AI-Enabled Precision Agriculture for Crop Monitoring and Yield Prediction Using Remote Sensing Technologies

Dear Colleagues,

Precision agriculture has emerged as a transformative approach to address the growing challenges of global food security, climate variability, and sustainable resource management. Recent advances in remote sensing technologies—including satellite imagery, unmanned aerial vehicles (UAVs), and proximal sensors—have enabled continuous, large-scale, and non-destructive monitoring of crop conditions. When combined with artificial intelligence (AI) and advanced data analytics, these technologies allow for accurate crop monitoring, stress detection, and yield prediction at unprecedented spatial and temporal resolutions. AI-driven models can integrate multi-source, multi-temporal remote sensing data to capture complex crop–environment interactions, supporting timely and informed decision-making. This Special Issue, “AI-Enabled Precision Agriculture for Crop Monitoring and Yield Prediction Using Remote Sensing Technologies”, will highlight a rapidly evolving research area that is critical for improving agricultural productivity, resilience, and sustainability in the face of climate change and increasing demand for food.

The aim of this Special Issue is to provide a dedicated platform for cutting-edge research that integrates AI, machine learning, and remote sensing for precision agriculture applications. It seeks to bridge methodological advances with practical agricultural outcomes, emphasizing scalable and data-driven solutions. This issue aligns closely with the journal’s scope by advancing innovative sensing, modeling, and analytical techniques that support sustainable agricultural systems, environmental monitoring, and data-intensive decision support. Suggested articles may address, but are not limited to, the following topics:

• Precision yield monitoring;
• AI-based crop yield prediction models;
• Crop stress, disease, and nutrient deficiency detection;
• Multi-sensor and multi-scale data fusion;
• Deep learning for time-series remote sensing analysis;
• Decision-support systems for precision farming.

Dr. Abid Ali
Prof. Dr. Hans-Peter Kaul
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-anonymized 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.