Strawberry Disease Diagnose Mobile App Integration System

Authors

  • I. Ketut Agung Enriko Department of Telecomunication Engineering, Institut Teknologi Telkom Purwokerto, Purwokerto, Indonesia.
  • Erika Lety Istikhomah Puspita Sari Department of Telecomunication Engineering, Institut Teknologi Telkom Purwokerto, Purwokerto, Indonesia.
  • Melinda Departement of Electrical and Computer Engineering, Faculty of Engineering, Syiah Kuala University, Banda Aceh, Indonesia
  • M. Fauzan Alfariz Departement of Electrical and Computer Engineering, Faculty of Engineering, Syiah Kuala University, Banda Aceh, Indonesia.
  • Nanda Arisna Departement of Electrical and Computer Engineering, Faculty of Engineering, Syiah Kuala University, Banda Aceh, Indonesia
  • Faruq Miqdad Mudaffar Departement of Electrical and Computer Engineering, Faculty of Engineering, Syiah Kuala University, Banda Aceh, Indonesia

Keywords:

Firebase, Flask, Strawberry, Accuracy, Application Mobile

Abstract

The agriculture industry is one of the commodities driving Indonesia's economic growth. In wealthy countries, the application of technology in agriculture has already begun. One approach is to make use of the Internet of Things (IoT). IoT coupled with artificial intelligence to assist farmers in diagnosing illnesses in strawberry plants with accuracy findings meet the target goal of >90%. Using the Convolution Neural Network (CNN) whose results will be integrated as create a mobile application that can identify android-based ailments faced by strawberry plants. Where the model is fed into the flask framework, which has been connected with Firebase, and the database storage is configured to accept the data. The functionalities are then inserted into the flask code framework design connected with Firebase. Following that, the machine learning model produces predictions to detect illnesses in strawberry plants which will be displayed on the mobile application system.

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Published

11.01.2024

How to Cite

Enriko, I. K. A. ., Puspita Sari, E. L. I. ., Melinda, M., Alfariz, M. F. ., Arisna, N. ., & Mudaffar, F. M. . (2024). Strawberry Disease Diagnose Mobile App Integration System. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 271–277. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4449

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

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