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p-Index From 2021 - 2026
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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Lim, Hwee San
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Computer Science & IT

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Design and development of machine learning-based web application for oil palm yield prediction Ang, Yuhao; Shafri, Helmi; Lee, Yang Ping; Bakar, Shahrul Azman; Lim, Hwee San; Abdullah, Rosni; Yusup, Yusri; AL-Habshi, Mohammed Mustafa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp228-237

Abstract

The prediction of crop yields is influenced by various factors such as weather conditions, agronomic practices, and management strategies. Accurately predicting oil palm yield is crucial for sustainable production, as it plays a significant role in global food security. Challenges such as climate change and nutrient deficiencies have adversely affected yields, highlighting the necessity for a specialized web application tailored to the oil palm industry. This study presents a machine-learning-based web application that utilizes a deep learning model to estimate oil palm yields by integrating key parameters, including weather, agronomy, and satellite data. The application features a user-friendly interface and a dashboard for comparing predicted and actual yields, enhancing user engagement and facilitating collaboration among stakeholders. By deploying this tool on the cloud, plantation managers can make informed decisions early in the yield prediction process, ultimately improving plantation management and profitability. This web application is designed to provide valuable insights to stakeholders, contributing to effective decision-making in the oil palm sector.
Co-Authors AL-Habshi, Mohammed Mustafa Ang, Yuhao Bakar, Shahrul Azman Lee, Yang Ping Rosni Abdullah Shafri, Helmi Yusup, Yusri