Comparative Analysis of Explainable Spectroscopic-Fusion Models for Avocado Leaf Nitrogen Prediction

Precise nutrient management in avocado orchards requires accurate and interpretable predictions of leaf nitrogen content. In this study, explainable machine-learning models integrating spectral reflectance (350–2500 nm) and multispectral spatial features were comparatively evaluated for non-destructive estimation of avocado leaf nitrogen content. Partial Least Squares Regression (PLSR) and one-dimensional Convolutional Neural Network (1D-CNN) models were first developed using only spectral reflectance data as a baseline. To incorporate spatial–spectral information, multispectral images were arranged into cubes and processed with a 3D-CNN to extract deep features. These features were subsequently fused with the original spectral data to form a combined dataset. Both PLSR and 1D-CNN models were retrained using the fused inputs. The CNN hyperparameters were fine-tuned using Bayesian optimization, achieving a prediction R2 of 0.73 and an RMSE of 0.92 mg/g, which was comparable to the conventional PLSR model. The fusion approach showed numerically better performance than models based solely on spectra, suggesting spatial context may provide complementary information relevant to nitrogen estimation. Explainable artificial intelligence techniques, including SHapley Additive exPlanations and Integrated Gradients, identified 745 nm (red-edge), 994 nm (near-infrared), and 2459 nm (shortwave infrared), together with deep spatial features extracted from multispectral image cubes, as the most important predictors of nitrogen content in avocado leaves.