Abu Hassan Abdullah
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Abu Hassan Abdullah
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Abdullah, Abu Hassan
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26029734700

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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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Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

2023-01-01 , Salleh N.M. , Abu Hassan Abdullah , Sukhairi Sudin , Noor Shazliza Zakaria

Utilising single or multiple modalities systems, non-destructive techniques have been used to assess and determine the quality of mango (magnifera indica L.). It is challenging to anticipate and varies by cultivar at what harvest maturity stage will result in the optimum postharvest quality. Insidious Fruit Rot (IFR) is a disease that affects mangoes. When infected with Insidious Fruit Rot (IFR), the mango variety Harumanis does not exhibit exterior mutilation at the time of harvest or during the mature stage. However, a lack of density in the sinus area can occasionally be detected. Traditional ways of locating the diseases or pests living in the mango are useless for the commercialization of the product. This research presents the investigation done on IFR infection detection using piezoelectric vibration sensors and electret microphones. Data derived by the sensors were processed using the PCA and Random Forest methods to determine the non-IFR and the mango afflicted with IFR. The proposed approach achieved correct classification and is expected to be useful for planters in detecting IFR correctly before Harumanis mangoes were marketed.

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Integration of asset tracking system through trilateration method as detection mechanism

2019 , M A Fadzilla , Shahriman Abu Bakar , Azizi Harun , Mohd Sani Mohamad Hashim , Zuradzman Mohamad Razlan , Wan Khairunizam Wan Ahmad , Z. Ibrahim , Abu Hassan Abdullah , J.S.C Turner , K.A.A Kassim , M.S.A Khalid , Z. Jawi , M.H.M Isa , Rizalafande Che Ismail

Demands for localization system has been growing rapidly in the last several years both for an outdoor and indoor area. In conjunction with this, the capability and reliability of this system to precisely locate and track objects of interest for the indoor area has catered researchers and study on how to do so. One of the major ideas on making it more advance is by incorporating the use of wireless devices into the system. There are numbers of issues that could interrupt the efficiency and success of the system. One of the main problems is the signal loss mainly caused by the attenuation of the signal as they propagate through from the transmitter to the receiver. These attenuations are mostly due to the surface types the signal are traveling on and the objects that are in the Line of Sight in between the transmitter and receiver. In order to ensure the most reliable and efficient wireless connection between transmitter and receiver, a propagation study on the signal is needed for us to analyze and find the best way to trade off the signal attenuation based on the environment surrounding the system. By doing so, a thorough system that has models that can work efficiently even if we are to consider the attenuation factors. The system consists of nodes installed inside the research institute that acts as both transmitter and receivers. The transmitter and receiver will then process the signal that will then determine their location. The receiver is connected to the laptop in order to get a real-time reading so that we will be able to locate the transmitter. A networked of nodes are installed inside the research institute for experiment and the layout of the research is conferred for future references. Data from the experiment are then analyzed and a model for the signal propagation alongside the research institute is created. This model will be able to apprehend the signal attenuation despite the surrounding environment such as furniture and walls. A completed asset tracking system with models of signal attenuation will be built in the future for a more efficient signal transmission.

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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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Design and Development of IoT based Garbage Monitoring and Management System

2021-12-01 , Wan Mohd Nooriman Wan Yahya , Lim R.Y. , Rudzuan M.N. , Sofi Y. , Fauzi M.M. , Abu Hassan Abdullah

Daily garbage production causes increasing in garbage management and cleansing cost which required an approach for better monitoring and management system to be applied. Currently, demand of IoT keep increasing as a part of Industrial Revolution 4.0 (IR4.0). Eventually, this IoT based Garbage Monitoring and Management System has been developed. For hygiene, self-opening dustbin lid is applied and IoT technology is used to integrate better garbage monitoring and management into an innovative and effective system. The development of this smart bin uses a pair of infrared sensors and an ultrasonic, push notifications were developed in Blynk application, and user-friendly infographic data is designed on webpage for monitoring and garbage management purposes. The smart dustbin notifies the user when the garbage level exceeds 80%. The dustbin sends a push notification to the user's phone to alert the user to take actions needed before the bin exceed the limit.

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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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Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles

2021-12-01 , Wan Mohd Nooriman Wan Yahya , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Erdy Sulino Mohd Muslim Tan

Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia's state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree's demands is critical. The aim of this study is to create a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.

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Electronic Nose Testing for Confined Space Application Utilizes Principal Component Analysis and Support Vector Machine

2020-12-18 , Muhammad Aizat Abu Bakar , Abu Hassan Abdullah , Wan Azani Wan Mustafa , Zol Bahri Razali , Syahrul Affandi Saidi , Mohamed Mydin Hj M.Abdul Kader , Aman M.N.S.B.S.

A confined space has a limited space for entry and exit but it is large enough for workers to enter and perform work inside. It is not designed for continuous occupancy because it can contribute atmospheric hazards accidents that threaten the worker safety and industry progress. In this work, we reported the testing an instrument to assist workers for atmosphere testing during pre-entry. An electronic nose (e-nose) using specific sensor arrays is the integration between hardware and software that able to sense different concentrations of gases in an air sample using pattern recognition techniques. The instrument utilizes multivariate statistical analysis which is Principal Component Analysis (PCA) for discriminate the different concentrations of gases and the Support Vector Machine (SVM) to classify the acquired data from the air sample. The instrument was successfully tested using diesel, gasoline, petrol and thinner. The results show that the instrument able to discriminate an air sample using PCA with total variation for 99.94%, while the classifier success rate for SVM indicates at 98.21% for train performance and 95.83% for test performance. This will contribute significantly to acquiring a new and alternative method of using the instrument for monitoring the atmospheric hazards in confined space to ensure the safety of workers during work progress in a confined space.

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Defects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction

2021-12-01 , Mohd Nazri Abu Bakar , Abu Hassan Abdullah , Rahim N.A. , Haniza Yazid , Zakaria N.S. , Omar S. , Nik W.M.F.W. , Bakar N.A. , Sulaiman S.F. , Ahmad M.I. , Ahmad K. , Maliki N.M. , Romle S.R.

Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.

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A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

2011-08 , Ammar Zakaria , Ali Yeon Md Shakaff , Masnan, Maz Jamilah , Norazian Subari , Nazifah Ahmad Fikri , Abdul Hamid Adom , Mohd Noor Ahmad , Mahmad Nor Jaafar , Latifah Munirah Kamarudin , Abdul Hallis Abdul Aziz , Abu Hassan Abdullah , Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

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