Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots
Modern agriculture faces escalating challenges that demand predictive models balancing accuracy and interpretability to support informed decision-making. While precision agriculture has advanced through high-resolution remote sensing technologies capturing spectral, thermal, and soil data, interpret...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025027392 |
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| author | J. Triana-Martinez A. Álvarez-Meza G. Castellanos-Dominguez |
| author_facet | J. Triana-Martinez A. Álvarez-Meza G. Castellanos-Dominguez |
| author_sort | J. Triana-Martinez |
| collection | DOAJ |
| description | Modern agriculture faces escalating challenges that demand predictive models balancing accuracy and interpretability to support informed decision-making. While precision agriculture has advanced through high-resolution remote sensing technologies capturing spectral, thermal, and soil data, interpreting these complex, nonlinear datasets remains a significant challenge for agronomists. This study introduces the TabNet-informed UMAP-based Local Biplot (UL-Biplot) framework, which combines TabNet's attention-based feature attribution with a Local Biplot technique from the widely recognized Uniform Manifold Approximation and Projection (UMAP). The method provides an intuitive representation of non-stationary and non-linear data patterns, enhancing both global and cluster-level explainability. Also, we highlight feature loadings based on TabNet's attention scores and evaluate the framework on a synthetic benchmark and two real-world datasets: a forage grasses trial for breeder score prediction and the WMBD wheat dataset for LAI estimation. Quantitative results demonstrate that our framework achieved an R2=0.79±0.01 for LAI and delivered more consistent performance for breeder scores (R2=0.77±0.01) relative to standard machine learning models. Moreover, TabNet's latent embeddings improved significantly in trustworthiness (from 0.94 to 0.98) and in class-neighbourhood preservation (0.047 versus 0.018 with conventional methods). The UL-Biplot also revealed distinct agronomic clusters associated with irrigation-induced greenness indices and moisture stress responses, providing actionable insights for site-specific crop management. |
| format | Article |
| id | doaj-art-bab3bd84d1dc4d1197fe546843c55d8d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-bab3bd84d1dc4d1197fe546843c55d8d2025-08-20T04:01:02ZengElsevierResults in Engineering2590-12302025-09-012710667210.1016/j.rineng.2025.106672Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local BiplotsJ. Triana-Martinez0A. Álvarez-Meza1G. Castellanos-Dominguez2Corresponding author.; Signal Processing and Recognition Group, Universidad Nacional de Colombia, Sede Manizales, 170001, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Sede Manizales, 170001, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Sede Manizales, 170001, ColombiaModern agriculture faces escalating challenges that demand predictive models balancing accuracy and interpretability to support informed decision-making. While precision agriculture has advanced through high-resolution remote sensing technologies capturing spectral, thermal, and soil data, interpreting these complex, nonlinear datasets remains a significant challenge for agronomists. This study introduces the TabNet-informed UMAP-based Local Biplot (UL-Biplot) framework, which combines TabNet's attention-based feature attribution with a Local Biplot technique from the widely recognized Uniform Manifold Approximation and Projection (UMAP). The method provides an intuitive representation of non-stationary and non-linear data patterns, enhancing both global and cluster-level explainability. Also, we highlight feature loadings based on TabNet's attention scores and evaluate the framework on a synthetic benchmark and two real-world datasets: a forage grasses trial for breeder score prediction and the WMBD wheat dataset for LAI estimation. Quantitative results demonstrate that our framework achieved an R2=0.79±0.01 for LAI and delivered more consistent performance for breeder scores (R2=0.77±0.01) relative to standard machine learning models. Moreover, TabNet's latent embeddings improved significantly in trustworthiness (from 0.94 to 0.98) and in class-neighbourhood preservation (0.047 versus 0.018 with conventional methods). The UL-Biplot also revealed distinct agronomic clusters associated with irrigation-induced greenness indices and moisture stress responses, providing actionable insights for site-specific crop management.http://www.sciencedirect.com/science/article/pii/S2590123025027392TabNetUMAPLocal BiplotFeature selectionInterpretabilityRemote sensing |
| spellingShingle | J. Triana-Martinez A. Álvarez-Meza G. Castellanos-Dominguez Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots Results in Engineering TabNet UMAP Local Biplot Feature selection Interpretability Remote sensing |
| title | Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots |
| title_full | Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots |
| title_fullStr | Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots |
| title_full_unstemmed | Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots |
| title_short | Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots |
| title_sort | enhancing agricultural data interpretability and visualization with tabnet driven feature extraction and local biplots |
| topic | TabNet UMAP Local Biplot Feature selection Interpretability Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025027392 |
| work_keys_str_mv | AT jtrianamartinez enhancingagriculturaldatainterpretabilityandvisualizationwithtabnetdrivenfeatureextractionandlocalbiplots AT aalvarezmeza enhancingagriculturaldatainterpretabilityandvisualizationwithtabnetdrivenfeatureextractionandlocalbiplots AT gcastellanosdominguez enhancingagriculturaldatainterpretabilityandvisualizationwithtabnetdrivenfeatureextractionandlocalbiplots |