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The Artificial Intelligence Optimization Model for Urban Smart Farm Crop Cultivation

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Titel: The Artificial Intelligence Optimization Model for Urban Smart Farm Crop Cultivation
Autoren: Hyunjin Chun
Quelle: International Journal of High Speed Electronics and Systems.
Verlagsinformationen: World Scientific Pub Co Pte Ltd, 2025.
Publikationsjahr: 2025
Beschreibung: Recently, in the era of the Fourth Industrial Revolution, many smart farms have been built in urban spaces. These smart farms are called urban smart farms. In addition, with the development of the Internet of Things, urban smart farms can solve the problem of labor shortage through automation. Also, urban smart farms are recognized as a future agricultural revolution because they can quickly provide fresh crops to the city. However, although it is widely used in developed countries such as the United States, Korea, and Japan, urban smart farms are still not enough to be used in developing countries. Therefore, in this paper, an artificial intelligence optimization model for crop cultivation in an urban smart farm was studied. A qualitative research method was implemented in this study. As a qualitative research method, participatory observation on urban smart farms was conducted. The researcher visited an urban smart farm in Korea and conducted an in-depth interview with the smart farm operator. Based on the qualitative research, simulations are performed on the effects of smart farms using artificial intelligence. A total of five smart farms are the subject of the study. The size of each smart farm is variously composed of 100[Formula: see text]m2 to 300[Formula: see text]m2. And the period of such simulation is one year from January to December 2024. As for the types of crops to be simulated, representative greenhouse crops target tomatoes, cucumbers, and lettuce. The main results of this study are as follows. Applying the optimization model reduces crop harvesting age (days) compared to traditional cultivation methods, increasing crop yields by 20–30%. In addition, the consumption of nutrients in these models is reduced by more than 15%. And in terms of plant disease management, these artificial intelligence models are also effective. Because the model predicts the possibility of disease by analyzing temperature and humidity, the disease incidence rate decreases by about 10%. The economic, productivity, and disease management effects of this model were verified through field simulations of smart farms applying artificial intelligence. These studies can be used as theoretical data for crop cultivation for operators in an urban smart farm.
Publikationsart: Article
Sprache: English
ISSN: 1793-6438
0129-1564
DOI: 10.1142/s0129156425407818
Dokumentencode: edsair.doi...........07e4e3f74989538e4e06dddec89f7fb4
Datenbank: OpenAIRE
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  Data: The Artificial Intelligence Optimization Model for Urban Smart Farm Crop Cultivation
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  Data: Recently, in the era of the Fourth Industrial Revolution, many smart farms have been built in urban spaces. These smart farms are called urban smart farms. In addition, with the development of the Internet of Things, urban smart farms can solve the problem of labor shortage through automation. Also, urban smart farms are recognized as a future agricultural revolution because they can quickly provide fresh crops to the city. However, although it is widely used in developed countries such as the United States, Korea, and Japan, urban smart farms are still not enough to be used in developing countries. Therefore, in this paper, an artificial intelligence optimization model for crop cultivation in an urban smart farm was studied. A qualitative research method was implemented in this study. As a qualitative research method, participatory observation on urban smart farms was conducted. The researcher visited an urban smart farm in Korea and conducted an in-depth interview with the smart farm operator. Based on the qualitative research, simulations are performed on the effects of smart farms using artificial intelligence. A total of five smart farms are the subject of the study. The size of each smart farm is variously composed of 100[Formula: see text]m2 to 300[Formula: see text]m2. And the period of such simulation is one year from January to December 2024. As for the types of crops to be simulated, representative greenhouse crops target tomatoes, cucumbers, and lettuce. The main results of this study are as follows. Applying the optimization model reduces crop harvesting age (days) compared to traditional cultivation methods, increasing crop yields by 20–30%. In addition, the consumption of nutrients in these models is reduced by more than 15%. And in terms of plant disease management, these artificial intelligence models are also effective. Because the model predicts the possibility of disease by analyzing temperature and humidity, the disease incidence rate decreases by about 10%. The economic, productivity, and disease management effects of this model were verified through field simulations of smart farms applying artificial intelligence. These studies can be used as theoretical data for crop cultivation for operators in an urban smart farm.
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