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All Journal JOIV : International Journal on Informatics Visualization AJAD : Jurnal Pengabdian kepada Masyarakat EDUCTUM: Journal Research Business and Investment Review Socius: Social Sciences Research Journal Jurnal Multidisipliner Kapalamada JGEN : Jurnal Pengabdian Kepada Masyarakat
Rahmadani, Fitria
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Journal : JOIV : International Journal on Informatics Visualization

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Multilayer Perceptron Model with Feature Extraction for Potassium Deficiency Identification of Cocoa Plants Basri, Basri; Karim, Harli A; Assidiq, Muhammad; Arafah, Muhammad; Rahmadani, Fitria
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2829

Abstract

The development of Multilayer Perceptron (MLP) models for networked learning systems heavily relies on the specific application case study and the accurate parameterization aligned with the chosen computer vision feature extraction models. This study proposes an MLP model for identifying potassium deficiency in cocoa plants. The feature extraction methodology employs object feature extraction that commonly used in computer vision, including Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Hue Saturation Value (HSV) models. These computer vision techniques aid in analyzing leaf characteristics classified into two categories: normal conditions and leaves identified with potassium deficiency. The dataset used in this research comprises two conditions: with a white background and without any specific background. The study evaluates various feature extraction techniques based on MLP parameters, incorporating network learning rates and optimizing solvers. Employing the ROC analysis method throughout the data collection, algorithm development, validation, and analysis phases reveals that the most effective classification performance, reaching up to 93.33% accuracy on the background dataset and 90.00% on the non-background dataset, is achieved using HSV-based color feature extraction with MLP parameters set at an initial learning rate of 10-3 and employing the Adam optimization solver. These outcomes underscore the suitability of HSV color feature extraction for identifying potassium deficiency in cocoa plant leaves. However, optimizing parameters remains crucial to maximize its application in real-time identification systems. Future research should refine these parameters to enhance the model's robustness and efficacy across broader agricultural contexts.
Co-Authors Akma, Muhammad Ananda Ferga, Sherly Annisa Assidiq, Muhammad Basri Basri Batubara, Zakaria Dias Pertiwi, Eka Ezril, Ezril Febrianda, Nuriasma Gultom, Rumondang Hijaya, Agil Putra Ishak, Khodijah Junery, Muhammad Fadhil Karim, Harli A Marbun, Kesia Lina Uli Martina, Siska Evi Mehilda, Sukma Melviani, Nur Muhammad Arafah Rahim, Hainnuraqma Binti Rahmah, Sintya Ayu Ramadani, Fazrineka Rosman Ilato Rusdi Hidayat Nugroho Rusli Isa Sakbaniah, Nur Selamat, Muhammad Isa Shobabiya, Mahasri Sinaga, Janno Wardani, Amallia Putri Kusuma Yonggiana