A Transferable MMD-CWT Framework for Accurate Cross-Regional Prediction of Maize Leaf Nitrogen Using Hyperspectral Sensing

Abstract

Accurate prediction of leaf nitrogen concentration (LNC) is crucial for optimizing nitrogen (N) management; however, the robustness of hyperspectral LNC prediction models often varies with management practices and growth conditions. To improve model robustness, this study developed a feature-level transfer learning framework that integrates the Maximum Mean Discrepancy (MMD) with Continuous Wavelet Transform (CWT). Five maize hyperspectral datasets from arid and semi-arid regions of Inner Mongolia, including multiple varieties, growth stages, and irrigation and nitrogen regimes, were used to identify representative LNC-sensitive spectral features. The proposed approach was evaluated against a conventional partial least squares regression with variable importance in projection (PLSR-VIP) baseline and implemented within a transfer learning framework to assess its robustness across datasets. Our results showed that, compared with the PLSR-VIP approach, the MMD–CWT framework was able to identify LNC-sensitive and stable spectral features across datasets with contrasting cultivars, irrigation regimes, and management practices, which were primarily located in the red and red-edge spectral regions. In spite of better performance of PLSR-VIP within single-site datasets, its predictive accuracy was worse when transferred across datasets with different crop varieties and management conditions. In contrast, the MMD–CWT framework provided more robust, and transferable LNC predictions (R2 = 0.57–0.81), even though there was a limited set of five wavelet-derived spectral features. These findings highlight the practical value of integrating MMD-based domain alignment and CWT-based feature selection within existing transfer learning frameworks for robust and transferable LNC monitoring across diverse management conditions.

Yuzhe Tang

TUM Visiting PhD student (2024.12-2025.06)

My research interests include hyperspectral remote sensing, precision agriculture, and nitrogen nutrient management.

Dr. Haibo Yang

Lecturer (current)

My research interests include hyperspectral remote sensing, precision agriculture, and nitrogen nutrient management.

PD. Dr. Yuncai Hu

Senior Researcher

My research interests include Remote sensing, Precision N nutrient management, Plant phenotyping for complex traits of abiotic stress tolerance, Agricultural N emissions, and Physiological mechanisms of plant responses to abiotic stresses.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.