Advanced Research on Diagnosis and Biological Control of Crop Diseases

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1564

Special Issue Editors


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Guest Editor
Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, China
Interests: plant disease diagnosis; molecular detection; biological control; disease management

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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
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Special Issue Information

Dear Colleagues,

Crops are continuously confronted with a wide variety of plant pathogens, including fungi, nematodes, oomycetes, bacteria, and viruses, resulting in significant losses of crop yield and quality worldwide. Only after identifying the pathogen of crop disease can the corresponding control strategies be developed based on the biological characteristics and occurrence regularity of pathogen species. Therefore, a rapid and accurate diagnosis of crop diseases is essential for their effective control. At present, chemical control is still the main method used for controlling crop diseases. However, the increased use of chemical pesticides on agricultural crops has raised a great number of economic, ecological and health concerns. As an alternative, biological control is an effective and sustainable method, as it uses beneficial microorganisms or microbial metabolites to control crop diseases.

Reviews, original research articles, and communications are all welcome.

This Special Issue, entitled "Advanced Research on Diagnosis and Biological Control of Crop Diseases", aims to present the latest research findings on any aspect of disease diagnosis and biological control. Some of the main topics include, but are not limited to, the following:

  • New diagnostic tools for the detection of crop disease;
  • Research and application of novel biocontrol;
  • The relationships between microbial diversity and biocontrol;
  • The key mechanisms of biocontrol;

Development of more diverse and effective biocontrol for crop diseases

Dr. Yi Cheng
Dr. Xiaofeng Su
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. Agronomy 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

  • molecular identification
  • sustainable crop protection
  • diagnostic and detection
  • high-throughput identification
  • mechanisms
  • biological control
  • crop disease

Published Papers (2 papers)

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12 pages, 3309 KiB  
Article
Pan-Genome Analysis and Secondary Metabolic Pathway Mining of Biocontrol Bacterium Brevibacillus brevis
by Jie Du, Binbin Huang, Jun Huang, Qingshan Long, Cuiyang Zhang, Zhaohui Guo, Yunsheng Wang, Wu Chen, Shiyong Tan and Qingshu Liu
Agronomy 2024, 14(5), 1024; https://doi.org/10.3390/agronomy14051024 - 11 May 2024
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Abstract
Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we [...] Read more.
Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we retrieved the genomic sequences of nine B. brevis strains that had been assembled into complete genomes from the NCBI database. These genomic sequences were analyzed using phylogenetic analysis software, pan-genome analysis software, and secondary metabolite mining software. Results revealed that the genome size of B. brevis strains ranged from 6.16 to 6.73 Mb, with GC content ranging from 47.0% to 54.0%. Phylogenetic analysis classified the nine B. brevis strains into three branches. The analyses of ANI and dDDH showed that B. brevis NEB573 had the potential to become a new species of Brevibacillus and needed further research in the future. The pan-genome analysis identified 10032 gene families, including 3257 core gene families, 3112 accessory gene families, and 3663 unique gene families. In addition, 123 secondary metabolite biosynthetic gene clusters of 20 classes were identified in the genomes of nine B. brevis strains. The major types of biosynthetic gene clusters were non-ribosomal peptide synthase (NRPS) and transAT polyketide synthase (transAT-PKS). Furthermore, a large number of untapped secondary metabolites were identified in B. brevis. In summary, this study elucidated the pan-genome characteristics of the biocontrol bacterium B. brevis and identified its secondary metabolites, providing valuable insights for its further development and utilization. Full article
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0 pages, 6367 KiB  
Article
An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification
by Cheng Li, Yunxiang Tian, Xiaolin Tian, Yikui Zhai, Hanwen Cui and Mengjie Song
Agronomy 2024, 14(4), 864; https://doi.org/10.3390/agronomy14040864 - 20 Apr 2024
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Abstract
The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree [...] Read more.
The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection. Full article
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