Detection, Identification and Control of Plant Pathogens

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: 15 September 2024 | Viewed by 1210

Special Issue Editors


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Guest Editor
School of Agricultural, Forestry, Food and Environmental Sciences (SAFE), University of Basilicata, Viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
Interests: mycology; fungal pathogens; bacteria; viruses; molecular phylogeny; plant molecular pathology; plant disease
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy
Interests: plant disease; natural products; molecular diagnosis; bioactive substances; microbiology; biological control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Science, Università degli studi della Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy
Interests: genetic; molecular biology; agronomy; microalgae
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Guest Editor
National Institute for Research and Development in Forestry “Marin Drăcea”, Station of Brasov, 500040 Brasov, Romania
Interests: tree (silviculture, agroforestry, horticulture) pathology; ecology; host resistance; pathogen control; invasive pests/plants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The early detection of plant pathogens and their control are essential for the protection of agricultural and forest plant health and also to assure good yields. Several factors including climate change and unsustainable agricultural and/or forestry practices have a direct influence on pathogen emergence and development. Therefore, in order to protect forests and agricultural crops, guarantee the quality of the harvest, and provide quality food, plant disease management measures need to be addressed. In this regard, the early detection, identification, characterization, and control of plant pathogens is critical, paying particular attention to the invasive ones, affecting agricultural crops or forests, and making use of the novel products, technologies, and knowledge. These can help reduce the time for the precise identification and characterization of plant pathogens. Knowledge about the efficiency and use of plant-based extracts and microbial biological control agents (MBCAs) in controlling phytopathogens of agricultural crops or forests is becoming more and more valuable to manage plant pathogens and move towards green chemistry in agriculture.

This Special Issue, focusing on the detection, identification, characterization and control of plant pathogens of agricultural crops or forest hosts, will comprise highly interdisciplinary quality studies combining plant pathology with forestry, molecular biology, genetics, chemistry and agriculture. Research articles will cover a wide array of plant disease knowledge from their early diagnosis applying novel technologies to understanding their manifestations and providing new and essential data for their management and control. All types of articles, such as original research papers, opinions, and reviews, are very much welcome.

You may choose our Joint Special Issue in Forests.

Dr. Stefania Mirela Mang
Prof. Ippolito Camele
Dr. Rosa Paola Paola Radice
Dr. Danut Chira
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

  • plant pathogens
  • agricultural crops
  • phytopathogen control
  • forests
  • early detection
  • diagnostics
  • ecosystem stability
  • emerging plant pathogens
  • food security
  • genetic resistance to phytopathogens
  • invasive pathogens
  • molecular identification and characterization
  • high-throughput sequencing (HTS)
  • metagenomics
  • microbial biological control agents (MBCAs)
  • biopesticides

Published Papers (1 paper)

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Research

18 pages, 4166 KiB  
Article
Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis
by Huiru Zhou, Qiang Lai, Qiong Huang, Dingzhou Cai, Dong Huang and Boming Wu
Agriculture 2024, 14(2), 290; https://doi.org/10.3390/agriculture14020290 - 10 Feb 2024
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Abstract
The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. [...] Read more.
The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores. Full article
(This article belongs to the Special Issue Detection, Identification and Control of Plant Pathogens)
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