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Plants
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Sustainable Strategies for Managing Plant Diseases
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Sustainable Strategies for Managing Plant Diseases

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 December 2024 | Viewed by 2166

Special Issue Editor

Dr. El Jarroudi Moussa

E-Mail Website
Guest Editor
SPHERES Research Unit, Department of Environmental Sciences and Management, University of Liège, Arlon, Belgium
Interests: crop modelling; climate change; environmental impact assessment; sustainable agriculture; agrometeorology; monitoring; and remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue delves into cutting-edge research and practical applications aimed at minimizing the impact of plant pathogens while promoting ecological balance and agricultural sustainability. From advanced biotechnological interventions to eco-friendly farming practices, the issue covers a spectrum of strategies designed to enhance plant health, resilience, and productivity. Key topics include biocontrol agents, precision agriculture techniques, integrated pest management, genetic resistance breeding, and the utilization of beneficial microorganisms. By synthesizing interdisciplinary insights, the issue aims to foster collaboration among scientists, policymakers, and practitioners to address the global challenge of plant disease management in a sustainable and holistic manner.

Dr. El Jarroudi Moussa
Guest Editor

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

  • sustainable agriculture
  • plant pathology
  • disease management
  • biocontrol
  • precision farming
  • integrated pest management
  • genetic resistance
  • eco-friendly practices
  • microbial ecology
  • agricultural sustainability

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Published Papers (3 papers)

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Research

17 pages, 2452 KiB  
Article
Occurrence of Yam Mosaic Virus and Yam Mild Mosaic Virus on Dioscorea spp. Germplasm Collection in Cuba—Epidemiology of Associated Diseases
by José Efraín González Ramírez, Dariel Cabrera Mederos, Vaniert Ventura Chávez, Rosa Elena González Vázquez, Katia Ojito-Ramos, Liset García Romero, Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández, Elena Vicenta Hernández Navarro, Michel Leiva-Mora, Fabián Giolitti and Orelvis Portal
Plants 2024, 13(18), 2597; https://doi.org/10.3390/plants13182597 - 17 Sep 2024
Viewed by 485
Abstract
Potyvirus diseases are one of the main challenges facing the production of yam (Dioscorea spp.). The objective of this study was to identify the potyviruses present in the Dioscorea spp. germplasm collection at Instituto de Investigaciones de Viandas Tropicales (INIVIT) to establish [...] Read more.
Potyvirus diseases are one of the main challenges facing the production of yam (Dioscorea spp.). The objective of this study was to identify the potyviruses present in the Dioscorea spp. germplasm collection at Instituto de Investigaciones de Viandas Tropicales (INIVIT) to establish methodologies for the characterization of the associated diseases. For this purpose, immunochemical and molecular methods were used to identify the potyviruses present. The symptomatology of Dioscorea spp. at INIVIT’s germplasm collection was described. In addition, the severity and incidence in the germplasm collection and production areas were evaluated. As a result, the first report of yam mosaic virus (Potyvirus yamtesselati) and yam mild mosaic virus (Potyvirus yamplacidum) in Cuba is presented. The existence of resistant, tolerant, and susceptible cultivars to potyvirus-associated diseases in the germplasm collection was detected, and the incidence of these diseases was higher than 64% in the production areas evaluated. This study represents a step forward in the establishment of certification programs for propagating material of Dioscorea spp. in Cuba. Full article
(This article belongs to the Special Issue Sustainable Strategies for Managing Plant Diseases)
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21 pages, 4966 KiB  
Article
ICPNet: Advanced Maize Leaf Disease Detection with Multidimensional Attention and Coordinate Depthwise Convolution
by Jin Yang, Wenke Zhu, Guanqi Liu, Weisi Dai, Zhuonong Xu, Li Wan and Guoxiong Zhou
Plants 2024, 13(16), 2277; https://doi.org/10.3390/plants13162277 - 15 Aug 2024
Viewed by 578
Abstract
Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of [...] Read more.
Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model’s ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production. Full article
(This article belongs to the Special Issue Sustainable Strategies for Managing Plant Diseases)
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25 pages, 22045 KiB  
Article
A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds
by Jintao Liu, Chaoying He, Yichu Jiang, Mingfang Wang, Ziqing Ye and Mingfang He
Plants 2024, 13(13), 1827; https://doi.org/10.3390/plants13131827 - 3 Jul 2024
Cited by 1 | Viewed by 874
Abstract
Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize [...] Read more.
Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize leaf diseases and pests faces two key challenges: (1) In the actual process of identifying leaf diseases and pests, complex backgrounds can interfere with the identification effect. (2) The subtle features of diseases and pests are difficult to accurately extract. To address these challenges, this study proposes a maize leaf disease and pest identification model called LFMNet. Firstly, the localized multi-scale inverted residual convolutional block (LMSB) is proposed to perform preliminary down-sampling on the image, preserving important feature information for the subsequent extraction of fine disease and pest features in the model structure. Then, the feature localization bottleneck (FLB) is proposed to improve the model’s ability to focus on and locate disease and pest characteristics and to reduce interference from complex backgrounds. Subsequently, the multi-hop local-feature fusion architecture (MLFFA) is proposed, which effectively addresses the problem of extracting subtle features by enhancing the extraction and fusion of global and local disease and pest features in images. After training and testing on a dataset containing 19,451 images of maize leaf diseases and pests, the LFMNet model demonstrated excellent performance, with an average identification accuracy of 95.68%, a precision of 95.91%, a recall of 95.78%, and an F1 score of 95.83%. Compared to existing models, it exhibits significant advantages, offering robust technical support for the precise identification of maize diseases and pests. Full article
(This article belongs to the Special Issue Sustainable Strategies for Managing Plant Diseases)
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