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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025027392 |
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| Summary: | 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. |
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| ISSN: | 2590-1230 |