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A GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation

dc.contributor.authorBogdan Văduva
dc.contributor.authorAnca Avram
dc.contributor.authorOliviu Matei
dc.contributor.authorLaura Andreica
dc.contributor.authorTeodor Rusu
dc.contributor.otherDepartment of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
dc.contributor.otherDepartment of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
dc.contributor.otherDepartment of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
dc.contributor.otherDepartment of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
dc.contributor.otherDepartment of Technical Sciences and Soil Sciences, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
dc.date.accessioned2025-08-27T14:00:12Z
dc.date.accessioned2025-10-08T09:00:18Z
dc.date.available2025-10-08T09:00:18Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/38564
dc.description.abstractLand bonitation, or land rating, is a core instrument in agricultural policy used to evaluate land productivity based on environmental and climatic indicators. However, conventional Bonitation Coefficient (BC) methods are often rigid, require complete indicator sets, and lack mechanisms for handling missing or forecasted data—limiting their applicability under data scarcity and climate variability. This paper proposes a GIS-integrated, modular framework that couples classical BC computation with machine learning-based temporal forecasting and spatial generalization. Specifically, we apply deep learning models (LSTM, GRU, and CNN) to predict monthly precipitation—one of the 17 indicators in the Romanian BC formula—using over 61 years of data. The forecasts are spatially interpolated using Voronoi tessellation and then incorporated into the bonitation process via an adaptive logic that accommodates both complete and incomplete datasets. Results show that the ensemble forecast model outperforms individual predictors, achieving an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of up to 0.648 and an RMSE of 18.8 mm, compared to LSTM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.59</mn></mrow></semantics></math></inline-formula>), GRU (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.61</mn></mrow></semantics></math></inline-formula>), and CNN (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.57</mn></mrow></semantics></math></inline-formula>). While the case study focuses on precipitation, the framework is generalizable to other BC indicators and regions. This integration of forecasting, spatial generalization, and classical land evaluation addresses key limitations of existing bonitation methods and lays the groundwork for scalable, AI-enhanced land assessment systems. The forecasting module supports BC computation by supplying missing climate indicators, reinforcing that the primary aim remains adaptive land bonitation.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccAgriculture (General)
dc.titleA GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation
dc.typeArticle
dc.description.keywordsG.I.S.
dc.description.keywordscrop yield
dc.description.keywordsland bonitation
dc.description.keywordsmachine learning
dc.description.keywordsvoronoi tessellation
dc.description.doi10.3390/agriculture15161735
dc.title.journalAgriculture
dc.identifier.e-issn2077-0472
dc.identifier.oaioai:doaj.org/journal:08f69da3b11d42318ccfc59f2cb867bb
dc.journal.infoVolume 15, Issue 16


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