Shape, size and maturity features extraction with fuzzy classifier for non-destructive mango (Mangifera Indica L., cv. Kesar) grading

In the era of ICT technologies, automation in grading of mango (Mangifera Indica L.) is important to reach consumer demand for quality mango. This paper addresses that issue to identify agricultural produce based on shape, size and maturity. Fuzzy inference system is used for decision making process. In this paper, proposed methodology is divided in three phases: In first phase, mangoes are classified either well formed or deformed using eccentricity, extent and cross-ratio properties of shape. Second phase discusses about size and maturity classification. Weight and area are used for size feature extraction and mean of a* and b* channel of L*a*b* color space are used as parameters of maturity feature. In this phase, mangoes are classified in small, medium or big size and unripe, partially ripe or ripe maturity. At final phase, decision making theory is used to grade mango in class I, class II or class III. Integration of whole system results average accuracy 90% and system takes 2.1 seconds to grade a mango.