Classification of Longan Edibility using Machine Learning

Pratondo, Agus; Novianty, Astri

doi:10.1109/apics56469.2022.9918746

Cited by 12 publications

(1 citation statement)

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“…It can learn to recognize the texture, shape, and other characteristics that distinguish each variant from the others. The resulting model can then be used to classify new cassava leaf images with a high degree of accuracy, making it easy to identify each variant [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Classification of cassava leaves (Manihot esculenta) using transfer learning

Pratondo

2023

IJEEI

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There are several types of cassava leaves with different characteristics, tastes, and nutritional content. Some people use cassava leaves as a vegetable ingredient for daily consumption as a source of fiber and minerals. However, people often have difficulty identifying the different types of cassava leaves, including cassava leaf variants that are locally referred to as gajah, karet, and mentega. This study aims to use transfer learning to identify the variant of cassava leaves. The Inception v3 architecture was selected to build the classification model. To demonstrate the superiority of transfer learning, the Inception v3 architecture was run with two different weights. The first weight was randomly initialized, while the second weight was taken from pre-trained weights from ImageNet. The experimental results show that the classification accuracy rate using the pre-trained weights reached 95.76%. This indicates that the classification model used in this study is promising and can be used for practical purposes in everyday life.

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Section: Introductionmentioning

confidence: 99%