# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 2 Rapid Detection Technology for Nutrients
### Rapid Detection Technology for Soil Nutrients
The experimental results indicated that the prediction accuracy was highest when the heating depth and time were 15 mm and 20 s [23]. [PERSON] et al. used a method based on pyrolysis and artificial olfaction to predict soil total nitrogen. The model using the PLSR-BPNN exhibited better performance [24; 25; 26]. A specific comparison is presented in Table 5.  
Based on hyperspectral data, [PERSON] et al. used online collected Vis-NIR spectroscopy to develop a novel soil fertility index. The results revealed that this method had very good accuracy in predicting the soil fertility index [27]. [PERSON] et al. proposed a deep learning-based method for soil total nitrogen characteristic wavelength screening. A range of 8-50 characteristic wavelengths were screened, and the deep learning model incorporating the inception module performed well based on the characteristic wavelengths [28]. [PERSON] et al. used an embedded method to select characteristic wavelengths, and convolution operations were used to predict the soil total nitrogen. Thus, the accuracy improved [29]. [PERSON] et al. used the successive projection algorithm to extract wavelengths that were minimally redundant. The back-propagation neural network model was used to predict soil nitrogen, and a high prediction accuracy was obtained [30]. [PERSON] et al. conducted a study to develop an automatic filtering system of very noisy and non-soil spectra. The results indicated that the machine learning algorithms provided high classification accuracies [31]. To solve the limitations generated by massive hyperparameters on CNN for the prediction of organic carbon and total nitrogen, [PERSON] et al. employed spectral data obtained by Fourier-transform NIR spectroscopy and used SVD-CNN [32]. [PERSON] et al. proposed a spectral enhancement method based on a masked autoencoder to predict soil nutrients. This method can learn highly robust and generic spectral features from public NIR spectral datasets [33]. To overcome the influence of soil moisture, [PERSON] et al. proposed a method based on mixture-based weight learning to predict the soil total nitrogen [34]. [PERSON] et al. preprocessed the visible and near-infrared spectra of soil using the dual-wavelength indices transformations and constructed a soil parameter prediction model based on stacking machine learning approaches [35]. [PERSON] et al. extracted 272 hyperspectral bands using uninformative variable elimination and constructed a soil total nitrogen prediction model based on partial least squares regression [36]. The sensitive bands of soil total nitrogen content were extracted based on the Pearson correlation coefficient, and the random forest model was used to build the inversion model [37]. A specific comparison is presented in Table 6. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & \multicolumn{2}{c}{**Characteristic Variable**} & **Predictive Variables** & \multicolumn{1}{c}{**Regression Model**} & \multicolumn{1}{c}{**Results Results**} \\ \hline Ion-selective electrode [21] & Reading of nitrate ISE & Nitrate nitrogen & BP & The average relative errors: 5.0\% and 8.81\% \\ Ion-selective electrode [22] & Reading of nitrate ISE & Nitrate nitrogen & SAM & R\({}^{2}\): \textgreater{}0.9  RMSE: 2.21-5.49 mg/L \\ High-temperature excitation [23] & Carbon dioxide content & Organic matter & MLR & The 15 mm 20 s model had the highest accuracy, \textgreater{}90\%, \\ Pyrolysis and artificial olfaction [24; 25; 26] & Artificial olfaction feature space & Total nitrogen & PLSR & R\({}^{2}\): 0.92186  RMSE: 0.21781  RPDF: 3.3426 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key information of rapid detection technology for soil nutrients based on method of electrochemistry and others. Based on LIBS, [PERSON] et al. applied a CNN to soil analysis based on laser-induced breakdown spectroscopy. The CNN model decreased the root-mean-square error compared with partial least squares [38]. [PERSON] et al.