Mango Grading By Using Fuzzy Image Analysis

In agricultural industry the efficiency and the proper
grading process is very important to increase the productivity. Currently, the agriculture industry has a better improvement, particularly in terms of grading of fruits, but the process is needed to be upgraded. This is because the grading of the fruit is vital to improve the quality of fruits. Indirectly, high quality fruits can be
exported to other countries and generates a good income. Mango is the third most important fruit product next to pineapple and banana in term of value and volume of production. There are demands for this fresh fruit from both local and foreign market. However, mangoes grading by humans in agricultural setting are inefficient, labor intensive and prone to errors. Automated grading system not only speeds up the time of the process but also minimize error. Therefore, there is a need for an efficient mango grading method to be developed. In this study, we proposed and implement methodologies and algorithms that utilize digital fuzzy image processing, content predicated analysis, and statistical analysis to
determine the grade of local mango production in Perlis. The main contribution for this study is on a design and development of an efficient algorithm for detecting and sorting the mango at more than 80% accuracy in grading compared to human expert sorting. This study becomes significant and may contribute to the perspective of
new knowledge on fuzzy image clustering model and can be adapted to the other fruit such as apples, pineapples and banana.