Plant Disease Detection and Recognition Using Remotely Sensed Data

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 and
• 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.

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