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

1
1
1
1
Reset filters

Settings
Now showing 1 - 1 of 1
  • 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.