Bio-inspired vision fusion for quality assessment of harumanis mangoes

Abstract

The perceived quality of fruits, such as mangoes, is greatly dependent on many parameters such as ripeness, aroma, firmness, shape, size, and is influenced by other factors such as harvesting time. Unfortunately, a manual fruit grading has several drawbacks such as subjectivity, tediousness and inconsistency. By automating the procedure, as well as developing new classification technique, it may solve these problems. This paper presents the novel work on the bio-inspired multi-modality sensing system for classification and quality assessment of mangoes cv. Harumanis Mango using charge coupled device (CCD) camera and Infrared (IR) camera. A Fourier-based shape separation method was developed from CCD camera images to grade mango by its shape and able to correctly classify 100%. Colour intensity from infrared image was used to distinguish and classify the level of maturity and ripeness of the fruits. The finding shows 92% correct classification of maturity levels by using infrared vision