Near-infrared (NIR) spectroscopy is one of the most prominent non-destructive techniques which can classify the quality of agricultural products. An important issue for the NIR is when the new target data from different environments, e. g. area, orchard, harvesting age, harvesting year, etc. , is classified by the existing classifier, the predicting performance might not be effective. Furthermore, the classification model re-building can incur the cost in term of laboring or time. In this paper, an ensemble classification is applied to predict the quality of mango cv. Nam Dok Mai Si Thong fruits. The ensemble classification is a technique which combines multiple classifiers to work as a sole classifier. In addition, such individual classifier, sub-classifier, will be assigned a weight according to the similarity to the target dataset for adapting itself to the new environment. In our work, the ensemble classification will contain the classification models, multiple linear regression (MLR) equations, trained from the data with different harvesting year, and harvesting period for the fruit collection on such year. The weight of each sub-classifier is obtained from the difference between its prediction error and the prediction error of the target dataset. The proposed approach was evaluated by the experiments in which its performance was compared with the traditional sole classifiers as well as the naïve ensemble classifiers without weighting. From this result, the standard error of prediction (SEP) values of the proposed ensemble classifier, naïve ensemble classifier, and single classifier were 0. 95, 1. 08, and 0. 99, respectively. Such results can illustrate that our proposed ensemble classifier can perform well for the changing environments.

Ensemble classifier Near-infrared spectroscopy Mango