A GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation
A GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation
Bogdan Văduva, Anca Avram, Oliviu Matei, Laura Andreica, Teodor Rusu
Abstract. Traditional land bonitation, the agronomic evaluation of land quality and agricultural potential, often relies on static, historical data that fails to capture the dynamics of a changing climate. This paper presents a GIS-driven, machine learning-enhanced framework for adaptive land bonitation. By combining Geographic Information Systems (GIS) with machine learning algorithms, the framework continuously analyses complex spatial and environmental data to evaluate land potential dynamically. The result is a precision crop-matching tool that supports dynamic adaptation to evolving climate, rainfall and soil conditions, while contributing to sustainable land management. The approach provides stakeholders with highly accurate, future-proof land quality scores and prevents land degradation and over-farming.
Keywords: GIS; land bonitation; machine learning; adaptive evaluation; precision agriculture; sustainable land management
📋 Cite this publication
Bogdan Văduva, Anca Avram, Oliviu Matei, Laura Andreica, Teodor Rusu, "A GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation", Agriculture (Basel), vol. 15, no. 16, MDPI, 2025, 2023. DOI: https://doi.org/10.3390/agriculture15161735.
Reference: Agriculture (Basel), vol. 15, no. 16, MDPI, 2025. DOI: 10.3390/agriculture15161735
A comparative study of different genetic algorithms approaches to capacitated vehicle routing problem for collection of agricultural products
A comparative study of different genetic algorithms approaches to capacitated vehicle routing...
Using Machine Learning for Identifying the Intrinsic Economic Specializations of Localities
Using Machine Learning for Identifying the Intrinsic Economic Specializations of LocalitiesOliviu...
Embedding GIS in crop field bonitation computation
Embedding GIS in crop field bonitation computationBogdan Văduva, Oliviu Matei, Anca Avram, Laura...
A comparative study of machine learning models for plant disease identification
A comparative study of machine learning models for plant disease identificationMăcelaru Mara,...
A Novel CNN Approach for Accurate Tomato Disease Classification
A Novel CNN Approach for Accurate Tomato Disease ClassificationOvidiu Cosma, Laura Cosma Abstract....
Design of a collaborative network for mapping digital skills for Industry 5.0
Design of a collaborative network for mapping digital skills for Industry 5.0Maria Gustavsson,...
Solving the clustered minimum routing tree problem using Prüfer-coding based hybrid genetic algorithms
Solving the clustered minimum routing tree problem using Prüfer-coding based hybrid genetic...
Augmenting API Security Testing with Automated LLM-Driven Test Generation
Augmenting API Security Testing with Automated LLM-Driven Test GenerationEmil Marian Pasca, Rudolf...
Data Quality Assessment Methodology
Data Quality Assessment MethodologyDaniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei...
Privacy Assessment Methodology for Machine Learning Models and Data Sources
Privacy Assessment Methodology for Machine Learning Models and Data SourcesRudolf Erdei, Emil...
Aggregation Strategy for Federated Machine Learning Algorithm
Aggregation Strategy for Federated Machine Learning AlgorithmRudolf Erdei, Daniela Delinschi,...
Using Markov chains for determining the proximity contagion of smart specialization of localities
Using Markov chains for determining the proximity contagion of smart specialization of...
0 Comments