Soil Organic Matter and Carbon Content Analysis Using Machine Learning

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue of Applied Sciences entitled "Soil Organic Matter and Carbon Content Analysis Using Machine Learning". Soil organic analysis is of paramount importance for understanding soil health, fertility, and overall ecosystem dynamics. Traditional methods for soil analysis are often labor-intensive and time-consuming, limiting the scale and depth of studies. However, recent advancements in machine learning techniques coupled with innovative sensing technologies have opened up new avenues for rapid and accurate soil organic analysis. This Special Issue aims to explore the intersection of soil science and machine learning, offering insights into advanced methods for soil organic analysis or mapping.

This Special Issue aims to bridge the gap between soil science and machine learning by showcasing cutting-edge research in soil organic analysis. By harnessing the power of machine learning algorithms, researchers can analyze large datasets derived from various sensing platforms, such as satellite imagery, drone cameras, and spectroscopic techniques. The resulting insights can revolutionize our understanding of soil properties, enabling more informed decision-making in agriculture, environmental monitoring, and land management. This topic aligns closely with the scope of Applied Sciences, which welcomes interdisciplinary research at the intersection of engineering, technology, and applied sciences. Through this Special Issue, we aspire to gather a diverse collection of articles that will push the boundaries of soil analysis methodologies.

Suggested themes and article types for submissions:
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• The development of machine learning algorithms for soil organic analysis;
• The integration of remote-sensing data into machine learning techniques for soil mapping;
• Applications of drone cameras and other advanced sensing technologies for soil characterization;
• The merging of several advanced detection technologies;
• Case studies demonstrating the efficacy of machine learning in soil organic analysis;
• Challenges and future directions in the field of soil science and machine learning.

We look forward to receiving your contributions.

Dr. Yaping Xu
Dr. Rafael López Núñez
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. Applied Sciences 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 2400 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.

• soil science
• machine learning
• remote sensing
• soil mapping
• organic analysis
• drone technology
• spectroscopic analysis
• proximal soil-sensing techniques