing that this characteristic component of cistus is not the only one that determines AA of extracts. As a result, the rutin content is not an indicator of the antioxidant properties of extracts. The other polyphenolic compounds, although occurring at lower concentration levels compared to rutin, modify the resultant AA of extracts. These studies confirmed the biological activity of cistus as a valuable source of polyphenolic compounds in the human diet.
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(This article belongs to the Special Issue Natural Products: Phytochemical Extraction, Analysis and Application)
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