| Full text | |
| Author(s): |
Melo, Derlei D.
;
Cunha, Isabella A.
;
Amaral, Lucas R.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | AGRIENGINEERING; v. 7, n. 12, p. 21-pg., 2025-12-05. |
| Abstract | |
Segmenting agricultural fields into management zones (MZ) is a core principle of precision agriculture (PA). However, the widespread adoption of PA remains limited, partly due to operational barriers in MZ segmentation. These barriers often involve the necessity for advanced programming skills and a strong statistical background, in addition to the lack of a free, integrated and straightforward tool that executes the entire workflow. Addressing this gap required the development of the open-source QGIS plugin Precision Zones. The plugin reproducibly implements the entire MZ segmentation pipeline: (i) raster layers preprocessing; (ii) dimensionality reduction via Principal Component Analysis (PCA); (iii) multivariate clustering using K-Means ++, with integrated support for determining the optimal number of zones through the Elbow and Silhouette methods; (iv) spatial filtering of MZ to mitigate noise; and (v) assessment of MZ agronomic effectiveness using statistical metrics (i.e., within-zone variance reduction). This tool enables practical MZ segmentation for a wide range of agricultural applications, eliminating the need for programming knowledge. Despite its robust architecture, as a novel tool, it has not yet been formally characterized and presented to the scientific community. Therefore, this study describes the Precision Zones plugin, address the step-by-step user decisions and presents its validation. In a reproducible case study, the plugin produced agronomically coherent MZ and reduced within-zone variability (VR%) for most soil attributes analyzed. The study concludes that Precision Zones provides a reproducible, user-friendly workflow that bridges the gap between advanced spatial analysis and practical precision agriculture applications for growers, consultants and researchers. (AU) | |
| FAPESP's process: | 22/03160-8 - Soil spatial variability mapping and optimized sampling supported by sensing techniques: bases for a more efficient and sustainable precision agriculture |
| Grantee: | Lucas Rios do Amaral |
| Support Opportunities: | Research Grants - Initial Project |
| FAPESP's process: | 24/14044-4 - Detailed pedological mapping and management zones: characteristics, applications, and complementarity between approaches |
| Grantee: | Derlei Dias Melo |
| Support Opportunities: | Scholarships in Brazil - Doctorate |