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Aihsan, M. Z.; Kimpol, Norhanisa Binti; Fang, Liew Hui; Fahmi, M. I.; Mustafa, Wan Azani; Jobran, Junaidah Ali Mohd; Kader, M. M. A.

doi:10.1088/1757-899x/932/1/012075

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…The pot used for every hydroponic system is four pots which means the total pot will become eight. In this experiment, the same step has been taken from previous assessment [19]. The total image taken for the whole research would become approximately 23040 images to be used in the deep learning training.…”

Section: Fig 2 Hydroponic Systemmentioning

confidence: 99%

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN

Mohamed Mydin M. Abdul Kader,

Muhammad Naufal Mansor,

Zol Bahri Razali

et al. 2024

ARASET

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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.

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Section: Fig 2 Hydroponic Systemmentioning

confidence: 99%

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN

Mohamed Mydin M. Abdul Kader,

Muhammad Naufal Mansor,

Zol Bahri Razali

et al. 2024

ARASET

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“…The training progresses from processing the images for every pH level class which have been created. In this experiment, the same step has been taken from previous assessment [20].…”

Section: Data Split To 6 Classes According To the Ph Levelmentioning

confidence: 99%

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

Mohamed Mydin M. Abdul Kader,

Muhammad Naufal Mansor,

Wan Azani Mustafa

et al. 2024

ARASET

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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).

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“…The demand for electricity is growing and to reduce the emission of carbon dioxide gases during the power conversion leads the power electronic engineers to choose the power sources that are nonconventional. Amongst all the clean energy sources, Solar photovoltaic (PV) are attracting greater interest because of their cleanliness [1][2][3][4]. As well as infinite availability of nature.…”

Section: Introductionmentioning

confidence: 99%

“…Since it needs low maintenance cost, it plays an important role in the electric power generation. The voltage harvested from the PV system is direct voltage (DC) [1][2][3][4]. The conversion of availed C D voltage to C A voltage before connected to the load performed by two different configurations, namely the quadratic boost conversion system and DC to AC voltage conversion using H-bridge inverter [8,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

High Gain Transformer-less DC/AC Inverter for PV System

Zunnurain

Hariz

Ilham

et al. 2021

J. Phys.: Conf. Ser.

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Investigation interests on many scientific aspects of photovoltaic (PV) trans-former-less inverter system has improved over the past decade. Using step-up transformer or high frequency transformer in electrical system has made the entire system expensive and voluminous. There is alternative topology to replace the transformer by implementing DC/DC quadratic boost converter to expand the voltage from 12VDC to 325VDC from the photovoltaic (PV) solar and convert it to AC applying H-bridge inverter circuit. This method will replace the conventional method of bulky transformer into a lighter converter with the same performance. The circuit is simulated using Power Sim (PSIM) software to initiate the design and study the circuit capability. The experimental result will project the exact voltage in the range of 230VACrms . The harmonic profile of the inverter is studies and compared with the normal inverter configuration.

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Cited by 12 publications

References 5 publications

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

High Gain Transformer-less DC/AC Inverter for PV System

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