Automatic Fruit Grading System with High Adaptability Using Machine Learning Method

Fruit quality plays an important role in the agricultural economy. However, low efficiency and inaccurate detection in manual fruit grading has led to reduced fruit quality assurance. To solve these problems, an automatic fruit grading system for several kinds of fruits based on ML is proposed. In this research, four kinds of fruit are rapidly divided into three grades, depending on this automatic fruit grading system, through three steps. Firstly, the features with a large impact on fruit grading are extracted from fruit—the texture, shape, color, size, and defects. Then, the extracted fruit features are input into a Random Forest algorithm to train the fruit grading model. Finally, the grades of four kinds of fruit are predicted by this fruit grading model. The dataset contained 666 images from Kaggle of purchased fruit, including 270 images of apples, 170 images of pomegranates, 114 images of oranges, and 112 images of loquats. A 70% training set and 30% testing set split was used, and a ten-fold cross-validation (ten-fold CV) strategy was employed to evaluate the model. The experimental results show that the RF algorithm demonstrates the best stability and accuracy in classifying the four types of fruit, with accuracies of 98.6%, 95.3%, 98.1%, and 99.1% for apples, loquats, pomegranates, and oranges, respectively. Compared with other ML methods, RF performed the best in the multi-fruit classification task.