Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis

Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or pest/disease detection. As sensing advancement expands, measuring nutrient levels and disease detection also progresses. study aimed predict three different potassium (K) concentration peach leaves using principal component analysis (PCA) develop models for predicting K a leaf hyperspectral technique. images were acquired from randomly selected fresh multiple trees over spectral region between 500 900 nm. Leaves collected with varying high (2.7~3.2%), medium (2.0~2.6%), low (1.3~1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky–Golay first derivative, second standard normal variate (SNV)) applied raw data partial least square (PLS) was model each pretreatments. The R2 values method 0.8099, 0.6723, 0.5586, 0.8446, respectively. SNV prediction has highest accuracy validation data. result showed slightly lower = 0.8101 compared training. that HSI measure tree cultivars.