Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Reliable Orchard Mapping in Data-Scarce Landscapes

Articles in Press

Document Type : Research Article- En

Authors

N. Ahmadi Sani 1
S. Moradi 2
J. Henareh 3
M. Pato 3

1 Institute of Agriculture, Water, Food, and Nutraceuticals, Mah. C., Islamic Azad University, Mahabad, Iran

2 Department of Agricultural & Natural Resources Development, Faculty of Engineering, Payam Noor University, Tehran, Iran

3 Forests and Rangelands Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Urmia, Iran

https://doi.org/10.22067/jam.2026.96725.1449
Abstract
This study assesses the capability of Sentinel-2A imagery combined with machine-learning classifiers on the Google Earth Engine platform for accurate orchard mapping in the environmentally stressed Sharviran Plain along the southern margin of Lake Urmia, Iran. Random Forest, Support Vector Machine, and Classification and Regression Trees were applied to multispectral bands and selected spectral and vegetation indices, using 836 systematically collected samples from high-resolution imagery and field surveys for training and validation. The study specifically focuses on orchard mapping, with particular emphasis on reliably distinguishing orchards from other agricultural and non-agricultural land-use classes. Results show that orchards cover approximately 11% of the study area, and that the Random Forest classifier achieved the highest performance (Kappa = 0.84) and the best orchard F1-score, demonstrating superior class-level discrimination. Validation against 2023 ground-based orchard statistics showed a low error margin of 2.2%, confirming the reliability of the approach for operational orchard mapping. These findings confirm that integrating Sentinel-2A imagery with machine-learning classifiers on cloud-based platforms offers a robust, reproducible, and cost-efficient framework for orchard mapping in data-scarce and drought-affected regions. The resulting high-resolution orchard maps can effectively support agricultural planning, irrigation management, and evidence-based policy-making in the Lake Urmia basin. Future work may further improve performance by incorporating multi-temporal imagery, additional ecological and socio-economic variables, and advanced models such as deep learning.

Keywords

Machine learning
Orchard mapping
Sentinel 2
Urmia Lake
Subjects

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 14 February 2026

  • Receive Date 26 November 2025
  • Revise Date 13 January 2026
  • Accept Date 14 January 2026
  • First Publish Date 14 February 2026

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