Platform for Rapid Detection of Crop Moisture Content Based on NIR Spectroscopy and GBDT

Abstract:

Water content detection is essential for preventing mold growth and ensuring the proper storage and transportation of crops. However, existing portable detection methods often suffer from low accuracy and efficiency. To meet the demand for rapid crop water content measurement, a fast detection platform was proposed based on near-infrared (NIR) spectroscopy and a gradient boosting decision tree (GBDT) model. An off-axis Czerny-Turner (CT) optical structure was optimized by using Zemax, and a ZYNQ chip was employed to implement embedded decoding algorithms for a linear array CCD, with the system connected to a mobile phone. Savitzky-Golay (SG) smoothing and a piecewise direct standardization (PDS) data transfer algorithm were implemented on the mobile device. After spectral acquisition, the GBDT model running on the mobile phone was used to predict crop water content. Zemax simulations demonstrated that the off-axis optical design outperformed traditional spherical designs in terms of volume reduction and aberration control, maintaining a spectral resolution of 2 nm at the 950 nm edge of the image plane. SG smoothing improved the signal-to-noise ratio (SNR) from 18.11 dB to 35.10 dB. The GBDT-based prediction model achieved a coefficient of determination (R2) of 0.877 7 and a root mean square error of prediction (RMSEP) of 0.135 4% on the validation set. On the test set, the model attained an R2 of 0.861 7 and an RMSEP of 0.121 4%, with prediction absolute errors within 0.4%, meeting the requirements for rapid and efficient crop water content detection.