Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices—2nd Edition

Dear Colleagues,

In the endeavor to achieve sustainable agriculture, the integration of remote sensing (RS) and machine learning (ML) technologies presents an invaluable opportunity as these burgeoning technologies enable a data-driven approach to agricultural practices, ensuring the optimized utilization of resources, and a substantial reduction in environmental degradation. Remote sensing facilitates the real-time monitoring of crop and soil conditions from a distance, while machine learning provides the tools to analyze these vast datasets, uncovering patterns and insights that can guide sustainable agricultural decisions.

The nexus of RS and ML not only supports the monitoring and management of agricultural resources but also plays a critical role in addressing challenges like pest infestations, water scarcity, and nutrient management. By developing predictive models, it is possible to anticipate issues before they escalate, allowing for timely interventions. Furthermore, these technologies aid in the precision application of inputs such as water, fertilizers, and pesticides, ensuring that the agricultural footprint is minimized while productivity is enhanced.

This Special Issue invites original, quantitative, and comprehensive studies exploring the application of remote sensing and machine learning in sustainable agriculture. Submissions spanning a wide array of crops and agricultural systems, under both field and controlled environmental conditions, are welcomed. We are particularly interested in manuscripts addressing the following topics: (1) the development and validation of RS and ML models for crop monitoring as well as pest and disease detection; (2) the application of RS and ML in precision irrigation and water resource management; (3) the utilization of RS and ML for soil health assessment and nutrient management; (4) the assessment of the economic and environmental impacts of RS and ML applications on agriculture; and (5) case studies showcasing the successful integration of RS and ML in sustainable agricultural practices.

Dr. Sara Álvarez
Dr. Sergio Vélez
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.

• remote sensing
• machine learning
• sustainable agriculture
• digital agriculture
• precision irrigation
• crop monitoring
• pest and disease detection
• water resource management
• soil and nutrient assessment
• redictive modeling

• Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices in Agronomy (6 articles)