Evidence map›Paper›PMID 41272231›Full record

ArticleScientific reports2025

Comparison of machine learning models for mapping Arecanut based agroforestry system in Goa by enhancing precision and efficiency.

A R Uthappa, Bappa Das, S B Chavan, Anurag Raizada, Sujeet Desai, V Paramesha, Parveen Kumar, Prakash Kumar Jha, P V Vara Prasad

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

A R UthappaICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
Bappa DasICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India. bappa.iari.1989@gmail.com.
S B ChavanICAR-National Institute of Abiotic Stress and Management, Baramati, India.
Anurag RaizadaICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
Sujeet DesaiICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
V ParameshaICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
Parveen KumarICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa, 403402, India.
Prakash Kumar JhaDepartment of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, 39762, USA.
P V Vara PrasadDepartment of Agronomy, Kansas State University, Manhattan, KS, 66506, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agroforestry plays a pivotal role in mitigating climate change and supporting rural livelihoods. Especially in coastal regions, it aids soil conservation, provides biophysical protection, and diversifies income sources. Through conventional approaches, mapping the arecanut-based agroforests is difficult. This study utilised machine learning models to identify arecanut based traditional agroforestry systems using Sentinel-2 satellite data in Goa, India. A total of 374 non-agroforestry and 70 agroforestry locations were collected for model training and testing. Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) were employed. The Boruta algorithm was applied for feature selection and to improve the model accuracy. The findings showed that the GBM model had significantly higher overall accuracy (0.86) and kappa (0.83) scores than other models during validation. The Boruta analysis revealed NDWI2, B3, and SLAVI as important variables indicating the importance of water indices and vegetation parameters in mapping agroforestry. The GBM model achieved high accuracy in identifying arecanut based agroforestry region amounting to 58.64 km

Indexed as

AgricultureForestryMachine LearningAlgorithmsConservation of Natural ResourcesIndiaSupport Vector MachineArea estimationGradient boosting machineKappaRandom forestSupport vector machine

Identifiers

PMID41272231
PMCPMC12722746

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