## 9 Conclusions  
The classification was implemented with supporting the ML algorithms based on external features and weights of mangoes and then using four ML models as LDA, SVM, KNN, and RF respectively to grade mango automatically. There are several conclusions drawn as follows:  
* In this study, the quality of mango is classified by four models (RF, LDA, KNN, and SVM). The models have high accurate over 87.9%, especially the model of RF has the best predictive precision of 98.1% compared with 3 remaining models. Therefore, classification based on rules generated from input variables is suitable to classify the quality of mango.
* Throughout the classification, a sequence of analytical methods in computer vision is used to transform the captured image of the mango to the image form that can be easily extracted feature from the mango. The experiment shows that such methods are successful when the predicted results have a small error.
* The solution of designed ML could maintain high prediction accuracy for different mangoes. However, it should be applied similarly to the sample mangoes.
* This study classifies mangoes based on four features consist of length, width, defect, and weight. The results showed that successful combination of extracted features as mangoes are classified with 98.1% accuracy.
* This study indicated that data normalization and the elimination of outliers are essential to enhance the grading accuracy of mango. The results demonstrated a significant increase in the accuracy of the mango grading by over 37.5%. Furthermore, by optimizing its parameters, the performance of models increased by more than 6.5% (except for the LDA model, which only increased by 1.34%). Consequently, high accuracy is achieved not only by a good combination of the extracted features but also by the properties of machine learning.  
Conceptualization, N.T.T.; methodology, N.T.T.; software, N.T.M.L.; validation, N.T.M.L.; formal analysis, N.T.M.L.; resources, N.T.T.; data curation, N.T.M.L.; writing--original draft preparation, N.T.M.L.; writing--review and editing, N.T.T.; visualization, N.T.M.L.; supervision, N.T.T.; project administration, N.T.T.; funding acquisition, N.T.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Science and Technology--Dong Thap province, Vietnam.  
This research is supported by Ho Chi Minh City University of Technology and Education, Vietnam.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type** & & **G1** & **G2** & **G3** & **Sensitivity (\%)** & **Precision (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Random Forest} & G1 & 348 & 2 & 0 & 99.4 & 98.6 & 99.0 & \\  & G2 & 5 & 305 & 7 & 96.2 & 97.8 & 97.0 & 98.1 \\  & G3 & 0 & 5 & 361 & 98.6 & 97.6 & 98.1 & \\ \hline Linear & G1 & 320 & 25 & 5 & 91.4 & 90.4 & 90.9 & \\ Discriminant & G2 & 30 & 253 & 34 & 79.8 & 83.8 & 81.8 & 87.9 \\ Analysis (LDA) & G3 & 4 & 24 & 338 & 92.3 & 89.7 & 91.0 & \\ \hline \multirow{3}{*}{K-Nearest Neighbors (KNN)} & G1 & 338 & 5 & 7 & 96.6 & 94.9 & 95.6 & \\  & G2 & 15 & 282 & 20 & 89.0 & 95.3 & 92.2 & 94.8 \\ \cline{1-1}  & G3 & 3 & 9 & 354 & 96.7 & 96.7 & 96.7 & \\ \hline \multirow{3}{*}{Support Vector Machine (SVM)} & G1 & 336 & 7 & 7 & 96.0 & 93.1 & 94.6 & \\ \cline{1-1}  & G2 & 24 & 277 & 16 & 87.4 & 91.1 & 89.3 & 92.7 \\ \cline{1-1}  & G3 & 1 & 20 & 348 & 94.3 & 93.8 & 94.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The accuracy of model.