# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 2 Rapid Detection Technology for Nutrients
### Rapid Detection Technology for Soil Nutrients  
Research on rapid detection technology for soil nutrients has been mainly based on electrochemistry, hyperspectral, laser-induced breakdown spectroscopy, and combined spectra, and has focused on preprocessing algorithms and regression models. According on the methods of electrochemistry et al., an ion-selective electrode (ISE) is a quick and low-cost method for soil nitrate nitrogen (N) detection. The measurement accuracies of the linear regression, multiple regression and BP neural network models were compared. The results showed that the BP neural network model had the highest \begin{table}
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**Sample Type** & **Data Sources** & **Preprocessing Algorithm** & **Characteristic Variable** & **Predictive Variables** & **Regression Model** & **Results** \\ \hline Leaf and canopy & Multiplex 3.6 sensor & & Reflectance values & & ULR & Based on RVI: R\({}^{2}\); 0.7063 & \\ Citrus [18] & FieldSpectral multispectral & & RVI NDVI & Chlorophyll & MLR & Based on NDV: R\({}^{2}\); 0.7343 & \\  & Hyperspectral & & CARS & & & & \\ Canopy & Multispectral & CARS & & & & & \\ Tea plant [19] & (spectrometer, multispectral camera) & POA, VCPA & & & & & \\ Canopy & RGB-NIR & & & & & & \\ Canopy & (RGB camera, and NIR cameras) & CMDCP & Color features Vegetation indices & Chlorophyll & RF & R\({}^{2}\); 0.738, RMSEC: 6.296 & \\  & & & & & & & \\ \hline \hline \end{tabular}
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Table 4: Key information of rapid detection technology for tree nutrients based on combined spectra. \begin{table}
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**Sample Type** & **Data Sources** & **Preprocessing Algorithm** & **Characteristic Variable** & **Predictive Variables** & **Regression Model** & **Results** \\ \hline Leaf & Collected by spectrometer & SpA Wavelet denoising & Reflectance values & Potassium & SSAE–DLNs & R\({}^{2}\); 0.8771 & \\  & Collected by spectrometer & SNV & Reflectance values & & PLSR & RMSE: 0.5528 & Best: BP \\ Leaf & Collected by spectrometer & SNV & Reflectance values & Nitrogen & SVM & R\({}^{2}\); 0.78 & R\({}^{2}\); 0.78 & \\  & Collected by spectrometer & SNV & Reflectance values & Nitrogen & RF & R\({}^{2}\); 0.74 & \\  & Almond Dried and powdered [4] & Collected by spectrometer & Reflectance values & Carbohydrates & PLSR & Overall accuracy: \(>\)90\% & Unique spectral region: SWIR \\  & SNV MSC & & & & & & MSC + D2-Rrog-RF: \\  & Spectral & & Nitrogen & RF & \({}^{2}_{\text{P}}\) = 0.985 & \\ Leaf & Collected by spectrometer & derivatives PLSR Random frog & Multi-band reflectance Potassium & NHOSM & SNV & \({}^{2}\) SNV + D2-Rrog-RF: \\  & & & & & & SVM & \({}^{2}\) P\({}^{2}\) = 0.977 \\  & & (Rrog) & & & & & \({}^{2}_{\text{P}}\) = 0.978 \\ Leaf & Collected by spectrometer & FOD & Dual-band Tri-band & Chlorophyll & HO & 750 nm & \\ Citrus [16] & Collected by spectrometer & CWT & Tri-band & Chlorophyll & PLSR & Kurtosis: 3.2, skewness: & \\  & & & & & & & \\ Leaf and canopy Apple [17] & Collected by spectrometer & First derivative & Reflectance values & Nitrogen & PLSR & The MLR model based on the raw reflectance was & The dark reflectance was & \\  & & & & & & MLR & better (4 key wavelengths, less data) \\ \hline \hline \end{tabular}
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Table 3: Key information of rapid detection technology for tree nutrients based on hyperspectral data. accuracy [21; 22]. The standard addition method was used to predict nitrate nitrogen. [PERSON] et al. used an Ion-Sensitive Field-Effect Transistor (ISFET) sensor to estimate the pH and available nutrients in soils [2]. [PERSON] et al. designed a real-time detection system for soil organic matter content based on the high-temperature excitation principle. This system mainly detected the carbon dioxide produced by heating the soil.