Research on Hyperspectral Imaging Detection Method of Nitrogen in Facility-Grown Lettuce

Yixue Zhang, Jingbo Zhi, Jialiang Zheng, Zhaowei Li and Tiezhu Li

Artificial Intelligence and Digital Technology, 2025, vol. 2, issue 1, 178-185

Abstract: Rapid non-destructive detection of crop nutrition serves as a crucial basis for water-fertilizer management and environmental regulation. This paper proposes a rapid nitrogen detection method for lettuce based on hyperspectral imaging technology. Hyperspectral image data of lettuce samples were acquired, processed using the SG smoothing algorithm, and analyzed with the RF algorithm to extract nitrogen-specific wavelengths. Finally, a KELM model under the RBF-Kernel function was established to predict lettuce nitrogen content. Results demonstrate that the KELM prediction model based on RF feature extraction achieves excellent performance, with an R 2 value exceeding 0.95 and RMSE below 0.27. This method provides scientific support for water and fertilizer irrigation decisions based on crop nitrogen requirements.

Keywords: Protected lettuce; Crop nutrition; Hyperspectral imaging; Feature extraction; Rapid detection (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:axf:aidtaa:v:2:y:2025:i:1:p:178-185

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