Intelligent Systems Supporting the Assessment of Soil Influence on Crop Yields
A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land, Soil and Water".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 17
Special Issue Editors
Interests: potato production; yield prediction; ANN; geochemistry; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: yield modelling; machine learning; artificial neural networks; artificial intelligence; prediction; soil health; soil management; plant extracts; seed germination; plant analysis
Special Issue Information
Dear Colleagues,
The evaluation of the physical and chemical properties of soils and their effects on plant yield is a key area of research in the fields of agriculture, soil science, and environmental protection. The complexity of the relationships between soil components and plant growth and development requires the use of advanced analytical methods to effectively model and predict the effect of these variables, and developments in geo-information technologies, including remote sensing and geographic information systems (GISs), together with advances in machine learning and artificial intelligence, have opened up new possibilities in the analysis and interpretation of soil and plant data. Despite these advances, it is necessary to further develop methods that integrate diverse data sources and analytical techniques to better understand these complex natural relationships.
The aim of this Special Issue is to bring together original and review articles that explore applications of artificial intelligence and computational methods in assessing the impact of physical and chemical properties of soils on crop yield. This work should be guided by support for the development of data-driven predictive and decision-making tools to better manage natural resources and optimise crop yields. The publications reported in this Special Issue will aim to advance knowledge on the integration of modern analytical techniques with soil–plant-environmental research, in line with Land’s mission as a multidisciplinary forum in the field of earth and environmental sciences.
This Special Issue will welcome manuscripts that link the following themes:
- Using artificial intelligence and machine learning methods in the analysis of soil properties;
- The predictive modelling of plant growth and yield outcomes from soil data;
- Applications of remote sensing and GIS in soil–plant interaction research;
- Data-driven decision support systems for agriculture and sustainable land management;
- Reviews and meta-analyses on AI solutions in soil–plant relationship research.
We look forward to receiving your original research articles and reviews.
Dr. Magdalena Piekutowska
Dr. Patryk Hara
Guest Editors
Manuscript Submission Information
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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. Land 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.
Keywords
- artificial intelligence
- soil physics
- plant yield
- predictive modelling
- geographical information systems (GIS)
- geochemistry
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