Muhammad Firdaus Abdul Muttalib
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Preferred name
Muhammad Firdaus Abdul Muttalib
Official Name
Muhammad Firdaus , Abdul Muttalib
Alternative Name
Muttalib, Muhammad Firdaus A.
Muttalib, Muhammad Firdaus Abdul
A. Muttalib, M. Firdaus
Muttalib, M. F.A.
Muttalib, M.
Muttalib, M. Firdaus A.
Main Affiliation
Scopus Author ID
55747520500
Researcher ID
IMT-8544-2023

Research Output
Now showing 1 - 3 of 3
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Publication

Knowledge mapping trends of Internet of Things (IoT) in plant disease and insect pest study: a visual analysis

2023 , Muhammad Akmal Mohd Zawawi , Mohd Fauzie Jusoh , Marinah Muhammad , Laila Naher , Nurul Syaza Abdul Latif , Muhammad Firdaus Abdul Muttalib , Mohd Nazren Radzuan , Andri Prima Nugroho

The study and literature on the Internet of Things (IoT) and its applications in agriculture for smart farming are increasing worldwide. However, the knowledge mapping trends related to IoT applications in plant disease, pest management, and control are still unclear and rarely reported. The primary aim of the present study is to identify the current trends and explore hot topics of IoT in plant disease and insect pest research for future research direction. Peer review articles published from Web of Science (WoS) Core Collection (2010-2021) were identified using keywords, and extracted database was analysed scientifically via Microsoft Excel 2019, VOSviewer and R programming software. A total of 231 documents with 5321 cited references authored by 878 scholars showed that the knowledge on the studied area has been growing positively and rapidly for the past ten years. India and China are the most productive countries, comprising more than half (52%) of the total access database on the subject area in WoS. IoT application has been integrated with other knowledge domains, such as machine learning, deep learning, image processing, and artificial intelligence, to produce excellent crop and pest disease monitoring research. This study contributes to the current knowledge of the research topic and suggests possible hot topics for future direction.

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Publication

Automatic monitoring of class A pan evaporation using the Internet of Things (IoT)

2024-03 , Mohd Fauzie Jusoh , Muhammad Firdaus Abdul Muttalib , Nur Sakinah Saedin , Mohd Mahmud

This study aimed to assess suitable water level sensor types and implement the automated monitoring of water levels within a Class A pan evaporation system using the Internet of Things (IoT). Both analogue and ultrasonic water level sensors underwent testing in controlled laboratory conditions for performance analysis. The results showed that the analogue water level sensor exhibited suboptimal output sensor responses compared to the ultrasonic sensor, primarily due to its susceptibility to variations in solution types and immersion depths. In contrast, ultrasonic sensors demonstrated strong performance with acceptable error rates, as evidenced by the Mean Absolute Error (MAE) of 1.03, Root Mean Squared Error (RMSE) of 1.42, and Coefficient of Determination (R²) of 0.94 during laboratory testing. However, the ultrasonic sensor's performance was somewhat reduced during field testing, exhibiting accuracy levels ranging from 6.7% to 51.2% within a greenhouse environment during rock melon cultivation. These discoveries highlight the feasibility of using ultrasonic sensors with environmental calibration to automate real-time evaporation measurements towards precision irrigation practices.

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Publication

Automatic Monitoring of Class A Pan Evaporation using the Internet of Things (IoT)

2024-03-01 , Mohd Fauzie Jusoh , Muhammad Firdaus Abdul Muttalib , Nur Sakinah Saedin , MohdMahmud

This study aimed to assess suitable water level sensor types and implement the automated monitoring of water levels within a Class A pan evaporation system using the Internet of Things (IoT). Both analogue and ultrasonic water level sensors underwent testing in controlled laboratory conditions for performance analysis. The results showed that the analogue water level sensor exhibited suboptimal output sensor responses compared to the ultrasonic sensor, primarily due to its susceptibility to variations in solution types and immersion depths. In contrast, ultrasonic sensors demonstrated strong performance with acceptable error rates, as evidenced by the Mean Absolute Error (MAE) of 1.03, Root Mean Squared Error (RMSE) of 1.42, and Coefficient of Determination (R²) of 0.94 during laboratory testing. However, the ultrasonic sensor's performance was somewhat reduced during field testing, exhibiting accuracy levels ranging from 6.7% to 51.2% within a greenhouse environment during rock melon cultivation. These discoveries highlight the feasibility of using ultrasonic sensors with environmental calibration to automate real-time evaporation measurements towards precision irrigation practices

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