National-Scale Orchard Mapping and Yield Estimation in Pakistan Using Object-Based Random Forest and Multisource Satellite Imagery.

Ansar Ali, Ibrar Ul Hassan Akhtar, Maisam Raza, Amjad Ali

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ansar AliSAR Wing, Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), SUPARCO HQ, Islamabad Expressway, Islamabad P.O. Box 1271, Pakistan.ORCID 0000-0001-7045-4970

Ibrar Ul Hassan AkhtarSAR Wing, Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), SUPARCO HQ, Islamabad Expressway, Islamabad P.O. Box 1271, Pakistan.

Maisam RazaSAR Wing, Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), SUPARCO HQ, Islamabad Expressway, Islamabad P.O. Box 1271, Pakistan.

Amjad AliSAR Wing, Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), SUPARCO HQ, Islamabad Expressway, Islamabad P.O. Box 1271, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate geospatial inventories of fruit orchards are essential for precision horticulture and food security, yet Pakistan lacks consistent spatial datasets at district and tehsil levels. This study presents the first national-scale, object-based Random Forest (RF) framework for orchard delineation and yield estimation by integrating multi-temporal Sentinel-2 imagery on Google Earth Engine (GEE) with high-resolution Pakistan Remote Sensing Satellite-1 (PRSS-1) data. Among the tested classifiers, RF achieved the highest performance on Sentinel-2 data (Overall Accuracy (OA) = 79.0%, kappa (κ) = 0.78), outperforming Support Vector Machines (OA = 74.5%, κ = 0.74) and Gradient Boosting Decision Trees (OA = 73.8%, κ = 0.73), with statistical significance confirmed (McNemar's χ

Indexed as

Google Earth Enginemulti-sensor satellite imageryOBIAorchard delineationPakistanRandom Forestyield modeling

Identifiers

PMID41471463

DOI10.3390/s25247468

PMCPMC12736942

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.