IMPLEMENTASI TENSOR FLOW LITE PADA TEACHABLE UNTUK IDENTIFIKASI TANAMAN AGLONEMA BERBASIS ANDROID
| Autor: | Muhammad Bagus Baihaqi, Yovi Litanianda, Andy Triyanto |
|---|---|
| Rok vydání: | 2022 |
| Zdroj: | KOMPUTEK. 6:70 |
| ISSN: | 2614-0977 2614-0985 |
| DOI: | 10.24269/jkt.v6i1.1143 |
| Popis: | Aglonema or sri fortune has various types with various shapes, patterns and colors. Various types and more and more due to the many crossing processes carried out by owners and lovers of aglonema plants. For ordinary people who do not have insight into aglonema, it will be difficult to distinguish aglonema plants because the shapes, patterns and colors have similarities. It takes a Teachable Machine system with a complex but more sophisticated method that is able to recognize plants with a higher level of accuracy. The machine learning process is carried out on a computer to identify image data into classification results in the form of predictions. Tensorflow lite is a machine learning library specially designed for object recognition. Therefore, researchers are encouraged to create an Android-based mobile application that is able to recognize aglonema plants quickly, easily and accurately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|