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Agriculture
Special Issues
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: 20 September 2024 | Viewed by 310

Special Issue Editors

Dr. Shuo Yan

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

Published Papers (1 paper)

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Research

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 (registering DOI) - 22 May 2024
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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