Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles

Introduction Hyperspectral imaging (HSI) is a powerful, non-destructive technology that enables precise analysis of plant nutrient content, which can enhance forestry productivity and quality. However, its high cost and complexity hinder practical field applications. Methods To overcome these limitations, we propose a deep-learning-based method to reconstruct hyperspectral images from RGB inputs for in situ needle nutrient prediction. The model reconstructs hyperspectral images with a spectral range of 400–1000 nm (3.4 nm resolution) and spatial resolution of 768×768. Nutrient prediction is performed using spectral data combined with competitive adaptive reweighted sampling (CARS) and partial least squares regression (PLSR). Results The reconstructed hyperspectral images enabled accurate prediction of needle nitrogen, phosphorus, and potassium content, with coefficients of determination (R²) of 0.8523, 0.7022, and 0.8087, respectively. These results are comparable to those obtained using original hyperspectral data. Discussion The proposed approach reduces the cost and complexity of traditional HSI systems while maintaining high prediction accuracy. It facilitates efficient in situ nutrient detection and offers a promising tool for sustainable forestry development.