Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Plant Disease Detection and Recognition Using Remotely Sensed Data
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Plant Disease Detection and Recognition Using Remotely Sensed Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 1 July 2025 | Viewed by 6220

Special Issue Editors

Dr. Huajian Liu

E-Mail Website
Guest Editor
The Plant Accelerator, Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Building WT 40, Hartley Grove, Adelaide, SA 5064, Australia
Interests: machine vision and machine learning for plant phenotyping and precision agriculture; plant nutrient estimation; plant disease detection; drought and salt stress tolerance; plant growing status estimation; invertebrate pest detection
Special Issues, Collections and Topics in MDPI journals
Dr. Haiguang Wang

E-Mail Website
Guest Editor
Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China
Interests: plant pathology; epidemiology; disease monitoring; disease prediction; disease image recognition; smart phytoprotection; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant diseases pose significant threats to crop yields and forest health globally, exacerbated by factors like extreme weather events and climate change. Detecting and recognising these diseases early in their development stages, in a high-throughput and non-destructive manner, is critical for effective management. Remote sensing technologies have witnessed remarkable advancements in plant disease detection and recognition over the last decade, ranging from ground-based vehicles to satellite platforms, employing various sensing methods such as RGB imaging, hyperspectral imaging, thermal imaging, fluorescent technologies, spectroscopy technologies, and LiDAR techniques, coupled with sophisticated data processing methods.

This Special Issue aims to showcase the state-of-the-art methods for detecting and recognising plant diseases by sensing the biological and physiological stress induced by pathogens in plants using remotely sensed data. We invite contributions covering a wide range of topics, including, but not limited to, the following:

  • Application of hyperspectral, multispectral, thermal imaging and LiDAR for plant health assessment.
  • Integration of multi-source remotely sensed data for enhanced disease identification.
  • Novel algorithms for plant disease detection and recognition.
  • New machine learning and deep learning models for automated disease detection and recognition.
  • Case studies and real-world applications showcasing the effectiveness of remote sensing in combating plant diseases.

This Special Issue seeks to foster knowledge exchange and advance the field of remote sensing for plant disease detection and recognition. We encourage submissions of original research articles or reviews that contribute to the understanding and implementation of remote sensing technologies in combating plant diseases.

Dr. Huajian Liu
Dr. Haiguang Wang
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. Remote Sensing 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

  • remote sensing
  • plant disease detection
  • plant disease recognition
  • computer vision
  • machine learning
  • deep learning
  • image processing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 9801 KiB  
Article
Large-Scale Monitoring of Potatoes Late Blight Using Multi-Source Time-Series Data and Google Earth Engine
by Zelong Chi, Hong Chen, Sheng Chang, Zhao-Liang Li, Lingling Ma, Tongle Hu, Kaipeng Xu and Zhenjie Zhao
Remote Sens. 2025, 17(6), 978; https://doi.org/10.3390/rs17060978 - 11 Mar 2025
Viewed by 770
Abstract
Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on a large scale. The method combines unsupervised and supervised machine learning algorithms. To improve the monitoring accuracy of the [...] Read more.
Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on a large scale. The method combines unsupervised and supervised machine learning algorithms. To improve the monitoring accuracy of the PLB regression model, the study used the K-Means algorithm in conjunction with morphological operations to identify potato growth areas. Input data consisted of monthly NDVI from Sentinel-2 and VH bands from Sentinel-1 (covering the year 2021). The identification results were validated on 221 field survey samples with an F1 score of 0.95. To monitor disease severity, we compared seven machine learning models: CART decision trees (CART), Gradient Tree Boosting (GTB), Random Forest (RF), single optical data Random Forest Time series model (TS–RF), single radar data Random Forest Time series model (STS–RF), multi-source data Gradient Tree Boosting Time series model (MSTS–GTB), and multi-source data Random Forest Time series model (MSTS–RF). The MSTS–RF model was the best performer, with a validation RMSE of 20.50 and an R² of 0.71. The input data for the MSTS–RF model consisted of spectral indices (NDVI, NDWI, NDBI, etc.), radar features (VH-band and VV-band), texture features, and Sentinel-2 bands synthesized as a monthly time series from May to September 2021. The feature importance analysis highlights key features for disease identification: the NIR band (B8) for Sentinel-2, DVI, SAVI, and the VH band for Sentinel-1. Notably, the blue band data (458–523 nm) were critical during the month of May. These features are related to vegetation health and soil moisture are critical for early detection. This study presents for the first time a large-scale map of PLB distribution in China with an accuracy of 10 m and an RMSE of 26.52. The map provides valuable decision support for agricultural disease management, demonstrating the effectiveness and practical potential of the proposed method for large-scale monitoring. Full article
(This article belongs to the Special Issue Plant Disease Detection and Recognition Using Remotely Sensed Data)
►▼ Show Figures

Figure 1

23 pages, 4075 KiB  
Article
Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
by Andrés Hirigoyen and José Villacide
Remote Sens. 2025, 17(3), 537; https://doi.org/10.3390/rs17030537 - 5 Feb 2025
Viewed by 844
Abstract
Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales [...] Read more.
Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring. Full article
(This article belongs to the Special Issue Plant Disease Detection and Recognition Using Remotely Sensed Data)
►▼ Show Figures

Figure 1

21 pages, 23534 KiB  
Article
GVC-YOLO: A Lightweight Real-Time Detection Method for Cotton Aphid-Damaged Leaves Based on Edge Computing
by Zhenyu Zhang, Yunfan Yang, Xin Xu, Liangliang Liu, Jibo Yue, Ruifeng Ding, Yanhui Lu, Jie Liu and Hongbo Qiao
Remote Sens. 2024, 16(16), 3046; https://doi.org/10.3390/rs16163046 - 19 Aug 2024
Cited by 6 | Viewed by 3307
Abstract
Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing [...] Read more.
Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing devices for real-time processing of cotton aphid-damaged leaves captured by field cameras holds significant practical research value for large-scale disease and pest control measures. The mainstream detection models are generally large in size, making it challenging to achieve real-time detection on edge computing devices with limited resources. In response to these challenges, we propose GVC-YOLO, a real-time detection method for cotton aphid-damaged leaves based on edge computing. Building upon YOLOv8n, lightweight GSConv and VoVGSCSP modules are employed to reconstruct the neck and backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. In the backbone network, we integrate the coordinate attention (CA) mechanism and the SimSPPF network to increase the model’s ability to extract features of cotton aphid-damaged leaves, balancing the accuracy loss of the model after becoming lightweight. The experimental results demonstrate that the size of the GVC-YOLO model is only 5.4 MB, a decrease of 14.3% compared with the baseline network, with a reduction of 16.7% in the number of parameters and 17.1% in floating-point operations (FLOPs). The mAP@0.5 and mAP@0.5:0.95 reach 97.9% and 90.3%, respectively. The GVC-YOLO model is optimized and accelerated by TensorRT and then deployed onto the embedded edge computing device Jetson Xavier NX for detecting cotton aphid damage video captured from the camera. Under FP16 quantization, the detection speed reaches 48 frames per second (FPS). In summary, the proposed GVC-YOLO model demonstrates good detection accuracy and speed, and its performance in detecting cotton aphid damage in edge computing scenarios meets practical application needs. This research provides a convenient and effective intelligent method for the large-scale detection and precise control of pests in cotton fields. Full article
(This article belongs to the Special Issue Plant Disease Detection and Recognition Using Remotely Sensed Data)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop