Prediction of Prunoideae Fruit Quality Characteristics Based on Machine Learning and Spectral Characteristic Acquisition Optimization

Machine learning algorithms have been widely used in the estimation and prediction of various quality characteristics of food and agricultural products. The purpose of this study is to study the availability and accuracy of the prediction model of Prunoideae fruit quality characteristics based on machine learning algorithms. XGBoost, LightGBM, CatBoost, and Random Forest (RF) are the four machine learning algorithms used in this work to create prediction models for the soluble solids content (SSC) and titratable acidity (TA) of peach, apricot, and cherry. The study investigated three hyperspectral denoising methods and used two feature extraction techniques to lower the dimensionality of hyperspectral data. The models' accuracy was further improved by adding multiple types of spectral data. Based on opposite spectral data input, the MLP-SG-XGBoost model produced the best SSC predictions for peach and apricot, with R2 values of 0.9162 and 0.9251, respectively, according to experimental results. When inputting opposite spectral data, the LGR-SG-LightGBM model showed the highest accuracy in predicting TA for peach and apricot, with R2 values of 0.9193 and 0.9206, respectively. The outcomes imply that the models can accurately forecast the qualities of Prunoideae fruit.