# Triple Cascaded Spectral Attention U-ResNet for UAV RGB-to-multispectral reconstruction in cocoa agroforestry
> Simanihuruk L.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105052010264
Jurnal / Konferensi: Journal of Agriculture and Food Research
Tahun terbit: 2026
DOI: https://doi.org/10.1016/j.jafr.2026.103333
ISSN: 26661543
Kuartil SJR: Q1
Citations: 0

## Authors
- Simanihuruk L.

## Abstract
High-resolution multispectral imaging systems for vegetation monitoring remain costly for many smallholder farmers. Most existing deep learning approaches attempt to infer multispectral information from RGB inputs. However, such methods ignore the biophysically determined spectral order across bands of light. Conventional single-decoder designs process all spectral outputs jointly and treat homogeneous feature representations across bands as a given. This study introduces TC-SA-UResNet (Triple Cascaded Spectral Attention U-Net with ResNet Encoder) as an integrated architecture that answers the gaps left by existing methods. TC-SA-UResNet performs cascaded reconstruction from RGB to Red Edge and Near Infrared bands. The network integrates a pretrained ResNet-50 encoder, Learnable Feature Injection, Spectral Attention via Cross-Attention, and a Biophysical Spectral Consistency Loss. The framework formalizes spectral reconstruction as an optimization problem with biophysical constraints. Evaluation on UAV-acquired cocoa agroforestry imagery yields SSIM values of 0.9540, 0.8805, and 0.9233 for the Blue, Green, and Red bands, respectively. The Red Edge and NIR bands achieve SSIM scores of 0.8024 and 0.7180. The proposed method achieves the highest NIR SSIM compared to T-GAN and U-Net baselines. TC-SA-UResNet also improves Red Edge R 2 to 0.8099 and exceeds T-GAN (0.6360). The improved R 2 indicates enhanced radiometric fidelity for vegetation index computation. The reconstructed NDVI achieves a Mean Absolute Error of 0.0342 with an R 2 of 0.8160 and a Pearson correlation of 0.9068 on the test set. The reported results position the proposed framework as a credible low-cost route to multispectral monitoring for precision agriculture applications.

## Keywords
- Multispectral image
- RGB color model
- Computer science
- Remote sensing
- Hyperspectral imaging
- Red edge
- Artificial intelligence
- Spectral bands
- Feature (linguistics)
- Vegetation (pathology)
- Pattern recognition (psychology)
- Enhanced Data Rates for GSM Evolution
- Normalized Difference Vegetation Index
- Multispectral pattern recognition
- Process (computing)
- Mean squared error
- Spectroradiometer
- Mathematics
- Consistency (knowledge bases)
- Spectral signature
- Computer vision
- Standard illuminant
- Pixel
- Artificial neural network
- Reflectivity
- Spectral slope
- Precision agriculture
- Near-infrared spectroscopy
- Reduction (mathematics)
- Convolutional neural network
- Edge device
- Iterative reconstruction

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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