The Experts below are selected from a list of 17007 Experts worldwide ranked by ideXlab platform
Li-chun Huang - One of the best experts on this subject based on the ideXlab platform.
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Consumer Attitude, Concerns, and Brand Acceptance for the Vegetables Cultivated with Sustainable Plant Factory Production Systems
Sustainability, 2019Co-Authors: Li-chun HuangAbstract:Plant factories are perceived as a sustainable agricultural production system, since they provide a cultivation environment for growing agricultural crops with less resource consumption and no pesticide use. However, as the industry and academic participants have been contributing in the development of Plant Factory technology, consumer acceptance for the crops cultivated from that technology remains unknown. Without consumer acceptance, all the costs spent in the research and development (RD) of Plant factories cannot gain the profit. To address this deficiency, this study was aimed to: (1) investigate consumers’ attitudes, concerns and willingness to pay for the vegetables cultivated with Plant factories, (2) explore the branding mode that is most effective for selling Plant Factory vegetables to consumers, and (3) determine the influence of consumers’ socio-demographics and vegetable purchase behavior for their willingness to pay for Plant Factory vegetables. With a modified strategy of multi-stage cluster sampling, a consumer survey was conducted and 390 valid questionnaires were obtained for statistical analysis. Data were analyzed with descriptive statistical analysis, analysis of variance, Duncan’s post hoc analysis, and regression analysis to meet the study objectives. The study results indicated that over half of the subjects appreciated the value of Plant Factory technology. However, as high as 64.4% of the subjects revealed concerns. Most of the concerns were about the issues of environmental pollution and food safety. It also showed that price played a decisive role for consumers’ purchase intentions to Plant Factory vegetables. Moreover, consumers were more willing to pay a higher price for the Plant Factory vegetables labeled with an allied brand of academic institutes and private corporations, compared with those labeled with other types of brand. Consumers who had higher income and/or consume more organic vegetables were also more willing to pay for the Plant Factory vegetables. The study findings help the industry participants to build up effective market strategies for selling the crops cultivated with sustainable Plant Factory systems.
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Food-Energy Interactive Tradeoff Analysis of Sustainable Urban Plant Factory Production Systems
Sustainability, 2018Co-Authors: Li-chun Huang, Yu-hui Chen, Ya-hui Chen, Chi-fang WangAbstract:This research aims to analyze the food–energy interactive nexus of sustainable urban Plant Factory systems. Plant Factory systems grow agricultural products within artificially controlled growing environment and multi-layer vertical growing systems. The system controls the supply of light, temperature, humidity, nutrition, water, and carbon dioxide for growing Plants. Plant factories are able to produce consistent and high-quality agricultural products within less production space for urban areas. The production systems use less labor, pesticide, water, and nutrition. However, food production of Plant factories has many challenges including higher energy demand, energy costs, and installation costs of artificially controlled technologies. In the research, stochastic optimization model and linear complementarity models are formulated to conduct optimal and equilibrium food–energy analysis of Plant Factory production. A case study of Plant factories in the Taiwanese market is presented.
Joon-ho Cho - One of the best experts on this subject based on the ideXlab platform.
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A Study on Modular Smart Plant Factory Using Morphological Image Processing
Electronics, 2020Co-Authors: Bong-hyun Kim, Joon-ho ChoAbstract:This paper is a study on a modular smart Plant Factory integrating intelligent solar module, LED module with high efficiency for Plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to Plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the Plant Factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on Plant growth and status was confirmed.
Haruhiko Murase - One of the best experts on this subject based on the ideXlab platform.
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The Latest Development of Laser Application Research in Plant Factory
Agriculture and Agricultural Science Procedia, 2015Co-Authors: Haruhiko MuraseAbstract:Abstract Essential resource elements in crop production are light, water, carbon dioxide and fertilizer. Optimum design for air-conditioning and lightings in Plant Factory system is required to realize the economical operation of Plant Factory because most energy consuming elements of Plant Factory system are air-conditioning and lightings. In this sense application of solid state lighting sources such as LED in Plant Factory system have been promoted to expect some reduction of running cost in lighting. Research work of solid state laser application in Plant Factory has just started recently at Osaka Prefecture University Plant Factory Research Center. The latest development of laser application research in Plant Factory will be reported in this article.
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Applications of intelligent machine vision in Plant Factory
IFAC Proceedings Volumes (IFAC-PapersOnline), 2014Co-Authors: Yusuf Hendrawan, Dimas Firmanda Al Riza, Haruhiko MuraseAbstract:© IFAC. Intelligent machine vision has been widely used in Plant Factory for many purposes. There are two aims in this study i.e. the first is improving the performance of intelligent machine vision for precision irrigation system using optimized feature selection technique and the second is developing intelligent machine vision for precision artificial lighting system using Light Emitting Diode (LED). The proposed feature selection technique used in the first aim is Neural-Discrete Hungry Roach Infestation Optimization (N-DHRIO) algorithm. The intelligent machine vision for precision irrigation system and the precision LED lighting system have successfully been developed, and it shows effective to control moisture content and light intensity of the Plant precisely. In large scale Plant Factory, those systems can optimize Plant growth and reduce the water consumption and energy costs.
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Laser application on Plant Factory
2013Co-Authors: Haruhiko MuraseAbstract:Essential resource elements in crop production are light, water, carbon dioxide and fertilizer. Optimum design for air-conditioning and lightings in Plant Factory system is required to realize the economical operation of Plant Factory because most energy consuming elements of Plant Factory system are air-conditioning and lightings. In this sense application of solid state lighting sources such as LED in Plant Factory system have been promoted to expect some reduction of running cost in lighting. Research work of solid state laser application in Plant Factory has just started recently at Osaka Prefecture University Plant Factory Research Center. The latest development of laser application research in Plant Factory will be reported in this article.
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Effects of Airflow for Lettuce Growth in the Plant Factory with an Electric Turntable
IFAC Proceedings Volumes, 2013Co-Authors: Toru Nishikawa, Hirokazu Fukuda, Haruhiko MuraseAbstract:Abstract To promote cultivation performance by controlling the air flow in Plant Factory, we introduced a new rotation mechanism for the alleviation of the inhomogeneous of airflow by the rotation of Plant. The combination of air flow control and rotation mechanism makes about 20% large lettuce compared with the normal condition.
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Development of Precision Irrigation System using Machine Vision in Plant Factory
2011 Louisville Kentucky August 7 - August 10 2011, 2011Co-Authors: Yusuf Hendrawan, Haruhiko MuraseAbstract:In a Plant Factory, optimal control for obtaining higher yield, higher production efficiency, minimum waste, and better quality of Plants is essential. Sunagoke moss is one of the Plant products which are cultivated in Plant Factory. One of the primary determinants of moss growth is water availability. Hence, there is need to develop precision irrigation for moss production in Plant Factory. The present work attempted to develop machine vision-based micro-precision irrigation system to optimize water use in Plant Factory and maintain the water content of moss constantly in optimum growth condition. The specific objective of this study is to propose nature-inspired algorithms to find the most significant set of image features suitable for predicting water content of cultured Sunagoke moss. Feature Selection (FS) methods include Neural-Genetic Algorithms (N-GAs) and Neural-Discrete Particle Swarm Optimization (N-DPSO), Neural-Honey Bee Mating Optimization (N-HBMO) and Neural-Fish Swarm Intelligent (N-FSI). Image features consist of color features and textural features with the total of 212 features extracted from grey, RGB, HSV, HSL, L*a*b*, XYZ, LCH and Luv color spaces. Back-Propagation Neural Network (BPNN) model performance was tested successfully to describe the relationship between water content of Sunagoke moss and image features. FS methods improve the prediction performance of BPNN.
Chi-fang Wang - One of the best experts on this subject based on the ideXlab platform.
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Food-Energy Interactive Tradeoff Analysis of Sustainable Urban Plant Factory Production Systems
Sustainability, 2018Co-Authors: Li-chun Huang, Yu-hui Chen, Ya-hui Chen, Chi-fang WangAbstract:This research aims to analyze the food–energy interactive nexus of sustainable urban Plant Factory systems. Plant Factory systems grow agricultural products within artificially controlled growing environment and multi-layer vertical growing systems. The system controls the supply of light, temperature, humidity, nutrition, water, and carbon dioxide for growing Plants. Plant factories are able to produce consistent and high-quality agricultural products within less production space for urban areas. The production systems use less labor, pesticide, water, and nutrition. However, food production of Plant factories has many challenges including higher energy demand, energy costs, and installation costs of artificially controlled technologies. In the research, stochastic optimization model and linear complementarity models are formulated to conduct optimal and equilibrium food–energy analysis of Plant Factory production. A case study of Plant factories in the Taiwanese market is presented.
Bong-hyun Kim - One of the best experts on this subject based on the ideXlab platform.
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A Study on Modular Smart Plant Factory Using Morphological Image Processing
Electronics, 2020Co-Authors: Bong-hyun Kim, Joon-ho ChoAbstract:This paper is a study on a modular smart Plant Factory integrating intelligent solar module, LED module with high efficiency for Plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to Plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the Plant Factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on Plant growth and status was confirmed.
