Zol Bahri Razali
Thumbnail Image
Preferred name
Zol Bahri Razali
Official Name
Zol Bahri, Razali
Alternative Name
Razali, Zol Bahri
Razali, Z. B.
Main Affiliation
Scopus Author ID
55050201000
Researcher ID
AAD-8927-2019

Research Output

Filters

4 1
4
Reset filters

Settings
Now showing 1 - 4 of 4
  • Publication
    Think-aloud Technique in Assessing Practical Experience: A Pilot Study
    ( 2020-12-18)
    Zol Bahri Razali
    ;
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Affendi N.S.S.R.
    ;
    Daud M.H.
    The learning domains such as cognitive, affective and psychomotor for Engineering Technology programs should be identified and valued. The acquisition of hands-on experience in workplace settings and laboratory classes is just as important as explicit technical knowledge, and should be measured in psychomotor domain. However, the explicit knowledge is valued in engineering technology education. Furthermore, practically all assessments measure cognitive value. This implicit devaluation of hands-on experience could significantly impair engineering technology students' ability to acquire and value practical skills. Therefore, developing a new model to include effective assessment in psychomotor domain could be one way to overcome this problem. Thus, the aim of this project is to find ways to measure changes in hands-on experience in engineering laboratory classes. The second aim is to test the relationship between hands-on experiences acquired in laboratory classes with the ability to diagnose simple experiment faults in laboratory arrangements. The method of think-aloud is used in the research where the finding of students' attainment is compared to experts' acquisition. The results show that the value of psychomotor domain in laboratory classes via hands-on experience can be assessed and valued between two groups of students which is experiment and control group. Methodologies and detail results for this research are described in this project.
  • Publication
    Development of a Multi-Fan System (MFS) in a Plant Factory with Artificial Light
    ( 2022-01-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Zol Bahri Razali
    ;
    Wan Azani Wan Mustafa
    ;
    Muhammad Naufal Mansor
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Akbar M.F.
    ;
    Osman M.K.
    ;
    Setumin S.
    ;
    Idris M.
    ;
    Bin Ramli M.A.
    ;
    Sharifful Mizam N.S.
    A plant factory is a factory that grows plants indoors. These indoor farms could be the key to solve food shortages in the world. Plant factories are operated in indoor spaces under controlled cultivation conditions such as light, temperature and humidity. Then, a multi-fan system (MFS) for single culture beds. The MFS had four fans which were installed on both the front and back sides of culture beds to generate airflow from two opposite horizontal directions by using the Internet of Things (IoT) via the access and connection of smartphone devices. The fans that push the air into the culture bed were air inlets while those that pull the air out of the culture bed were air outlets. The main problem is in plant factories with artificial light, a heat that is usually used to control the environmental parameters and the air velocity is generally lower than the optimum range required for plant growth. Compare to a plant factory without using a multi-fan, it no circulation of air in the container to ensure continuous gas exchange. This reduction in gas exchange can impact calcium uptake by the plants. The gas exchange makes the tip burn. Tip burn can have a significant impact on the salability of a lettuce crop. Based on the limitations that have been highlighted previously, this research has been carried out by using multi-fan and without multi-fan. To get the data that need to be compared. Then, to improve the airflow in a plant factory with artificial light and prevent tip burn occur on the lettuce itself. In a nutshell, this prototype is expected to help plant factories reduce tip burn symptoms on leaf lettuce and the airflow can improve the growth of indoor cultured lettuce.
  • Publication
    Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)
    ( 2025-01-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Zol Bahri Razali
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Samsul Setumin
    ;
    Muhammad Khusairi Osman
    ;
    Mohaiyedin Idris
    ;
    Akbar M.F.
    ;
    Premavathy Kunasakaran
    ;
    Muhammad Zubir Zainol
    ;
    Nor Syamina Sharifful Mizam
    As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth-related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).
  • Publication
    Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN
    ( 2025-01-01)
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Muhammad Naufal Mansor
    ;
    Zol Bahri Razali
    ;
    Mustafa W.A.
    ;
    Ahmad Anas Nagoor Gunny
    ;
    Setumin S.
    ;
    Osman M.K.
    ;
    Idris M.
    ;
    Akbar M.F.
    ;
    Farid W.M.F.N.M.
    ;
    Zainol M.Z.
    ;
    Mizam N.S.S.
    Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.