Classification Grading of Nam Dok Mai See-Thong Mango by Deep Learning and Transfer Learning

Nam Dok Mai See-Thong mango is a highly profitable export fruit for Thailand's economy. The grading of high-quality mangoes meeting international standards leads to higher export prices. This paper presents applied deep learning and transfer learning techniques to classify and grade Nam Dok Mai See-Thong mango. Four models, namely MobileNetV2, DenseNet121, InceptionV3, and Xception, were employed to classify mangoes into four categories based on images: perfect mango, ripe, moldy, and shape. The study utilized a large dataset of mango images for training the models and evaluated the results using accuracy, precision, recall, and F1-score. The study proposes the potential of machine learning to enhance the accuracy and efficiency of mango classification. The result showed that the MobileNetV2 model performed best in classifying ripe and shaped mangoes, achieving accuracies of 0.94 and 0.71, respectively. In contrast, the Xception model demonstrated superior performance in classifying moldy mangoes, attaining an accuracy of 0.96. This research highlights the importance of utilizing technology in the quality grading of export fruits to improve their economic value.

I. Introduction

Thailand's position as one of the largest mango-producing countries in the world is significant, and the fact that Thailand is the leading exporter of mangoes globally is impressive. The Nam Dok Mai See-Thong Mango [1] significantly contributes to Thailand's economy and is a unique product famous worldwide. Assessing the quality of Nam Dok Mai See-Thong Mango's quality is crucial in obtaining better prices for the produce. However, the current assessment process is primarily manual and relies on human expertise and visual inspection, which can be time-consuming and prone to individual errors due to fatigue. Consequently, farmers incur high labor costs. As a result, many farmers opt to sell their entire plantation to intermediaries without grading their mangoes, leading to lower prices for their produce.