| Autor: |
I. R., Senanayake, B. J. D. A ., Weerasekara, H. T. A., Siriwardana, N. G. R. P., Ekanayake, Fernando, Harinda, Kelegama, Thamali |
| Předmět: |
|
| Zdroj: |
International Research Journal of Innovations in Engineering & Technology; Oct2023, Vol. 7 Issue 10, p543-549, 7p |
| Abstrakt: |
The research paper introduces the NAVRO, this is a smart navigation system that aims to ease the customer's shopping experience. This application uses machine learning algorithms and IOT based technology. This application offers a range of features to enhance the customer's shopping experience in the supermarket including real time navigation, Product localization using Lora, crowd identification and identifying raw fruits. Creation of map. Here we manually edited it in Photoshop. In this application customers can navigate to the shortest path for the product in the supermarket. The crowded area is identified in the supermarket using crowd detection features. This application shows where the relevant product is located using LoRa .This is powered by deep neural network and IOT based technology by utilizing Long Range (LoRa) communication, the proposed system seeks to improve the effectiveness and accuracy of navigation in congested areas. For reliable picture recognition and object detection, CNN and neural network techniques are used, allowing for easy product identification and localization. The system also makes use of LoRa technology to relay location data, allowing for smooth communication and accurate product tracking. Additionally, the device has mapping and crowd detection features to enhance navigation in crowded spaces. The effectiveness of the suggested system is assessed through in-depth simulations and practical trials, revealing its potential to revolutionize product management and navigation in a variety of settings. Mostly in the agriculture sector, identifying rotten fruits and vegetable has been critical. Classification of fresh and rotten fruits and vegetables is usually done by humans. But this method is ineffective. So, we proposed a method for that. From the input fruit and vegetable images, the proposed model classifies fresh and rotten. The performance of the proposed model is tested on Kaggle dataset. The findings revealed that the proposed CNN model is capable of distinguishing between fresh and rotting fruits. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
