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Agriculture
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Comprehensive Application and Prospects of New Technologies for Plant Protection
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Comprehensive Application and Prospects of New Technologies for Plant Protection

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Protection, Diseases, Pests and Weeds".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 9090

Special Issue Editors

Prof. Dr. Shuo Yan

E-Mail Website
Guest Editor
College of Plant Protection, China Agricultural University, Beijing 100193, China
Interests: nanocarrier-based delivery system; RNA pesticide; nano-pesticide; nano-fertilizer
Dr. Xiaofeng Su

E-Mail Website
Guest Editor
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
Interests: plant resistant mechanism; fungal pathogenic mechanism; plant/microbe interaction; host-induced gene silencing; RNAi
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangrui Li

E-Mail Website
Guest Editor
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
Interests: insect wing development; aphid control technology; RNA pesticide; RNAi

Special Issue Information

Dear Colleagues,

The application of novel technologies for plant protection, including RNA interference (RNAi), artificial intelligence (AI) methods, nanotechnology, omics analysis, bioinformatics, gene editing, etc., has profoundly transformed traditional agriculture. These cutting-edge technologies offer diverse and promising applications, revolutionizing the prevention and management of crop pests and diseases. Their extensive research prospects and practical value underscore their significance.

The purpose and scope of this Special Issue involve exploring the applications of these novel technologies in agriculture while fostering innovation and sustainable development in crop protection.

We invite authors to submit original research (including concise communications) and unique reviews that contribute to the utilization of multiple novel techniques in agriculture. Subtopics of particular interest include (but are not limited to) the following:

  • RNA interference (RNAi) technology: RNAi-based pest management strategy.
  • Artificial intelligence (AI) methods: optimizing agricultural production management and crop protection strategies using AI.
  • Nanotechnology and new materials: development of novel nanomaterials or nanopesticides for insect and disease control in plant protection.
  • Omics analysis techniques: in-depth exploration of interactions between plants and pathogens through omics analysis.
  • Bioinformatics applications: analyzing large-scale biological data to inform crop protection decisions.
  • Gene editing techniques: leveraging gene editing to enhance crop resistance against pests and diseases.
  • Imaging technologies: early detection of plant health issues through imaging techniques.

Dr. Shuo Yan
Dr. Xiaofeng Su
Dr. Xiangrui Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nanocarriers
  • nanopesticide
  • plant diseases
  • gene editing
  • RNA interference
  • omics analysis
  • imaging
  • bioinformatics
  • green pest control
  • artificial intelligence

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Published Papers (7 papers)

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Research

24 pages, 12131 KiB  
Article
Reproductive Structures of Female Phytoseiulus persimilis (Acari: Phytoseiidae) and the Development of Egg and Embryo in the Body
by Binting Huang, Mingxia Li, Xiaohuan Jiang, Bo Zhang, Yong Huang and Xuenong Xu
Agriculture 2024, 14(9), 1647; https://doi.org/10.3390/agriculture14091647 - 20 Sep 2024
Viewed by 710
Abstract
The Phytoseiulus persimilis specialized in preying on Tetranychus species, with particularly strong predation capability against Tetranychus urticae. To investigate the morphology of female reproductive structures and effects of different gravid times on structures of oocytes and embryos in Phytoseiulus persimilis, we [...] Read more.
The Phytoseiulus persimilis specialized in preying on Tetranychus species, with particularly strong predation capability against Tetranychus urticae. To investigate the morphology of female reproductive structures and effects of different gravid times on structures of oocytes and embryos in Phytoseiulus persimilis, we employed paraffin sectioning, transmission electron microscopy (TEM), and scanning electron microscopy (SEM) on the model species of predatory mite Phytoseiulus persimilis. The female adult possessed several reproductive organs, including paired solenostomes, major ducts, embolus, calyces, and vesicles within the sperm-access system, as well as lyrate organ and the ovary. Furthermore, the reproductive system also encompassed the uterus, vagina, and genital pore, which were involved in egg development and expulsion. The solenostomes were situated between the third and fourth legs, and they were scarcely discernible in virgin, but they became apparent during mating. The occurrence of mating significantly influenced the nucleus of lyrate organ. In virgin, the nucleus exhibited underdeveloped morphology, whereas in mated individuals, it was well-formed. However, the duration of mating did not impact its development. The cellular structure of the ovary was solely associated with the stage of the surrounding oocyte and was not directly linked to mating occurrences. The uterus was barely visible outside of mating periods but became observable 12 h after mating when eggs were present within the body. At this point, it opened in preparation for egg laying when both the vagina and reproductive opening were open. Positioned in front of the vesicle but behind the ovary was the lyrate organ, with its lower part housing the uterus. The vagina was connected to the genital pore. No significant difference was observed in oocyte morphology between the virgin ovaries and the mated. Oocyte development occurred through four stages: during stage I (4–9 h after mating), yolk accumulation took place; stage II (10 h after mating) involved egg relocation; stage III (12–13 h after mating) was marked by eggshell formation; finally, at stage IV (14–16 h after mating), embryonic development commenced, leading to egg deposition. The fusion of sperm and egg occurred approximately 9–10 h after mating. These findings established a solid foundation for investigating the Phytoseiid reproductive mechanisms. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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24 pages, 1777 KiB  
Article
Implementation of Large Language Models and Agricultural Knowledge Graphs for Efficient Plant Disease Detection
by Xinyan Zhao, Baiyan Chen, Mengxue Ji, Xinyue Wang, Yuhan Yan, Jinming Zhang, Shiyingjie Liu, Muyang Ye and Chunli Lv
Agriculture 2024, 14(8), 1359; https://doi.org/10.3390/agriculture14081359 - 14 Aug 2024
Viewed by 1909
Abstract
This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. [...] Read more.
This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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27 pages, 7293 KiB  
Article
Implementation and Evaluation of Attention Aggregation Technique for Pear Disease Detection
by Tong Hai, Ningyi Zhang, Xiaoyi Lu, Jiping Xu, Xinliang Wang, Jiewei Hu, Mengxue Ji, Zijia Zhao, Jingshun Wang and Min Dong
Agriculture 2024, 14(7), 1146; https://doi.org/10.3390/agriculture14071146 - 15 Jul 2024
Viewed by 864
Abstract
In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including [...] Read more.
In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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21 pages, 2548 KiB  
Article
Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production
by Xiaotong Gao, Songwei Li, Xiaotong Su, Yan Li, Lingyun Huang, Weidong Tang, Yuanchen Zhang and Min Dong
Agriculture 2024, 14(7), 1098; https://doi.org/10.3390/agriculture14071098 - 9 Jul 2024
Viewed by 1054
Abstract
In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the [...] Read more.
In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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16 pages, 13212 KiB  
Article
Identification and Functional Characterization of an Odorant Receptor Expressed in the Genitalia of Helicoverpa armigera
by Weihao Liu, Dongdong Sun, Xiaoqing Wang, Zhiqiang Wang and Yang Liu
Agriculture 2024, 14(7), 1030; https://doi.org/10.3390/agriculture14071030 - 28 Jun 2024
Viewed by 877
Abstract
Olfaction is critical for guiding the physiological activities of insects, with antennae being the primary olfactory organs. However, recent evidence suggests that other tissues may also participate in olfactory recognition. Among these, the genitalia of moths have received attention due to their roles [...] Read more.
Olfaction is critical for guiding the physiological activities of insects, with antennae being the primary olfactory organs. However, recent evidence suggests that other tissues may also participate in olfactory recognition. Among these, the genitalia of moths have received attention due to their roles in mating and oviposition. Sensilla and odorant receptors (ORs) in moth genitalia highlight the potential olfactory function of these structures. In this study, we examined the olfactory sensing capacity of the genitalia in Helicoverpa armigera by analyzing their structure in males and females and characterizing the expressed ORs. Scanning electron microscopy uncovered many sensilla distributed throughout the male and female genitalia. Transcriptome sequencing identified 20 ORs in the genitalia, with HarmOR68 exhibiting significant responses to methyl esters: methyl benzoate and salicylate. Our findings provide theoretical evidence that H. armigera genitalia may have significant olfactory perception functions. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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16 pages, 2812 KiB  
Article
Preparation of Nanoscale Indoxacarb by Using Star Polymer for Efficiency Pest Management
by Min Chen, Jie Zhang, Hongtao Wang, Lingyun Li, Meizhen Yin, Jie Shen, Shuo Yan and Baoyou Liu
Agriculture 2024, 14(7), 1006; https://doi.org/10.3390/agriculture14071006 - 26 Jun 2024
Viewed by 1430
Abstract
The utilization efficiency of conventional pesticides is relatively low in agricultural production, resulting in excessive application and environmental pollution. The efficient utilization of pesticides is crucial for promoting sustainable agriculture, and the development of nanopesticides presents a promising solution to the challenges associated [...] Read more.
The utilization efficiency of conventional pesticides is relatively low in agricultural production, resulting in excessive application and environmental pollution. The efficient utilization of pesticides is crucial for promoting sustainable agriculture, and the development of nanopesticides presents a promising solution to the challenges associated with traditional pesticides. In order to explore an efficient application method for indendicarb (IDC), a star polymer nanocarrier (SPc) was employed to design and construct an efficient nanodelivery system for IDC. In this study, the morphology and physicochemical properties of the complex were determined, and its bioactivity and control efficacy were assessed using leaf-dipping and field spraying methods. The results show that IDC could be spontaneously incorporated into the hydrophobic core of SPc via hydrophobic association. This assembly disrupted the self-aggregated structure of IDC and significantly reduced its particle size to nanoscale. Furthermore, IDC emulsifiable concentrate (IDC EC) demonstrated improved adhesion to plant leaves with the aid of SPc, increasing retention from 8.083 to 10.418 mg/cm2. The LC50 (1d) of IDC EC against Plutella xylostella (Linnaeus) and Pieris rapae (Linnaeus) decreased by 6.784 and 1.931 times, respectively, with the addition of SPc. The inclusion of SPc increased the control effect of IDC EC by up to 8.28% (7d, 3000×) for P. xylostella and 12.53% (3d, 8000×) for P. rapae. This reveals that the IDC EC + SPc formulation exhibits superior insecticidal activity against these two highly destructive insect pests. This study successfully developed a novel nanodelivery system for the efficient application of IDC, which has the potential to reduce over-application and promote sustainable agricultural practices. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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31 pages, 2824 KiB  
Article
Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques
by Kuiheng Chen, Jingjing Lang, Jiayun Li, Du Chen, Xuaner Wang, Junyu Zhou, Xuan Liu, Yihong Song and Min Dong
Agriculture 2024, 14(6), 797; https://doi.org/10.3390/agriculture14060797 - 22 May 2024
Cited by 1 | Viewed by 1259
Abstract
An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention [...] Read more.
An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention mechanism backbone network, an aligned-head module, a Transformer-based semantic segmentation network, and a specially designed alignment loss function. The integration of these technologies not only optimizes the model’s ability to capture subtle disease features but also improves the efficiency of integrating sensor and image data, further enhancing the accuracy of the segmentation tasks. Experimental results demonstrate the superiority of this framework. For disease detection, the proposed method achieved a precision of 94%, a recall of 92%, and an accuracy of 92%, surpassing classical models like AlexNet, GoogLeNet, VGGNet, ResNet, and EfficientNet. In lesion segmentation tasks, the proposed method achieved a precision of 95%, a recall of 90%, and an mIoU of 94%, significantly outperforming models such as SegNet, UNet, and UNet++. The introduction of the aligned-head module and alignment loss function provides an effective solution for processing images lacking sensor data, significantly enhancing the model’s capability to process real agricultural image data. Through detailed ablation experiments, the study further validates the critical role of the aligned-head module and alignment loss function in enhancing model performance, particularly in the attention-head ablation experiment where the aligned-head configuration surpassed other configurations across all metrics, highlighting its key role in the overall framework. These experiments not only showcase the theoretical effectiveness of the proposed method but also confirm its practical value in agricultural disease management practices. Full article
(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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