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 in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed to predict three different levels of potassium (K) concentration in peach leaves using principal component analysis (PCA) and develop models for predicting the K concentration of a peach leaf using a hyperspectral imaging technique. Hyperspectral images were acquired from a randomly selected fresh peach leaf from multiple trees over the spectral region between 500 and 900 nm. Leaves were collected from trees with varying potassium levels of high (2.7 similar to 3.2%), medium (2.0 similar to 2.6%), and low (1.3 similar to 1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky-Golay first derivative, Savitzky-Golay second derivative, and standard normal variate (SNV)) were applied to the raw data and partial least square (PLS) was used to develop a model for each of the pretreatments. The R-2 values for each pretreatment method were 0.8099, 0.6723, 0.5586, and 0.8446, respectively. The SNV prediction model has the highest accuracy and was used to predict the K nutrient using the validation data. The result showed a slightly lower R-2 = 0.8101 compared with the training. This study showed that HSI could measure K concentration in peach tree cultivars.

Automated summary

Hyperspectral imaging is a useful technology in agriculture for monitoring plant health and detecting pest and diseases. This study predicts the potassium concentration in peach leaves using principal component analysis and develops models for prediction. Different pretreatment methods and partial least square analysis were used to achieve different levels of accuracy in prediction.