Enhancing Nutrient Management in Crops through Innovative Spectral Indices

In our recent studies, we have focused on improving spectral sensing and predicting chlorophyll and nitrogen content in field crops using advanced machine learning techniques and novel spectral indices. The Yang et al. 2025, IJRS paper introduces a hybrid dataset to enhance the prediction of potato leaf chlorophyll content, addressing the critical issue of crop health monitoring with machine learning. Meanwhile, the Tang et al. 2025, FCR paper presents the Nitrogen Triangle Ratio Index (NTRI), a new spectral index that significantly improves the accuracy of diagnosing nitrogen status for maize. Both papers underscore the importance of precise nutrient management in agricultural practices, especially in arid regions. The integration of spectroscopic techniques allows for real-time, non-destructive monitoring of crop health, providing actionable insights for site-specific fertigation strategies. The novelty of our research lies in developing robust tools that surpass traditional indices, thereby enabling farmers to enhance yield sustainably. These findings not only contribute to academic knowledge but also have practical implications, inspiring further research into smart farming technologies that can adapt to evolving agricultural challenges.