Fadhilnor Abdullah
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Preferred name
Fadhilnor Abdullah
Official Name
Fadhilnor, Abdullah
Alternative Name
Abdullah, Fadhilnor
Abdullah, F.
Main Affiliation
Scopus Author ID
57951617900
Researcher ID
GSD-4300-2022

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Antifungal Activity of Essential Oil Extracted from Melaleuca alternifolia Against Pathogenic Fungi on Mango (Mangifera Indica L.) for Mango Postharvest Application

2022-01-01 , Ahmad Anas Nagoor Gunny , Seminai J.J.A. , Mohd Hishamuddin Che Mat , Ahmad A.A. , Yusof R. , Fadhilnor Abdullah

An infection by pathogenic fungi is one of the major problems in post-harvest stage of mangoes. Therefore, the extraction of essential oil from Melaleuca alternifolia is being tested as a natural fungicide for controlling fungus infections of selected mangoes locally known as susu mango. The extraction was performed using Solvent-Free Microwave Assisted Extraction with a yield of 0.57% concentration. The inhibitory effect of this essential oil against isolated fungi from mango was investigated through in-vitro and in-vivo analyses. Distilled water was used as a negative control while chemical fungicide (Globus 5.5) was used as a positive control in the analyses. The mycelial growth inhibition of the extracted essential oil for poisoned food test and disc diffusion method showed 62.50 ± 0.49% and 59.70 ± 0.30%, respectively, for in-vivo experiment which used unwounded and artificially wounded mangoes. The result also demonstrated that the essential oil applied on the mangoes could decrease the disease incidence from 100% to 61.33% for up to 10 days incubation at room temperature compared to that of the control. Hence, the essential oil of Melaleuca alternifolia can act as a green fungicide and is also a promising alternative to the synthetic chemical fungicide for controlling post-harvest disease on mangoes.

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IoT Enabled Mushroom Farm Automation with Machine Learning

2024-06-03 , Shafie Omar , Wan Mohd Faizal Wan Nik , Muhammad Imran Ahmad , Tan Shie Chow , Mohd Nazri Abu Bakar , Shahrul Fazly Man@Sulaiman , Fadhilnor Abdullah , Vikneshwara Ram Suppiah

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

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Measurement of leaf chlorophyll content in Harumanis mango cultivated in a greenhouse using SPAD meter

2023-01-01 , Ahmad N.A. , Muhammad Firdaus Abdul Muttalib , Muhammad Nur Aiman Uda , Zainal Abidin Arsat , Fadhilnor Abdullah , Mohd Khairul Rabani Hashim , Fathin Ayuni Azizan , Jusoh M.F. , Kamaruzaman S.R.S. , Nordin A.A.

The Soil and Vegetation Analysis Development (SPAD) value was correlated to the actual value of chlorophyll content in the Harumanis mango leaf using a developed regression model. Distribution of chlorophyll content in Harumanis mango leaves were mapped using Geospatial Analyst in the ArcGIS. Total chlorophyll content and SPAD value were well established with the polynomial regression model with coefficient of determination (R2) of 0.925. The results show that the measured SPAD value in the morning were comparable to those value made in the evening and the Harumanis leaves located in the middle of the greenhouse have 25–31.3% lower chlorophyll content compared to other parts of the greenhouse.

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Intelligent irrigation system using rain water harvesting system and fuzzy interface system

2021-12 , Azremi Abdullah Al-Hadi , Sukhairi Sudin , Ahmad Z.A , Fathinul Syahir Ahmad Sa'ad , I Ahmad , Fadhilnor Abdullah , Wan Mohd Faizal Wan Nik , A. Deraman , N. M Maliki , S R S Kamaruzaman , S R Romle

Shortage of water has become a predominant problem all over the world as water plays an important role in agriculture, domestic and industry. In certain parts of the world, farmers face problems watering their crops especially during the dry season. Limited water resources with low efficiency greatly affect crop growth. Therefore, this study proposes an intelligent irrigation system using Rain Water Harvesting (RWH) and Fuzzy Interface System (FIS) for crops watering process. The RWH is a system that collects, centralises and stores rainwater, while the FIS uses temperature and soil moisture sensors to determine the time and amount needed for the watering process. Thus, the intelligent irrigation system will ensure the process of watering the crops to be efficient. The results of this study show that FIS can analyse temperature and soil moisture data, which improves the efficiency of crops watering process and the use of RWH will make it sustainable. The developed project is currently operating at the Institute of Sustainable Agro Technology, i.e. a university-owned agricultural research institute.

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Growth Responses of Okra (Abelmoschus esculentus L. Moench) to Selected Plant Growth Regulators

2024-06-03 , Fadhilnor Abdullah , Mohd Zamzuri Mohammad Zain , Syarifah Rokiah Syed Kamaruzaman , Muhammad Nur Aiman Uda , Zainal Abidin Arsat , Muhammad Firdaus Abdul Muttalib , Mohd Khairul Rabani Hashim

This study was conducted to evaluate the effects of two types of plant growth regulators (PGRs) which are gibberellins (GA3) and Paclobutrazol (PBZ) on the growth and photosynthetic pigment (chlorophyll) of Okra (Abelmoschus esculentus L. Moench) plants. Exogenous applications of GA3 and PBZ with different concentrations (i.e. 20, 40, 80 and 100 mg/L) were sprayed on two-week-old Okra plants under the nursery stage. The control plants were only treated with distilled water. The stem diameter (mm) of treated and control plants was measured weekly. At the end of the experimental period, data on growth characteristics such as plant height (cm), leaf area (cm2) and number of leaves were recorded. The estimation of chlorophyll was measured using the SPAD-502 Chlorophyll Meter. Results showed that the plant morphological characteristics of Okra plants were significantly affected by the application of GA3 and PBZ (P<0.0001). In addition, stem growth (expressed as stem cross-sectional area- mm2) of Okra plants was significantly increased with increasing GA3 concentrations. In contrast, applying PBZ reduced Okra plants' stem growth. This study highlighted the major effects of GA3 and PBZ on the growth of Okra plants when planted under tropical climate conditions.

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Plant Disease Classification Using Image Processing Technique

2024-06-03 , Shahrul Fazly Man@Sulaiman , Shafie Omar , Muhammad Imran Ahmad , Wan Mohd Faizal Wan Nik , Tan Shie Chow , Mohd Nazri Abu Bakar , Fadhilnor Abdullah , Asbhir Yuusuf Omar

Agriculture remains pivotal to our economy, with farming playing a central role in revenue generation. Challenges such as pests, plant diseases, and evolving climate patterns pose threats to crop yield and production. Addressing these challenges, timely and accurate detection of plant diseases emerges as imperative. Manual detection, however, remains resource-intensive and often lags. Addressing this gap, this project proposes an innovative image processing-based system for rapidly detecting plant diseases. The system proficiently identifies specific diseases by analyzing images of plant leaves against a curated dataset. The emphasis of this study was on three major diseases: Bacterial Blight (with an accuracy of 98.6%), Alternaria Alternata (98.5714%), and Cercospora Leaf Spot (97.5%). The compelling results underline the system's capacity to swiftly and effectively categorize diseases, offering monoculture farmers an indispensable tool for obtaining prompt, disease-specific insights.

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AI Assisted and IOT Based Fertilizer Mixing System

2024-06-03 , Wan Mohd Faizal Wan Nik , Shahrul Fazly Man@Sulaiman , Muhammad Imran Ahmad , Shafie Omar , Tan Shie Chow , Mohd Nazri Abu Bakar , Fadhilnor Abdullah , Muhammad Khamil Akbar

Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.

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High flex femoral component with slanted pegs improves fixation strength of total knee arthroplasty

2023-01-01 , Zainal Abidin Arsat , Muhammad Nur Aiman Uda , Fadhilnor Abdullah , Mohd Khairul Rabani Hashim , Mutallib M.F.A. , Kadarman A.H. , Ahmed Shokri A. , Aziz M.E. , Shuib S.

High incidences of early aseptic loosening cases have led to numerous analyses on fixation strength of femoral component (FC). This study consists of two objectives: to analyze the correlation between articulating contact area shape conformities and fixation strength, and (2) to analyze the relationship between slanting box patterns of the FC and fixation strength. Two design analyses were constructed: first, using three different types of contact congruence with radius ratios of 0.50, 0.89 and flat. Second, box patterns of the FC were anteriorly slanted at 2, 4 and 6°. The fixation strength of the FC is significantly improved (38.59%) by slanting the box pattern anteriorly as compared to the non-slanted FC design.

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Analysis on Silica and Graphene Nanomaterials Obtained From Rice Straw for Antimicrobial Potential

2024-06-12 , Muhammad Nur Aiman Uda , A Jalil N.H. , Muhammad Firdaus Abdul Muttalib , Fadhilnor Abdullah , Uda Hashim , Subash Chandra Bose Gopinath , Afnan Uda M.N. , Shahidah Arina Shamsuddin , Norizah Abd Karim , Ahmad Radi Wan Yaakub , Ibrahim N.H. , Tijjani Adam , Nor Azizah Parmin , Baharum N.A.

This study focuses on the encapsulation of silica and graphene nanoparticles and their potential applications. The encapsulation enhances the properties and effectiveness of these nanoparticles, with silica providing stability and graphene contributing to high surface area and electrical conductivity. Characterization of silica-graphene nanoparticles was conducted using various techniques including High Power Microscope (HPM), Scanning Electron Microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), and 3D Nano Profiler. The antimicrobial activity of silica, graphene, and silica-graphene nanoparticles was evaluated using a disc diffusion assay against E. coli and B. subtilis at varying concentrations. Results showed significant antimicrobial activity, with the inhibition zone being directly proportional to the concentration. Silica-graphene nanoparticles demonstrated higher efficacy against E. coli compared to B. subtilis, attributed to differences in cell wall structure. Statistical analysis using ANOVA confirmed significant differences in antimicrobial activity among the tested components.

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