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Implementation of Learning Vector Quantization (LVQ) Algorithm for Durian Fruit Classification Using Gray Level Co-occurrence Matrix (GLCM) Parameters

2019; IOP Publishing; Volume: 1196; Linguagem: Inglês

10.1088/1742-6596/1196/1/012040

1742-6596

Sutarno Sutarno, Sara Putri Fauliah,

Leaf Properties and Growth Measurement

Diversity on durian varieties makes it difficult to distinguish between durian varieties. In addition, some durian varieties also have similarities which further complicate the classification process, and are known as typical tropical fruits, these fruits are native to Southeast Asia and have been introduced throughout the world. The ability to recognize objects is needed by humans. Recognition can facilitate human daily activities. Pattern recognition is often applied to a variety of objects, one of which is pattern recognition on fruit. There is some limitations of human memory in remembering object features. Based on this problem, research was conducted to identify fruit plants based on its features using digital image processing techniques, Learning Vector Quantization (LVQ) algorithm and Gray Level Co-occurrence Matrix (GLCM). Each type of plant has different shapes, colors, and textures; this is what makes the durian unique to other durians. The program was created using Microsoft Visual C # 2010 software. The test results achieved an 89% success rate in recognizing fruit plants based on the fruit.