Shape classification of harumanis mango using discriminant analysis (DA) and support vector machine (SVM)

The perceived quality of fruits, such as mangoes, is greatly dependent on many parameters such as ripeness, 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 using visible Imaging as a Tool in Quality Monitoring of Harumanis Mangoes. A Fourier-Descriptor method was developed from CCD camera images to grade mango by its shape. Discriminant analysis (DA) and Support vector machine (SVM) were applied for classification process and able to correctly classify 98.3% for DA and 100% for SVM