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Plants
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Research on Plant Pathology and Disease Management
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Research on Plant Pathology and Disease Management

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Protection and Biotic Interactions".

Deadline for manuscript submissions: 29 November 2024 | Viewed by 540

Special Issue Editors

Dr. Monika Walter

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Guest Editor
Motueka Research Centre, Motueka, New Zealand
Interests: T biocontrol postharvest actin; cytoskeleton apple; Trichoderma; applied mycology
Dr. Ramon Gerardo Guevara-Gonzalez

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Guest Editor
Center of Applied Research in Biosystems-CARB, School of Engineering-Campus Amazcala, Autonomous University of Queretaro, Amazcala, El Marques, Querétaro 76265, Mexico
Interests: plant physiology of stress; plant molecular biology; plant biochemistry; plant pathology; plant biotechnology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant pathology is a field in need of continuous study if we are to try to understand the mechanisms of plant–pathogen interaction and to cope with global crop losses due to disease. Disease management is also currently a crucial theme in plant pathology, and the development and design of novel environmentally friendly strategies has now become a necessity. This Special Issue (SI) in Plants entitled “Research on Plant Pathology and Disease Management” aims to be a platform showcasing current basic and applied research regarding plant–pathogen interactions at different levels, as well as novel disease management strategies. This SI welcomes manuscripts dealing with aspects of basic and applied plant–pathogen interaction studies involving disease mechanisms at different levels, the description of novel plant diseases, as well as crop protection and novel proposals of disease management strategies. Research involving studies in plant–pathogen interactions with viruses, fungi, bacteria, oomycetes, nematodes, etc., at the epigenetic, molecular, biochemical, physiological, and omic levels and showing a clear advance of knowledge in this field are especially welcome.

Dr. Monika Walter
Dr. Ramon Gerardo Gerardo Guevara-Gonzalez
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. Plants is an international peer-reviewed open access semimonthly 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 2700 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

  • phytopathology
  • disease management
  • crop protection
  • plant disease mechanisms

Published Papers (1 paper)

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Research

24 pages, 12237 KiB  
Article
Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN
by Rong Ye, Guoqi Shao, Ziyi Yang, Yuchen Sun, Quan Gao and Tong Li
Plants 2024, 13(10), 1377; https://doi.org/10.3390/plants13101377 - 15 May 2024
Viewed by 342
Abstract
In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement [...] Read more.
In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value. Full article
(This article belongs to the Special Issue Research on Plant Pathology and Disease Management)
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