Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 1167
Special Issue Editors
Interests: GIS (geographic information systems); remote sensing; machine learning; predictive modeling and mapping; cropland suitability assessment; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; precision agriculture; multicriteria analysis; farming and cropping systems; inventory of natural resources
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; precision agriculture; drones; geoinformation technologies; land use management; agricultural engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the increasing demand for efficient and sustainable food production, the evaluation of cropland suitability using geospatial technologies has become a critical research topic. The history of this field is rooted in the development of land evaluation frameworks, evolving from traditional soil-based assessments to modern approaches that integrate remote sensing, GIS, and machine learning. Today, these methods enable more accurate, timely, and scalable assessments of land potential while addressing challenges posed by climate change, resource limitations, and environmental degradation.
This Special Issue will provide a platform for state-of-the-art research on spatial methods and models that advance cropland evaluation and yield prediction. The scope encompasses interdisciplinary contributions bridging agriculture, environmental science, and geoinformatics to support sustainable land use planning and precision farming practices.
We particularly welcome papers that explore novel applications of remote sensing, GIS, and big data analytics in cropland suitability mapping, yield prediction, and resource optimization. Research incorporating machine learning, multi-sensor data fusion, and phenological modeling is of high interest. Case studies demonstrating practical applications in diverse agroecosystems worldwide are strongly encouraged.
Dr. Dorijan Radočaj
Prof. Dr. Mladen Jurišić
Dr. Ivan Plaščak
Guest Editors
Manuscript Submission Information
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Keywords
- cropland suitability
- crop yield prediction
- geospatial data
- remote sensing
- GIS
- precision agriculture
- machine learning
- phenological modeling
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