Plant Diseases: Interactions, Resistance, Epidemiology, and Control

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Plant Science".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 1649

Special Issue Editors

State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
Interests: cirus diseases; cereal crops; control; transmission; epidemics
Special Issues, Collections and Topics in MDPI journals
Plant Virus and Vector Interactions Group, Crop Research Institute, 16106 Prague, Czech Republic
Interests: plant virus and vector interactions; cereal and canola viruses; resistance; diagnosis; control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to global climate warming, increasing transportation of agricultural products and planting materials and intensive agricultural farming practices, the epidemics of emerging pathogens and known pathogens significantly threaten crop production. The development of a control measure for a plant disease in a crop requires a good understanding of the epidemiology which involves the pathogen, host, vectors and changing environment. Epidemiology concerns how and why a pathogen spreads in a plant population, and the consequences in terms of disease. For the effective management of a plant disease, first obtaining an understanding of its epidemiology is essential.

In recent years, the understanding of the interactions between crop pathogens, their hosts and vectors, and the epidemic rules of various crop diseases have increased significantly. Epidemiological studies on the distribution and determinants of crop diseases and the application of these studies to the control of diseases will be the focus of this Special Issue.

In particular, we welcome contributions on the following topics:

  • Pathogen–host interactions during viral replication, translation, movement and host resistance;
  • Pathogen–vector interactions during transmission;
  • Epidemic determinants;
  • Current understanding of the best management strategies to control crop diseases.

Prof. Dr. Xifeng Wang
Dr. Jiban Kumar Kundu
Guest Editors

Manuscript Submission Information

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Keywords

  • crop diseases
  • crops
  • transmission
  • epidemics
  • control

Published Papers (2 papers)

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Research

12 pages, 2542 KiB  
Article
Identification of Fusarium oxysporum Causing Leaf Blight on Dendrobium chrysotoxum in Yunnan Province, China
Life 2024, 14(3), 285; https://doi.org/10.3390/life14030285 - 20 Feb 2024
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Abstract
Leaf-blight disease caused by the Fusarium oxysporum is an emerging problem in Dendrobium chrysotoxum production in China. Symptoms of leaf blight were observed on seedlings of D. chrysotoxum cultivated in a nursery in Ruili City, Yunnan Province, China. In this study, we isolated [...] Read more.
Leaf-blight disease caused by the Fusarium oxysporum is an emerging problem in Dendrobium chrysotoxum production in China. Symptoms of leaf blight were observed on seedlings of D. chrysotoxum cultivated in a nursery in Ruili City, Yunnan Province, China. In this study, we isolated the Fusarium sp. associated with leaf-blight disease of D. chrysotoxum from the diseased seedlings. A pathogenicity test was performed to fulfill Koch’s postulates to confirm the pathogenicity of isolated strains and identified using morphological and molecular techniques. The results revealed that all four isolated Fusarium sp. isolates (DHRL-01~04) produced typical blight symptoms followed by marginal necrosis of leaves on the D. chrysotoxum plants. On the PDA medium, the fungal colony appeared as a white to purplish color with cottony mycelium growth. Microconidia are oval-shaped, whereas macroconidia are sickle-shaped, tapering at both ends with 2–4 septations. The phylogenetic trees were construed based on internal transcribed spacer (ITS), translation elongation factor (EF-1α), and RNA polymerase subunit genes RPB1 and RPB2 genes, respectively, and blasted against the NCBI database for species confirmation. Based on the NCBI database’s blast results, the isolates showed that more than 99% identify with Fusarium oxysporum. To our knowledge, this is the first comprehensive report on the identification of Fusarium oxysporum as the causal agent of Dendrobium chrysotoxum leaf blight in Yunnan Province, China, based on morphological and molecular characteristics. Full article
(This article belongs to the Special Issue Plant Diseases: Interactions, Resistance, Epidemiology, and Control)
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22 pages, 4291 KiB  
Article
Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds
Life 2023, 13(11), 2125; https://doi.org/10.3390/life13112125 - 26 Oct 2023
Cited by 2 | Viewed by 998
Abstract
Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the [...] Read more.
Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases. Full article
(This article belongs to the Special Issue Plant Diseases: Interactions, Resistance, Epidemiology, and Control)
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