Fathinul Syahir Ahmad Sa'ad
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Preferred name
Fathinul Syahir Ahmad Sa'ad
Official Name
Fathinul Syahir, Ahmad Sa'ad
Alternative Name
Ahmad Saad, Fathinul Syahir
Sa’ad, Fathinul Syahir Ahmad
Main Affiliation
Scopus Author ID
57205307496
Researcher ID
R-5360-2019

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Analysis of Soil Nutrient (NPK) Test Value - Relative yield Relationship for Harumanis Mango using Modification Arcsine-Log Calibration Curve.

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.K.

The cultivation of Harumanis mango (Mangifera indica) is of significant agricultural importance, especially in tropical regions like Malaysia, where it is renowned for its exceptional taste and quality. Maximizing mango yield and maintaining fruit quality are vital aspects of successful cultivation, relying on optimal soil nutrient management, particularly nitrogen (N), phosphorus (P), and potassium (K). In this research, the soil nutrient test value - relative yield relationship for Harumanis mango is investigated using a modification arcsine-log calibration curve. Traditional linear calibration curves may not fully capture the nonlinearities observed in crop responses, potentially leading to inaccurate nutrient requirements for optimal yield. By employing the innovative modification arcsine-log calibration curve, a more precise and robust relationship between soil nutrient test values and relative mango yield is established. Soil samples are collected from mango orchards, and NPK levels are measured using standardized laboratory techniques, alongside corresponding relative mango yields. This study advances precision agriculture by offering precise soil nutrient recommendations for mango farmers. Utilizing calibrated curves improves mango yield, minimizes nutrient waste, and encourages sustainable farming. In conclusion, the modified arcsine-log calibration curve reveals vital insights for optimal Harumanis mango production, benefiting the industry and sustainability.

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Cloud-based System for University Laboratories Air Monitoring

2020-09-21 , Abu Hassan Abdullah , Sukhairi Sudin , Mustafa M.H. , Fathinul Syahir Ahmad Sa'ad , Khairul Azwan Ismail , Muhammad Aizat Abu Bakar , Mohamed Elshaikh Elobaid Said Ahmed , Abdul Ghapar Ahmad , Zahari Awang Ahmad , Sara Yasina Yusuf

Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.

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Nutrient Requirements and Growth Response of Harumanis Mango (Mangiferaindica L.) during Vegetative Shoot Growth Stages: A Mitscherlich Law Analysis

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.

This study investigates the nutrient requirements of Harumanis mango (Mangifera indica L) during different vegetative shoot growth stages by analyzing the soil nutrient test value-relative growth relationships. The research utilizes the Mitscherlich Law to model the response of mango yield in relation to varying nutrient levels. The data came from experimental plots, and the results show the asymptotic behavior of mango yield for three essential nutrients: nitrogen (N), phosphorus (P), and potassium (K). For vegetative shoot growth1, the asymptotic yield was estimated at 665.5 with a decline rate of -3.39 concerning N, -2.17 concerning P, and -1.35 concerning K. The coefficient of determination (R2) was 0.934, indicating a high goodness of fit for the model. Similar trends were observed for vegetative shoot growth2 and 3, where the asymptotic yields and nutrient decline rates varied accordingly. This study provides crucial insights into Harumanis mango nutrient needs across growth stages, aiding orchard management for sustainable yields. Applying the Mitscherlich Law enhances our understanding of how nutrients affect mango growth. These findings support targeted fertilization, boosting productivity and orchard efficiency. Future research can explore more growth factors and long-term nutrient impacts.

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Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango

2021-12-01 , Noor Shazliza Zakaria , Abu Hassan Abdullah , Fathinul Syahir Ahmad Sa'ad , Muhamad Khairul Ali Hassan , Mohd Nazri Abu Bakar , Saad A.R.M. , Sukhairi Sudin , Ibrahim M.F.

Harumanis mango 'Insidious Fruit Rot'(IFR), is one of the common issues that hampered the fruit quality and consequently lowered the premium value of Harumanis Mango. Physically and visually the affected fruit does not show any attributes that indicates the presence of IFR on any part of the fruit until it has been cut open. This paper investigates the feasibility of a non-destructive method to screen the Harumanis mango from IFR using near-infra red light and artificial neural network. A common NIR light emitting diodes of 1000nm wavelength was used as the light source to emit NIR light while a photodiode was used to measure the intensity of the reflected NIR light from Harumanis mango. Early detection of IFR were done manually by local expert using acoustic method by flicking fingers to detect any abnormality inside the fruit. Sample data on NIR Spectroscopy reflectance results of 120 samples were used to classify the presence of IFR using neural network. Mean value of NIR reflectance of RBG for Harumanis mango with an incidence of Insidious Fruit Rot are R= 0.651, G= 0.465 and B=0.458, while without IFR are R = 0.211, G=0.15 and B=0.146. Using MATLAB's neural network training tool, a training set regression was obtained with an accuracy value of 0.9805 for prediction of IFR, thus this value is very high in accuracy.

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Cycling performance prediction based on cadence analysis by using multiple regression

2021-12-01 , Sukhairi Sudin , Aziz Naim Abdul Aziz , Fathinul Syahir Ahmad Sa'ad , Nurul Syahirah Khalid , Ismail Ishaq Ibrahim

This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000.Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days' worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable's mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

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Holonomic Mobile Robot Planners: Performance Analysis

2022-01-01 , Aljamali Y.S. , Muhammad Juhairi Aziz Safar , Khairul Salleh Basaruddin , Yazid H. , Basha S.N. , Fathinul Syahir Ahmad Sa'ad , Hassan M.K.A.

Many algorithms have been proposed to tackle the path planning problem in mobile robots. Among the well-known and established algorithms are the Probabilistic Road Map (PRM) algorithm, A* algorithm, Genetic algorithm (GA), Rapidly-exploring random tree (RRT), and dual Rapidly-exploring random trees (RRT-connect). Hence, this paper will focus on the performance comparison between the aforementioned algorithms concerning computation time, path length, and fail and success rate for producing a path. For the sake of fair and conclusive results, simulation is conducted in two phases with four different environments, namely, free space environment, low cluttered environment, medium cluttered environment, and high cluttered environment. The results show that RRT-connect has a high success rate in producing a feasible path with the least computation time. Hence, RRTs-based sampling algorithms, in general, and RRT-connect, in specific, will be explored in-depth for possible optimization.

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Specific Gravity-based of Post-harvest Mangifera indica L. cv. Harumanis for ‘Insidious Fruit Rot’ (IFR) Detection using Image Processing

2020-01-01 , Nurul Syahirah Khalid , Shazmin Aniza Abdul Shukor , Fathinul Syahir Ahmad Sa'ad

Bruising and internal defects detection is a huge concern for food safety supplied to the consumers. Similar to many other agricultural products, Harumanis cv. has non-uniform quality at harvesting stage. Traditionally, in adapting the specific gravity approach, farmers and agriculturist will estimate the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. by using floating techniques based on differences in density concept. However, this method is inconvenient and time consuming. In this research, image processing is explored as a method for non-destructive measurement of specific gravity to predict the absence of ‘Insidious Fruit Rot’ (IFR) in Harumanis cv. The predicted specific gravity of 500 Harumanis cv. samples were used and compared with the actual result where it yielded a high correlation,R2 at 0.9055 and accuracy is 82.00%. The results showed that image processing can be applied for non-destructive Harumanis cv. quality evaluation in detecting IFR.

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Performance analysis of multi-level thresholding for microaneurysm detection

2022-09-01 , Choong K.H. , Shafriza Nisha Basah , Haniza Yazid , Muhammad Juhairi Aziz Safar , Fathinul Syahir Ahmad Sa'ad , Lim Chee Chin

Diabetic retinopathy (DR) – one of the diabetes complications – is the leading cause of blindness among the age group of 20–74 years old. Fortunately, 90% of these cases (blindness due to DR) could be prevented by early detection and treatment via manual and regular screening by qualified physicians. The screening of DR is tedious, which can be subjective, time-consuming, and sometimes prone to misclassification. In terms of accuracy and time, many automated screening systems based on image processing have been developed to improve diagnostic performance. However, the accuracy and consistency of the developed systems are largely unaddressed, where a manual screening process is still the most preferred option. The main contribution of this paper is to analyse the accuracy and consistency of microaneurysm (MA) detection via image processing by focusing on Otsu’s multi-thresholding as it has been shown to work very well in many applications. The analysis was based on Monte Carlo statistical analysis using synthetic retinal images of retinal images under variation of all stages of DR, retinal, and image parameters – intensity difference between MAs and blood vessels (BVs), MA size, and measurement noise. Then, the conditions – in terms of obtainable retinal and image parameters – that guarantee accurate and consistent MA detection via image processing were extracted. Finally, the validity of the conditions to guarantee accurate and consistent MA detection was verified using real retinal images. The results showed that MA detection via image processing is guaranteed to be accurate and consistent when the intensity difference between MAs and BVs is at least 50% and the sizes of MAs are from 5 to 20 pixels depending on measurement noise values. These conditions are very important as a guideline of MA detection for DR.

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Harumanis mango quality assessments technique based on high level features fusion of infra-red thermal and optical image

2017 , Fathinul Syahir Ahmad Sa'ad

Mangoes imported from other parts of the world, especially Malaysia, Thailand, Mexico and the Philippines, are usually available all year round but in Perlis, Malaysia there is one unique and famous mango is Harumanis mango and this fruit is seasonal. Every year, a large amount of mangoes are produced and need to be evaluated for quality assessments. Presently, the quality inspection was done manually by the quality expert as there are no automated grading system is available. Hence, by automating the procedure as well as developing new classification technique, it may solve these problems. This thesis presents the new method on the high level features fusion of visible and IR Thermal Image features for mango quality assessment. A shape and weight analysis was developed from visible imaging and a maturity analysis was developed from IR thermal imaging. A Fourier-Descriptor method was developed to grade mango by its shape and a cylinder analysis method was used to grade Harumanis mango by its weight and it give different accuracy result of classification. The spectrum of infrared image was used to distinguish and classify the level of maturity of the fruits and it gave low accuracy compare to shape and weight classification. To get high accuracy for quality assessment for Harumanis mango, high level data fusion was proposed. This method combined all three classifier of shape, weight and maturity and it was found to be able to achieve 98% accuracy classification.

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Analysis on Clustering Based Method for Diabetic Retinopathy Using Color Information

2022-01-01 , Selvam S.A , Haniza Yazid , Shafriza Nisha Basah , Fathinul Syahir Ahmad Sa'ad , Muhamad Khairul Ali Hassan

Diabetic Retinopathy (DR) is an important global health concern and it can causes blindness. Early detection and treatment can prevent the patients from loss their vision. This study presents an approach of color image segmentation for automatic exudate detection. The color retinal images are converted into four different color spaces and preprocessed by applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Fuzzy C-Means (FCM) and K-means clustering (KMC) algorithms are applied on the preprocessed image for the segmentation purpose. Then, optic disc is detected and eliminated by using Circular Hough Transform (CHT). Performance evaluation of developed algorithm is done using Structured Analysis of the Retina (STARE) dataset. The proposed algorithm achieved sensitivity of 93.4% for STARE datasets for LUV color space with KMC.

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