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低成本之智慧自動化溫室系統與紅外線等數據分析

摘要


溫室具有隔離外界環境影響以及提供適合農作物生長的環境等優點,可以有效的提高農作物的產量以及生長品質。但是傳統的溫室建置需要昂貴的成本以及傳統控制界面不夠友善(或者只是普通的手動排程設定),大大降低了溫室的方便性。隨著雲時代的來臨、低成本的物聯網建置越來越成熟,我們可以用這些低成本高效率的建置來收集監控植物生長資訊,再透過這些收集的大量資料(例:天氣變化數據、農作物生長數據、病蟲害分析等...。)來做進一步的植物生長分析將可大大的改善溫室種植的效益及維護成本。本研究針對中小型溫室設計開發出一套方便操控的低成本溫室自動化監控系統並且對數據以及植物的紅外線照片進行初步分析。本研究針對中小型溫室設計開發出一套方便操控的低成本溫室自動化監控系統並將收集到的數據透過日誌檔的方式將重要資訊做儲存,並收集紅外線照片的每一像素透過Normalized Difference Vegetation Index (NDVI)運算計算出一筆數值來判斷是否為光合作用旺盛的區域;並且將收集到的數據使用數據分析軟體(Splunk)進行初步的分析。此一系統朝向解決-初步的資料分析以及自動控制。

關鍵字

NDVI Splunk 數據 分析 溫室自動化 即時監控

並列摘要


Greenhouse agriculture has the advantage of protecting the plants from outside harsh conditions and providing suitable conditions for plant growth; it can effectively improve the crop yield and quality. But the traditional monitoring/control system of greenhouse construction costs a lot and the traditional control interface is not friendly (some are just manual setting); it is, therefore, not very cost-effective, friendly and high-productive. With the advent of the cloud computing and low-cost Internet-of-Things (IoT) systems, we can apply these low-cost and effective technologies to monitor environment conditions/plant growth and control the facilities. In addition to conveniently monitor/control greenhouse facilities, a real-time platform to dynamically analyzing the collected data can greatly improve the efficiency of greenhouse cultivation, maintenance costs and decision making. In this study, a low-cost greenhouse monitoring system is developed for small-sized and medium-sized greenhouse installations with real-time data analysis. With RethinkDB, raspyberry pi, tornado, image analysis,NDVI and Splunk, we develop an efficient-and-effective greenhouse system to achieve the above goals.

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