A study was conducted to evaluate the concentrations of nitrogen (N) and potassium (K) in different tillage and fertilization modes using laser-induced breakdown spectroscopy (LIBS). The study focused on analyzing nutritional elements in rice leaves, ears, and stems. Due to the inorganic and organic compounds in rice leaves, ears and stems, the non-interfering characteristic line spectra with the low matrix effect were chosen to quickly and accurately analyze nutritional elements of N (670.8759, 742.3641, 744.2298, 746.8312, 848.4861, 856.7735, 862.9235, 868.0282 nm) and K (766.4911, 769.8974 nm). A total of fifteen groups of rice samples were collected and analyzed using mean and range normalization to reduce noise. Principal component analysis (PCA) was used to extract eigenvalues and reduce data sets. K-means cluster and Fisher linear discriminant methods were used to classify rice samples with different tillage and fertilization modes. The results showed that rice leaves were clearly distinguished with an 81.96% confidence interval. The study demonstrates the potential use of LIBS as a tool for analyzing the effect of nutritional elements in rice and evaluating tillage and fertilization modes.

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Analysis of N and K Elements in Root-Removed Rice Samples Using Laser-Induced Breakdown Spectroscopy: Evaluating Different Modes of Tillage and Fertilization

  • Jiandong Hu,
  • Min Jiang,
  • Haifeng Sun,
  • Shakeel Ahmed,
  • Hong Zhang,
  • Hao Zhang,
  • Jorge Hernandez

摘要

A study was conducted to evaluate the concentrations of nitrogen (N) and potassium (K) in different tillage and fertilization modes using laser-induced breakdown spectroscopy (LIBS). The study focused on analyzing nutritional elements in rice leaves, ears, and stems. Due to the inorganic and organic compounds in rice leaves, ears and stems, the non-interfering characteristic line spectra with the low matrix effect were chosen to quickly and accurately analyze nutritional elements of N (670.8759, 742.3641, 744.2298, 746.8312, 848.4861, 856.7735, 862.9235, 868.0282 nm) and K (766.4911, 769.8974 nm). A total of fifteen groups of rice samples were collected and analyzed using mean and range normalization to reduce noise. Principal component analysis (PCA) was used to extract eigenvalues and reduce data sets. K-means cluster and Fisher linear discriminant methods were used to classify rice samples with different tillage and fertilization modes. The results showed that rice leaves were clearly distinguished with an 81.96% confidence interval. The study demonstrates the potential use of LIBS as a tool for analyzing the effect of nutritional elements in rice and evaluating tillage and fertilization modes.