Controlled release urea (CRU) is considered to enhance crop yields while alleviating negative environmental problems caused by the hazardous gas emissions that are associated with high concentrations of ammonium (NH₄⁺) and nitrate (NO₃⁻) in black soils. Short-term effects of sulfur-coated urea (SCU) and polyurethane-coated urea (PCU), compared with conventional urea, on NO₃⁻ and NH₄⁺ in black soils were studied through the buried bag experiment conducted in an artificial climate chamber. We also investigated nitrogen (N) release kinetics of CRU and correlations between the cumulative N release rate and concentrations of NO₃⁻ and NH₄⁺. CRU can reduce concentrations of NO₃⁻ and NH₄⁺, and PCU was more effective in maintaining lower soil NO₃⁻/NH₄⁺ ratios than SCU and U. Parabolic equation could describe the kinetics of NO₃⁻ and NH₄⁺ treated with PCU. The Elovich equation could describe the kinetics of NO₃⁻ and NH₄⁺ treated with SCU. The binary linear regression model was established to predict N release from PCU because of significant correlations between the cumulative N release rate and concentrations of NO₃⁻ and NH₄⁺. These results provided a methodology and data support for characterizing and predicting the N release from PCU in black soils.

For the reasonable and effective collection of Ophiocordyceps sinensis, a new method of on-site identification was attempted using a portable multispectral imaging (MSI) technique. Three dimensional (3D) data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation (SNV). Principal component analysis (PCA) and simulated annealing particle swarm optimisation (SAPSO) algorithms were used to extract characteristic images and develop the support vector classification (SVC) models. Results show that the fused feature model of SAPSO-SVC has the best performance, resulting in a recognition accuracy of the prediction set of 96.30%. Moreover, on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC, and the target could be easily identified from the distribution map. This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai–Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm. Keywords: Ophiocordyceps sinensis, MSI, SAPSO-SVC, On-site distribution map DOI: 10.25165/j.ijabe.20201306.5425 Citation: Duan H W, Tong X, Cui R X, Han L J, Huang G Q. On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics. Int J Agric & Biol Eng, 2020; 13(6): 166–170.