Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems

Dear Colleagues,

With the evolution of orbital and proximal remote sensing technologies, a big data that must be converted to information is being generated in the agricultural sector. These data when analyzed with machine and deep learning approaches applied to remote sensing products have been recently used with success. The computational power using cloud based systems and recent advances on farm machinery equipments providing data collection, processing and analysis open up several opportunities of development and adoption of new technologies. Large scale on farm precision experimentation conducted in partnership with commercial farms and the appearence of new sensors on board of UAVs, crop duster airplanes and satelittes such as radar technologies that allow daily remote data collection under cloudy skies are exciting and require more investigation of several sorts. New equipment, sensors are enabling a better crop monitoring and land use map as well in a regional scale. The intent of this topical edition of Remote Sensing is to convey publications from collaborators that are working with a big pool of data that is being analyzed using deep and machine learning approaches in Precision Agriculture and also to improve regional scale remote sensing applications.

Prof. Dr. Carlos Antonio Da Silva Junior
Dr. Luciano Shiratsuchi
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 250 words) can be sent to the Editorial Office for assessment.

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.