Mohd Firdaus Ibrahim
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Preferred name
Mohd Firdaus Ibrahim
Official Name
Mohd Firdaus , Ibrahim
Alternative Name
Ibrahim, M. F.
Ibrahim, Mohd Firdaus
Main Affiliation
Scopus Author ID
56571962400
Researcher ID
AAE-5507-2020

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Design of Compact UHF-RFID Tag Antenna with Meander Line Technique

2020-03-20 , Fazilah A.F.M. , Muzammil Jusoh , Ammar Zakaria , Thennarasan Sabapathy , Mohd Firdaus Ibrahim , Mohamed Nasrun Osman , Yaasin M.N. , Malhotra S. , Hasliza A Rahim @ Samsuddin

In this paper, a compact UHF RFID tag antenna that can operate at Malaysia UHF RFID frequency (860 MHz - 960MHz) is proposed. The antenna with a common geometry structure without any shorting pin consists of 90degree angled line (meander line) and double T-match structure. The proposed antenna has been designed and simulated using CST Simulation software. The proposed tag antenna design shows a good performance in terms of size, gain and impedance with a dimension of 36 mm 25 mm 1.6 mm. The simulated gain of antenna obtained is -0.135 dB at 910 MHz with 19 MHz bandwidth.

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Development of a machine vision system for rice seed inspection system

2020-01-01 , Ruslan R. , Khairunniza-Bejo S. , Rukunudin I.H. , Jahari M. , Mohd Firdaus Ibrahim

Rice seed production in Malaysia is greatly dependent on the purity of the cultivated paddy seed produced through the government certified paddy seed program. The seeds to be marketed by the seed processors must undergo quality control protocol where the seed lots are sampled from the seed farms and seed processing plants for purity analysis by the enforcing agency at the Seed Testing Laboratory of the Department of Agriculture (DoA). The current inspection conducted by the laboratory is based on a manual process, which is laborious and time-consuming. Therefore, a prototype (Patent ID: PI2018500018) of a machine vision-based rice seed inspection system (RiSe-IViS) was developed to explore the possibility of replacing the existing manual method in distinguishing the weedy rice and cultivated rice seeds under the Standard Jabatan Pertanian Malaysia (SJPM) standard protocol with a modern, effective and efficient technique using an image processing approach. The developed RiSe-IViS prototype consists of two parts i) hardware configuration and ii) software development. This paper discussed the criteria to be established, challenges and limitation encountered in developing the hardware prototype involving the image acquisition setup, lighting configuration and seed plate design. The importance of each criterion to ensure its reproducibility are also discussed. A software programme was developed to assist the user for image acquisition and analysis. The image processing steps undertaken in the programme are also discussed. The RiSe-IViS is expected to classify major rice seed varieties available in Malaysia against the weedy rice variants with superior accuracy.

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Multi sensor system for classifying Harumanis mango based on external and internal quality

2016 , Mohd Firdaus Ibrahim

This thesis presents a multi sensor system for classifying Harumanis mango based on its external and internal quality. Both external and internal quality of Harumanis mango affects the consumer buying preferences. Current method of classifying Harumanis mango is done manually and destructive for its internal quality determination. The proposed system consists of two parts. First part is the external quality classification using machine vision system which is based on its shape and mass. The second part is the internal quality classification using near infrared (NIR) spectroscopy, based on its total soluble solid (TSS) value. An image acquisition platform was built to capture the 3- Dimensional image of Harumanis mango in a single acquisition. A real-time measurement calibration technique was developed in this research. Combination of Fourier descriptor parameters and size-shape parameters was used to recognize the shape of Harumanis mango. An improved two-dimensional disk method was used to estimate the volume of Harumanis mango based on the captures image. Then a correlation between the actual volume and actual mass was derived and used to estimate the mass of Harumanis mango on inline system. The proposed method can correctly classify the Harumanis mango according to its shape and mass 94.2% of the time. NIR spectrometer was used to obtain the reflectance wavelength of the Harumanis mango. The juice from the mango was obtained and measured with a refractrometer to obtain the actual TSS value. Then, the acquired NIR wavelength was analysed and correlate with the actual TSS value using multivariate analysis. A regression value of 0.85 for calibration set was found from the analysis, which explained that there was a high correlation between the wavelength and TSS. Stepwise Discriminant analysis method was used to find the significant wavelength that can be used to determine the maturity stage in real-time system. Ten wavelength points were selected and verified on the testing set. The discriminant model can be accurately determined the maturity stage with 85.0% accuracy.

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IoT Monitoring System for Fig in Greenhouse Plantation

2023-07-01 , Mohd Firdaus Ibrahim , Muammar Mohamad Isa , Razaidi Hussin , Syed Muhammad Mamduh Syed Zakaria , Norhawati Ahmad , Yuan C.K.J. , Abdullah A.F.T.

Fig is rich in nutrients and has a high market value due to its extensive application in promoting a nutritious food supply and supporting various medical disciplines. However, the equatorial climate in Malaysia poses significant difficulties for the large-scale cultivation of figs. Therefore, a Smart Monitoring System for controlled Greenhouse Plantation was proposed in this study to enable more efficient cultivation. The proposed system was equipped with LoRa and GSM to overcome the distance and data transmission limitations, developed using the Arduino Uno microcontroller. The proposed system consists of sensors to measure soil moisture, temperature, and humidity, while the data is transmitted using long-range LoRa communication to the control unit. The sensors circuit also has a solar power supply for convenient application in rural areas. The control unit is placed at a location with good data coverage. The system functioned well, and the monitoring parameter was accurately read, collected, and updated every 30 minutes. The optimal temperature, humidity, and soil moisture for growing fig is 22°C-33°C, > 60%, and 50%-60%, respectively. Real-time data monitoring enabled the sensors and control unit to achieve LoRa data transmission over a distance of 2.5 km. Any data exceeding the controlled parameters will trigger an alarm so that the user can perform corrective actions.

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Smart Gateless System using RFID Technology in Universiti Malaysia Perlis

2021-06-11 , Ainur Fasihah Mohd Fazilah , Muzammil Jusoh , Ammar Zakaria , Thennarasan Sabapathy , Mohd Firdaus Ibrahim , Hasliza A Rahim @ Samsuddin , Muhammad Mokhzaini Azizan , Mohd Zakimi Zakaria , Nashrul Fazli Mohd Nasir , Albreem M.A.M. , R Badlishah Ahmad

This paper describes a smart monitoring gate-less system by using RFID technologies that had been implemented in Universiti Malaysia Perlis. The objective of this system is to ease the security to monitoring the cars-in and cars-out from the main gate manually. The security can check and manage the staff by system management in PC. This smart monitoring gate-less system consists of RFID tag antenna that be attached at sticker's staff, RFID reader and software management application. For the software management application, this project used LabView as a simulation tool for the data acquisition. The data which contains of name, staff number, number plate, car model and color will be stored in cloud-based storage. All the information data can be accessed from any location because the security management can access it through mobile. This project will present how efficient the vehicle control application on campus which enable the security guard to check a car sticker either legal or illegal in a shortest way.

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In-Line sorting of Harumanis Mango based on external quality using visible imaging

2016 , Mohd Firdaus Ibrahim , Fathinul Syahir Ahmad Sa'ad , Ammar Zakaria , Ali Yeon Md Shakaff

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

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Deep CNN-based Planthopper classification using a high-density image dataset

2023 , Mohd Firdaus Ibrahim , Siti Khairunniza-Bejo , Marsyita Hanafi , Mahirah Jahari , Mohammad Aufa Mhd Bookeri , Fathinul Syahir Ahmad Sa'ad

Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps.

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