Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Intelligent Engineering
Ryan Prayuga Ardiansyah
Universitas Panca Marga
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Android-Based Weed Identification and Herbicide Recommendation Using Convolutional Neural Networks Ahmad Izzuddin; Ryan Prayuga Ardiansyah; Andrik Sunyoto; Dyah Ariyanti; Ira Aprilia
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.40594

Abstract

Weed infestation reduces crop yield and quality, while inappropriate herbicide selection often limits effective control. This paper presents the design and implementation of an Android-based decision-support application for weed identification and herbicide recommendation using a smartphone camera. Weed images are classified using a lightweight Convolutional Neural Network with a MobileNetV2 architecture optimized for mobile deployment. Herbicide recommendations are generated using the Cosine Similarity method to associate identified weed characteristics with suitable control agents. The system is modeled using the Unified Modeling Language (UML) to ensure modularity and scalability. Experimental results show that the proposed CNN model achieves a classification accuracy of 96%. The integrated on-device image acquisition and intelligent recommendation enable practical field deployment, providing an efficient tool to support weed management decisions.
Co-Authors Ahmad Izzuddin Andrik Sunyoto Dyah Ariyanti Ira Aprilia