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Horticulturae
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Advances in Disease Diagnostics and Pathogen Biocontrol of Horticulture Crops:...
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Advances in Disease Diagnostics and Pathogen Biocontrol of Horticulture Crops: 2nd Edition

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Plant Pathology and Disease Management (PPDM)".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 2033

Editors

Prof. Dr. Graciela Dolores Ávila-Quezada

E-Mail Website
Guest Editor
Facultad de Ciencias Agrotecnológicas, Universidad Autónoma de Chihuahua, Chihuahua 31350, Mexico
Interests: nanoparticles for controlling plant pathogens; plant health; nanophytopathology; nanotechnological tools
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Harald Scherm

E-Mail Website
Guest Editor
Department of Plant Pathology, University of Georgia, 120 Carlton Street, Athens, GA 30602, USA
Interests: plant pathogen biology; epidemiology, and integrated disease management; diseases of fruit and vegetable crops and their control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the tremendous success of the first edition of the Special Issue “Advances in Disease Diagnostics and Pathogen Biocontrol of Horticulture Crops” (https://www.mdpi.com/journal/horticulturae/special_issues/SH53K2N3H5), we are eager to further advance research in this area.

Disease diagnosis and pathogen biocontrol are among the most important agricultural issues with regard to food safety. They are so important that the UN included zero hunger as a Sustainable Development Goal in the 2030 Agenda. The process of disease diagnosis has undergone great changes due to the use of new sensitive and precise technologies that help us identify emerging diseases in crops in a short time. The purpose of this Special Issue, "Advances in Disease Diagnostics and Pathogen Biocontrol of Horticulture Crops: 2nd Edition", is to present innovative plant disease diagnostic and biocontrol studies, tools, approaches, and techniques that have been successfully applied in food production. We are looking for the most innovative, precise, and rapid methods and equipment for the diagnosis of phytopathogens in the laboratory and in the field. We will also accept papers that address various biocontrol mechanisms and the most promising agents for the control of phytopathogens in order to ensure the production of high-quality, nutritious foods.

Prof. Dr. Graciela Dolores Ávila-Quezada
Prof. Dr. Harald Scherm
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Horticulturae 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 2200 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 pathogen detection
  • nanobiosensors
  • new-generation sequencing technology
  • biocontrol strategies
  • reprogramming of plant defense
  • biocontrol agents
  • plant pathogens
  • emerging pathogens
  • quarantined pathogens

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Review

25 pages, 2897 KB  
Review
Integrating UAVs and Deep Learning for Plant Disease Detection: A Review of Techniques, Datasets, and Field Challenges with Examples from Cassava
by Wasiu Akande Ahmed, Olayinka Ademola Abiola, Dongkai Yang, Seyi Festus Olatoyinbo and Guifei Jing
Horticulturae 2026, 12(1), 87; https://doi.org/10.3390/horticulturae12010087 - 12 Jan 2026
Cited by 2 | Viewed by 1625
Abstract
Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes [...] Read more.
Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection. It examines UAV platforms, sensor technologies, flight protocols, image preprocessing pipelines, DL architectures, and existing datasets, and it evaluates how these components interact within UAV–DL disease-monitoring frameworks. The review also compares model performance across convolutional neural network-based and Transformer-based architectures, highlighting metrics such as accuracy, recall, F1-score, inference speed, and deployment feasibility. Persistent challenges—such as limited UAV-acquired datasets, annotation inconsistencies, geographic model bias, and inadequate real-time deployment—are identified and discussed. Finally, the paper proposes a structured research agenda including lightweight edge-deployable models, UAV-ready benchmarking protocols, and multimodal data fusion. This review provides a consolidated reference for researchers and practitioners seeking to develop practical and scalable cassava-disease detection systems. Full article
(This article belongs to the Special Issue Advances in Disease Diagnostics and Pathogen Biocontrol of Horticulture Crops: 2nd Edition)
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