Precision Farming: GIS and Remote Sensing for Crop Management Optimization

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 405

Special Issue Editors


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Guest Editor
Computer Science, Federal University of Technology—Paraná, 85.722-332 Medianeira, Brazil
Interests: precision agriculture; geostatistics; remote sensing; development software

E-Mail Website
Guest Editor
Computer Science, Federal University of Technology—Paraná, Medianeira, Brazil
Interests: precision agriculture; database; development systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Computer Science, Federal University of Technology—Paraná, 85.722-332 Medianeira, Brazil
Interests: precision agriculture; development systems; remote sensing

Special Issue Information

Dear Colleagues,

The advancement of technologies for the development of systems and the interpretation of information on geographic representation and remote sensing has allowed for progress in understanding soil and vegetation conditions, enabling the rational management of productive areas and the optimization of agricultural production. In this perspective, this special edition, entitled “Precision Farming: GIS and Remote Sensing for Crop Management Optimization”, seeks to bring together studies that highlight the strategic role of computational tools for capturing, processing, and interpreting agricultural data in improving sustainable and intelligent practices aimed at better land use in agricultural practice.

The topics to be addressed include the following:

  • Data integration in the context of precision agriculture;
  • Satellite images, obtained by drones and field sensors, and their importance in crop monitoring and management;
  • Computational models for interpreting spatial data collected in the field;
  • Specialized software and platforms used in the context of precision agriculture and remote sensing;
  • Integration of spatial and temporal information in the context of operational, economic, and environmental efficiency;
  • Use of methodologies based on big data, artificial intelligence and machine learning applied to precision agriculture;

In this Special Issue, the journal reaffirms its commitment to promoting the dissemination of scientific and technological knowledge focused on precision agriculture, and it is expected that the articles will inspire new research and collaborations, strengthening the role of GIS and remote sensing for crop management optimization.

Prof. Dr. Cláudio Leones Bazzi
Prof. Dr. Kelyn Schenatto
Prof. Dr. Ricardo Sobjak
Guest Editors

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Keywords

  • precision agriculture
  • remote sensing
  • GIS

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Published Papers (1 paper)

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Research

23 pages, 4500 KB  
Article
Spatial Modelling of Soil Quality and Lime Requirement for Precision Management in Humid Tropical Coffee Systems
by Henry Diaz-Chuquizuta, Sharon Mejia, Ruth Mercado, Michell K. Arroyo-Julca, Ruddy Ore, Percy Diaz-Chuquizuta, Luis Fernando Manrique Gonzales, Martín Sánchez-Ojanasta and Kenyi Quispe
AgriEngineering 2026, 8(3), 79; https://doi.org/10.3390/agriengineering8030079 - 25 Feb 2026
Viewed by 252
Abstract
Soil heterogeneity and acidity are major constraints to Coffea arabica production in the Amazonian soils of Peru. This study developed a spatial predictive framework that integrates a weighted Soil Quality Index (SQIw) and geostatistical modelling (Regression–Kriging and Ordinary Kriging) to estimate lime requirements [...] Read more.
Soil heterogeneity and acidity are major constraints to Coffea arabica production in the Amazonian soils of Peru. This study developed a spatial predictive framework that integrates a weighted Soil Quality Index (SQIw) and geostatistical modelling (Regression–Kriging and Ordinary Kriging) to estimate lime requirements (LRs) and delineate management zones. A total of 69 coffee-cultivated soil samples were analysed, and spectral information (NDVI) was incorporated to estimate relative yield (RR). Multivariate analysis defined a Minimum Data Set (MDS) composed of exchangeable Na, available P, pH and silt percentage; the highest weights were assigned to P (Wi = 0.292) and pH (Wi = 0.276). SQIw exhibited wide variability (0.01–0.87; CV = 51.8%) and was grouped into five classes, with low (43.5%)- and very low (21.7%)-quality classes predominating. SQIw showed a strong relationship with RR (r = 0.64). Geostatistical models performed differently between localities: in Nuevo Huancabamba, Regression–Kriging improved prediction accuracy (SQIw: R2 = 0.58; LR: R2 = 0.396), whereas in San José de Sisa, Ordinary Kriging provided better fits only for LRs (R2 = 0.32). Nuevo Huancabamba is dominated by moderate-to-high-quality soils (87.29%; SQIw > 0.6) and low lime requirements (74.94%; <0.84 t ha−1), in contrast with San José de Sisa, where low-quality soils prevail (89.45%; SQIw < 0.4) alongside high LRs (75.26%; 2.54–7.13 t ha−1). The resulting maps enable targeted interventions—precision liming and focused P fertilisation—to correct acidity and phosphorus deficiency, thereby improving input-use efficiency and enhancing the sustainability of Amazonian coffee systems. Full article
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