Integrated Model and Decision Support System for Lettuce Production in Plant Factory

• Main Authors: Shih-Wei Kong, 康世緯
• Other Authors: 方煒
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/72828864143275859629

博士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 103 === This study focuses on the development of monitoring and control systems and decision support models for the production of Boston lettuce in a plant factory using artificial light. Programmable logic controller was used for the development of on-site monitoring and control system. The system can control the temperature, relative humidity, carbon dioxide concentration, EC and pH of nutrient and light intensity of each layer on a cultural bench for the production of hydroponically grown lettuce. A remote monitoring system was also developed capable of monitoring parameters mentioned above and also the amount of concentrated nutrients and supplemented water supplied during the growth period were recorded. The system also provided with the capability of predicting the fresh mass during the cultural period and harvested fresh mass. The real time image of plants can also be recorded through web camera. Totally six theoretical models were developed. They are: (1) Prediction of harvested fresh mass based on accumulative absorption of concentrated nutrient solutions during the growth periods. (2) Prediction of harvested fresh mass based on accumulative make-up water and vapor pressure deficit of air during the growth periods. (3) Dry mass prediction model based on daily light integral and carbon dioxide concentration. (4) Fresh mass prediction model based on daily light integral, carbon dioxide concentration and light spectrum. (5) Optimizing operating cost based on varying quanta and carbon dioxide concentration subject to hourly air exchange rate of the plant factory. (6) Relative contributions of various visible spectra on harvested dry mass of lettuce. Models 1, 2, 3 and 4 can be served as decision support tools capable of providing calculated harvested fresh and dry mass based on proposed growth related parameters. Model 5 is helpful in optimizing amount of light intensity and concentration of carbon dioxide subjected to the air tightness of the plant factory. Model 6 provided a systematic approach for the evaluation of the relative contribution of different spectra for the accumulation of harvested fresh mass. This can be very helpful to the manufacturers for the development of artificial grow light for plants. During the growth periods, situations such as low growth rate, too much or too little supplied of concentrated nutrient solutions and make-up water were all symptoms for none efficient growth and can be considered as early alarm. The function to predict amount of harvested fresh mass is also a great tool for production scheduling and marketing.