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Remote Sensing
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Plant Disease Detection and Recognition Using Remotely Sensed Data
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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: 15 December 2024 | Viewed by 2108

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

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Published Papers (1 paper)

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Research

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
Viewed by 1408
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 [email protected] and [email protected]: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)
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