# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 2 Rapid Detection Technology for Nutrients
### Rapid Detection Technology for Tree Nutrients
\begin{table}
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**Sample Type** & **Data Sources** & **Preprocessing Algorithm** & **Characteristic Variable** & **Predictive Variables** & **Regression Model** & **Results** \\ \hline Canopy & Collected by UAV & & Red, Green, & & & & Average error: \\ Cairoy & with multispectral & Blue, Red edge, & Macronutrients & GBRT & & \textless{17\%} \\ Citrus [8] & camera & & Near-infrared & & & For micronutrients: \\ Canopy & Collected by UAV & Vegetation index & Nitrogen & OLR & Better: MSR, RR & \\ Apple [9] & with multispectral camera & Vegetation index & Phosphorus & MSR & LNC R\({}^{2}\): 0.52–0.76 & \\ Non woven fabric & Collected by multispectral sensor & & Potassium & RR & LPC R\({}^{2}\): 0.67 & 0.69 \\ Canopy & Collected by UAV & & & & & LKC R\({}^{2}\): 0.76 & \\ \multirow{2}{*}{Cirnopy Apple [12]} & Collected by UAV with multispectral camera & & & NDVI & Chlorophyll & & R\({}^{2}\): 0.965 \\ \hline \hline \end{tabular}
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Table 2: Key information of rapid detection technology for tree nutrients based on multispectral data. \begin{table}
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**Sample Type** & **Data Sources** & **Preprocessing Algorithm** & **Characteristic Variable** & **Predictive Variables** & **Regression Model** & **Results** \\ \hline Canopy & Collected by UAV & & & & & & \\ Hans avocado [1] & Collected by UAV with camera & CNN & Vegetation & Indices & Nitrogen & & Better VIs: MGRVI \\ Canopy & Collected by Phenotype information collection platform with camera & YOLO V5 SNV & Color factor & Chlorophyll & FIT & R, G, B, G/R, G/B + STM & R\({}^{2}\): 0.731, RMSE: 2.16 \\ Leaf Apple [6] & Collected by camera & MSRx & R, G, B & 14 combination & Nitrogen & PCA & Best PCA-SVM \\  & & Gaussian filter Threshold & & & BP & MAE: \(\leq\)0.64 g/kg & \\ Leaf & Collected by camera & Segmentation & Color features & Potassium & ELM & RMSE: 0.80 g/kg \\ Leaf & Collected by camera & Carsy operator & Shape features & Potassium & MDA & Best: \\  & & MSR-color & & & & & MLR-LDA-SVM \\  & & restoration & & & & & Average accuracy: \\ \hline \hline \end{tabular}
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Table 1: Key information of rapid detection technology for tree nutrients based on RGB images. Based on the combined spectra, [PERSON] et al. developed a rapid detection method for chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors [18]. [PERSON] et al. proposed a method for estimating the N content of tea plants under field conditions based on the combination of a multispectral imaging system and hyper-spectral data [19]. In view of the uneven distribution of illumination on broad-leaved banana canopies in the field, [PERSON] et al. proposed a correction method based on the dark channel prior to enhance the image quality, and improve the estimation accuracy of chlorophyll content [20]. A specific comparison is presented in Table 4.