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Agronomy
Special Issues
Current Status and Applications of Remote Sensing in Plant Pest...
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Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 994

Special Issue Editors

Prof. Dr. Jinfeng Wang

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: unmanned farm information perception and equipment; crop remote sensing and non-destructive testing; intelligent paddy field agricultural equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Zhihang Song

E-Mail Website
Guest Editor
Department of Horticulture, University of Georgia, 1111 Plant Sciences Building, Athens, GA 30602, USA
Interests: controlled-environment agriculture (CEA); plant phenotyping; indoor remote sensing; hyperspectral imaging; interpretable machine learning; imagery data mining
Dr. Jian Jin

E-Mail Website
Guest Editor
School of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: plant phenotyping; hyperspectral imaging; precision agriculture; plant sensor

Special Issue Information

Dear Colleagues,

Plant pests and diseases threaten agriculture by reducing yields, degrading food quality, and causing economic losses, jeopardizing global food security. Early detection and management are crucial for sustaining crop production. Remote sensing, with its efficiency, non-destructive nature, and real-time capabilities, is a powerful tool for monitoring plant health, offering advantages in accuracy, scale, and efficiency. Widely applied in agriculture, it enhances disease detection and management. Based on the above, we are pleased to announce a Special Issue of Agronomy on “Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection”, which will focus on the following:

  • The current state of remote sensing technologies in detecting plant pests and diseases;
  • The integration of multispectral, hyperspectral, and thermal imaging techniques for accurate pest and disease identification;
  • Innovative applications of remote sensing in crop health monitoring across various agricultural environments;
  • Challenges in achieving reliable pest and disease detection under diverse field conditions.

Prof. Dr. Jinfeng Wang
Dr. Zhihang Song
Dr. Jian Jin
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. Agronomy 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 2600 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

  • remote sensing
  • plant pests
  • plant diseases
  • crop health monitoring
  • hyperspectral imaging
  • multispectral imaging
  • precision agriculture
  • early detection
  • machine learning
  • pest and disease management

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

Published Papers (2 papers)

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Research

18 pages, 2644 KiB  
Article
Multispectral and Chlorophyll Fluorescence Imaging Fusion Using 2D-CNN and Transfer Learning for Cross-Cultivar Early Detection of Verticillium Wilt in Eggplants
by Dongfang Zhang, Shuangxia Luo, Jun Zhang, Mingxuan Li, Xiaofei Fan, Xueping Chen and Shuxing Shen
Agronomy 2025, 15(8), 1799; https://doi.org/10.3390/agronomy15081799 - 25 Jul 2025
Viewed by 189
Abstract
Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused [...] Read more.
Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article
(This article belongs to the Special Issue Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection)
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15 pages, 4666 KiB  
Article
Fusion of Medium- and High-Resolution Remote Images for the Detection of Stress Levels Associated with Citrus Sooty Mould
by Enrique Moltó, Marcela Pereira-Sandoval, Héctor Izquierdo-Sanz and Sergio Morell-Monzó
Agronomy 2025, 15(6), 1342; https://doi.org/10.3390/agronomy15061342 - 30 May 2025
Viewed by 395
Abstract
Citrus sooty mould caused by Capnodium spp. alters the quality of fruits on the tree and affects their productivity. Past laboratory and hand-held spectrometry tests have concluded that sooty mould exhibits a typical spectral response in the near-infrared spectrum region. For this reason, [...] Read more.
Citrus sooty mould caused by Capnodium spp. alters the quality of fruits on the tree and affects their productivity. Past laboratory and hand-held spectrometry tests have concluded that sooty mould exhibits a typical spectral response in the near-infrared spectrum region. For this reason, this study aims at developing an automatic method for remote sensing of this disease, combining 10 m spatial resolution Sentinel-2 satellite images and 0.25 m spatial resolution orthophotos to identify sooty mould infestation levels in small orchards, common in Mediterranean conditions. Citrus orchards of the Comunitat Valenciana region (Spain) underwent field inspection in 2022 during two months of minimum (August) and maximum (October) infestation. The inspectors categorised their observations according to three levels of infestation in three representative positions of each orchard. Two synthetic images condensing the monthly information were generated for both periods. A filtering algorithm was created, based on high-resolution images, to select informative pixels in the lower resolution images. The data were used to evaluate the performance of a Random Forest classifier in predicting intensity levels through cross-validation. Combining the information from medium- and high-resolution images improved the overall accuracy from 0.75 to 0.80, with mean producer’s accuracies of above 0.65 and mean user’s accuracies of above 0.78. Bowley–Yule skewness coefficients were +0.50 for the overall accuracy and +0.28 for the kappa index. Full article
(This article belongs to the Special Issue Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection)
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