Remote Sensing and AI Algorithms for Plant Disease and Tree Health Detection

Dear Colleagues,

The combination of remote sensing and AI algorithms have become a research hotspot in the last five years, especially after the  entry of artificial intelligence and UAV in commercial applications for Precision Agriculture domain. For large-scale agricultural and forestry monitoring, data from multiple sensors such as hyperspectral camera, multi-spectral camera, airborne thermal infrared imager and LiDAR, are becoming the main data source, combined effectively with AI algorithms comprise the prevalent data analysis methods, both serving smart applications in agriculture replacing the traditional time-consuming methods for crop and trees health status assessment. Using remote sensing and artificial intelligence algorithms to detect early crop diseases and tree health status at its early stage remains a challenge.  The current special issue aims at novel approaches  that employ efficiently AI algorithms with  several remote sensing methods for crop and trees health status assessment.

This Special Issue aims at studies covering different analysing and AI modelling of remote sensing data acquired by different sensors and platforms. Topics may cover any application regarding from the crop disease assessment, and tree health monitoring. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches or studies focused on large scale monitoring in agriculture and forestry, among other issues, are welcome. Articles may address, but are not limited, to the following topics:

• Detection of crop disease
• Detection of tree disease and health
• Monitoring of infection degree of crop diseases and insect pests
• UAV remote sensing platform and application
• Satellite remote sensing data analysis for crop disease and tree health status
• Discrimination of biotic and abiotic stress in crops and trees
• AI applications for crop and trees protection

Remote sensing and AI algoriths with application to agricultural domain are the suggested themes.

Research articles, review articles as well as short communications are invited.

Dr. Xanthoula Eirini Pantazi
Dr. Xiaoling Deng
Dr. Jian Zhang
Prof. Dr. Dimitrios Moshou
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.