Geospatial Artificial Intelligence (GeoAI) Applications in Agriculture for Smart Farming Solutions

Dear Colleagues,

Background

Studies using artificial intelligence and spatial datasets for crop monitoring have become important and gained interest among scientists worldwide. The rich historical archives and continuing acquisition of earth observation datasets provide opportunities for crop monitoring at local, regional, and global scales in response to the impacts of climate change. In addition, recent advances and applications of artificial intelligence algorithms make it possible to process many spatiotemporal datasets for crop growth and damage assessment, crop health analysis, crop yield and water requirements, and crop yield forecasting, which is extremely important for agronomists to devise successful strategies for a country to address food security issues.

Aim and scope

This Special Issue aims to collect research manuscripts related to the applications of artificial intelligence and earth observation datasets for such crop monitoring purposes at different scales across the globe. The topics include, but are not limited to, the following aspects: 

• Geospatial analysis for precision irrigation in smart farming: Explore using GeoAI in optimizing irrigation strategies by integrating geospatial data, weather patterns, and machine learning algorithms to enhance water efficiency and crop yield.
• Spatial data fusion approaches for comprehensive agricultural insights: Explore methodologies for integrating various datasets (e.g., geospatial, weather, soil, and crop information) using advanced data fusion techniques for informed decision making.
• Integrating geospatial data and AI for smart crop monitoring: Investigate how remotely sensed data, coupled with AI, can revolutionize crop monitoring techniques for crop health, growth, and potential yield prediction.
• Pest and disease detection in precision agriculture: Exploring the application of GeoAI technologies (e.g., computer vision and machine learning) to detect and diagnose crop diseases and pest infestations for early intervention and sustainable pest management in smart farming.

Data-driven climate risk assessment for precision agriculture: Develop data-driven approaches to assess climate risks in precision agriculture and how predictive modeling and machine learning can help farmers anticipate and mitigate the impact of climate-related challenges on crop yields and overall farm productivity.

Dr. Nguyenthanh Son
Dr. Chien-Hui Syu
Dr. Cheng-Ru Chen
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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.

• geospatial artificial intelligence
• crop monitoring
• agricultural systems
• yield forecasting
• crop type mapping
• crop yield and water requirements
• crop health and damage assessment
• climate risk assessment