Environmental control design based on optimization of the multi-grid wireless sensors in a greenhouse eggplant cultivation facility
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- ISLAM Md Parvez
- The National Agriculture and Food Research Organization (NARO)
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- MATSUGI Hisashi
- Kochi Prefectural Agricultural Innovation Promotion Division
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- ITAKA Shizu
- The National Agriculture and Food Research Organization (NARO)
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- TOKUDA Kenichi
- The National Agriculture and Food Research Organization (NARO)
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- KOCHI Nobuo
- The National Agriculture and Food Research Organization (NARO)
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- LEE Unseok
- The National Agriculture and Food Research Organization (NARO)
抄録
<p>The greenhouse microclimate is an uncertain nonlinear system and affected by various physico-chemical processes, such as heat and mass transfer between plants, air, growing condition and the plastic cover, plants photosynthesis rate, cultivation methods, CO2 concentration, solar radiation, atmospheric RH, temperature, etc. Growers continuously supplied hot air and CO2 inside the greenhouse for cultivating eggplant during winter season. This causes ununiform distribution of CO2, RH and temperature inside the different area of the greenhouse and potentially risk against disease and pest breakout, water stress condition. Therefore, the quality and accuracy of the microclimate monitoring data for optimal control of the greenhouse microclimate has a great influence on ensuring the high crop yield with good quality throughout the year. In this experiment, we divided the greenhouse into 20 subzones and sensors were setup into three layers (top, middle, bottom). Total 72 wireless sensors (48 temperature sensors, 9 solar radiation sensors, 16 CO2 and RH sensors) with 4 mobile base stations were setup for the greenhouse area of 2910m2. In this paper, we investigated the accuracy, continuation and correlation of the different parameters to optimize the number of sensors for eggplant cultivation all the year round inside a solar type greenhouse.</p>
収録刊行物
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- 人工知能学会全国大会論文集
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人工知能学会全国大会論文集 JSAI2020 (0), 2O4GS1301-2O4GS1301, 2020
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390848250119519232
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- NII論文ID
- 130007857042
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可