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Plants
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Plant Diseases and Sustainable Agriculture
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Plant Diseases and Sustainable Agriculture

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2631

Special Issue Editors

Dr. Margarida Espada

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Guest Editor
Laboratory of Nematology, Mediterranean Institute for Agriculture, Environment and Development (MED) & CHANGE – Global Change and Sustainability Institute, University of Évora, Polo da Mitra, 7000-083 Évora, Portugal
Interests: plant pathology; plant nematology; molecular basis of plant–nematode parasitic interactions; biology of parasitism proteins (effectors); genomics and transcriptomics
Special Issues, Collections and Topics in MDPI journals
Dr. Cláudia S. L. Vicente

E-Mail Website
Guest Editor
MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Institute for Advanced Studies and Research, Universidade de Évora, Pólo da Mitra, Ap. 94, 7006-554, Évora, Portugal
Interests: plant protection; nematology; biocontrol; omics; microbe interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Patrick Materatski

E-Mail Website
Guest Editor
Laboratory of Virology, MED–Mediterranean Institute for Agriculture, Environment and Development & CHANGE–Global Change and Sustainability Institute, Universidade de Évora, Pólo da Mitra, 7006-554 Évora, Portugal
Interests: plant pathology; plant virology; molecular diagnosis of plant pathogens; sustainable plant protection; RNAi; virus-induced gene silencing; siRNAs; gene expression; CRISPR-Cas systems
Special Issues, Collections and Topics in MDPI journals
Dr. Carla Varanda

E-Mail Website
Guest Editor
1. ESAS, Instituto Politécnico de Santarém, Quinta do Galinheiro, S. Pedro, 2001-904 Santarém, Portugal
2. CERNAS-Research Centre for Natural Resources, Environment and Society, Coimbra, Portugal
3. MED–Mediterranean Institute for Agriculture, Environment and Development & CHANGE–Global Change and Sustainability Institute, 7006-554 Évora, Portugal
Interests: plant pathology; plant virology; molecular diagnosis of plant pathogens; sustainable plant protection; virus-induced gene silencing; gene expression; CRISPR-Cas systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant pathogens (i.e., viruses, bacteria, fungi, nematodes, and insects) can cause huge crop losses with evident economic impacts. At present, we face several challenges such as population growth, climate change, and limited resources. Sustainability strategies worldwide (e.g., the European Green Deal under the Farm to Fork and Biodiversity Strategies and 17 ONU Sustainable Development Goals) emphasize the importance of a resilient agri-food sector and an efficient use of natural resources. To meet these challenges, plant protection represents a key factor in order to develop efficient strategies for disease management. Plant pathogens vary, affecting pathogenicity strategies to overcome plant defenses. For the success of plant protection it is essential to be one step ahead of these pathogens. This is possible by understanding how these infectious agents interact with their hosts, vectors, other pathogens, and the environment. We must also predict how all these drivers will respond to climate change, which can increase disease severity as well as lead to pathogens spreading into new areas. The scope of this Special Issue embraces the following research areas and welcomes  original research articles and reviews. Research areas may include (but are not limited to) the following:

  • Agriculture and food security;
  • Public policies/phytosanitary measures for agriculture management;
  • Biocontrol and green solutions for pest management;
  • Interaction of plant pathogens with abiotic stresses;
  • Impact of global climate changes in spreading of plant pathogens;
  • Reports on plant diseases.

We look forward to receiving your contributions.

Dr. Margarida Espada
Dr. Cláudia S. L. Vicente
Dr. Patrick Materatski
Prof. Dr. Carla Varanda
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

  • food security
  • plant pathology
  • plant disease management
  • crops
  • biotic and abiotic stresses
  • diagnosis
  • control
  • global climate changes
  • modeling
  • next-generation sequencing

Published Papers (3 papers)

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Research

34 pages, 4993 KiB  
Article
Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet
by Li Wan, Wenke Zhu, Yixi Dai, Guoxiong Zhou, Guiyun Chen, Yichu Jiang, Ming’e Zhu and Mingfang He
Plants 2024, 13(11), 1581; https://doi.org/10.3390/plants13111581 - 6 Jun 2024
Viewed by 514
Abstract
Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate [...] Read more.
Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model’s performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops. Full article
(This article belongs to the Special Issue Plant Diseases and Sustainable Agriculture)
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16 pages, 4205 KiB  
Article
Identification of Multiple Diseases in Apple Leaf Based on Optimized Lightweight Convolutional Neural Network
by Bin Wang, Hua Yang, Shujuan Zhang and Lili Li
Plants 2024, 13(11), 1535; https://doi.org/10.3390/plants13111535 - 1 Jun 2024
Viewed by 309
Abstract
In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf [...] Read more.
In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model’s classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future. Full article
(This article belongs to the Special Issue Plant Diseases and Sustainable Agriculture)
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13 pages, 2469 KiB  
Article
Functional Characterization of ShK Domain-Containing Protein in the Plant-Parasitic Nematode Bursaphelenchus xylophilus
by Madalena Mendonça, Cláudia S. L. Vicente and Margarida Espada
Plants 2024, 13(3), 404; https://doi.org/10.3390/plants13030404 - 30 Jan 2024
Viewed by 1135
Abstract
ShK domain-containing proteins are peptides found in different parasitic and venomous organisms. From a previous transcriptomic dataset from Bursaphelenchus xylophilus, a plant-parasitic nematode that infects forest tree species, we identified 96 transcripts potentially as ShK domain-containing proteins with unknown function in the [...] Read more.
ShK domain-containing proteins are peptides found in different parasitic and venomous organisms. From a previous transcriptomic dataset from Bursaphelenchus xylophilus, a plant-parasitic nematode that infects forest tree species, we identified 96 transcripts potentially as ShK domain-containing proteins with unknown function in the nematode genome. This study aimed to characterize and explore the functional role of genes encoding ShK domain-containing proteins in B. xylophilus biology. We selected and functionally analyzed nine candidate genes that are putatively specific to B. xylophilus. In situ hybridization revealed expression of one B. xylophilus ShK in the pharyngeal gland cells, suggesting their delivery into host cells. Most of the transcripts are highly expressed during infection and showed a significant upregulation in response to peroxide products compared to the nematode catalase enzymes. We reported, for the first time, the potential involvement of ShK domain genes in oxidative stress, suggesting that these proteins may have an important role in protecting or modulating the reactive oxygen species (ROS) activity of the host plant during parasitism. Full article
(This article belongs to the Special Issue Plant Diseases and Sustainable Agriculture)
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