植物工廠萵苣量產整合模型與決策支援

Abstract

本研究旨在建立適用於完全人工光型植物工廠內栽培波士頓萵苣的監控軟硬體與決策支援系統。使用可程式控制器建立了床架上的監控系統，亦開發了量產系統的遠端管理系統。前者包括了可控制栽培床架上各層的溫度、濕度、光量、二氧化碳濃度與養液循環系統。後者提供使用者在遠端即可針對栽培室內的環境進行溫度、濕度、二氧化碳、養液EC與pH與光量等的監控、亦可量測植物的間接生理參數，譬如：濃縮養液補充量與補水量。同一監控軟體還具備生長過程中鮮重的預測、收穫後產量的預估與提供即時影像。 本研究建立了六個理論模型，分別為：(1) 依據收穫前兩週累計的濃縮養液吸收量預測收穫時萵苣鮮重之模型，(2) 依據收穫前兩週累計的補水量與空氣蒸汽壓差預測收穫時萵苣鮮重之模型，(3) 依據日累積光量與二氧化碳濃度探討對波士頓萵苣乾重影響之模型，(4) 依據日累積光量、光譜與二氧化碳探討對波士頓萵苣鮮重生長的影響模型，(5) 基於植物工廠氣密程度 (以每小時換氣率為指標) 探討不同光量與二氧化碳濃度的耦合控制模型，(6) 不同光譜波段對於波士頓萵苣鮮重增加貢獻度的分析模型。 模型1, 2, 3, 4可模擬各環境參數與波士頓萵苣鮮重或乾重之關係，可用於植物工廠管理之決策支援。模型5適合使用者針對自家植物工廠的氣密性來求出可有最低操作成本 (燈光電費與二氧化碳) 的最適光量與二氧化碳濃度設定參數值。模型6提供了評估不同光譜波段對於收穫時鮮重貢獻度的方法，可提供人工光源製造商針對所欲栽培的作物提供適合的光譜建議。 生長過程中萵苣鮮重的偏低、濃縮養液補充量或補水量的不足或偏高都是在提供即時的警訊，提醒管理者注意。可依據訂單大小而進行生產排程的規劃是植物工廠有別於傳統農業的優勢之一，本研究建立的產能預測功能對於生產管理與行銷應可提供助益。

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.