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| Title: | 農業物聯網數據異常檢測的監督式學習演算法
A Supervised Learning Algorithm for Anomaly Detection in Agricultural Internet of Things Data |
| Authors: | 王飛鴻
Wang, Fei-Hong |
| Contributors: | 蔡瑞煌
周珮婷
Tsaih, Rua-Huan
Chou, Pei-Ting
王飛鴻
Wang, Fei-Hong |
| Keywords: | 農業物聯網
監督式學習演算法
數據異常檢測
Agricultural IoT
Supervised learning algorithm
Data anomaly detection |
| Date: | 2023 |
| Issue Date: | 2023-08-16 13:36:29 (UTC+8) |
| Abstract: | 隨著物聯網、雲計算等資訊技術的發展普及，農業資訊化從數位農業、精準農業進入到了一個新的階段——智慧農業。智慧農業是以物聯網技術為主要支撐和手段的現代農業形態，它在受益於物聯網技術進步的同時，也不可避免的受制於農業物聯網的缺陷，尤其是土壤監測器、微氣象站、智能電網系統等物聯網感測器出現的異常情況。為了讓農業生產者大量購買使用，農業物聯網感測器往往在產品設計，用料，功能等方面盡可能降低成本，從而導致其品質和穩定性的降低，再加上長時間暴露在自然環境下，更容易發生異常甚至損壞……因此，物聯網感測器在日常運作過程中，難免會出現電池電量低，校準不當、設備老化等異常情況。而這種異常又往往會導致監測資料的異常，讓農業生產者做出錯誤的判斷，進行錯誤的決策，從而導致嚴重的後果。因此，我們迫切需要找到一種方法，能夠幫助農業生產者自動檢測農業物聯網的數據異常，並加以精准的分析，從而引導生產者做出科學的判斷。
在本研究中，我們提出了一種基於機器學習的監督式學習演算法,即 SH 學習演算法(Soil humidity learning algorithm, SHLA)來實現農業物聯網的數據異常檢測。
With the development of information technologies such as the Internet of Things(IoT), agricultural informatization has become smart agriculture. Different from past, smart agriculture is highly dependent on IoT sensors, such as soil monitors, microweather stations, smart grid systems, etc. However, while smart agriculture enjoys the benefits brought by the progress of IoT, it is also limited to the shortcomings of it. Under normal circumstances, it is inevitable for IoT sensors to encounter abnormal conditions such as low battery power, improper adjustment, or aging equipment. Especially agricultural IoT sensors’ product design, materials, functions, etc. have been weakened to reduce costs to help agricultural producers purchase or replace in large quantities, which means the stability of agricultural IoT sensors is greatly reduced. In addition, since agricultural IoT sensors are exposed to the natural environment, sensors are more prone to abnormalities or even get broken.
For agricultural producers, unstable agricultural IoT sensors with anomaly data, may make them make wrong decisions, resulting in serious consequences such as reduced crop yields even crop damage. Therefore, smart agriculture urgently needs a method that can automatically detect data anomalies in the agricultural IoT.
In this experiment, we propose a supervised learning algorithm named SHLA (Soil humidity learning algorithm) based on machine learning to detect the data anomalies of the agricultural IoT. |
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| Description: | 碩士
國立政治大學
資訊管理學系
110356050 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110356050 |
| Data Type: | thesis |
| Appears in Collections: | [Department of MIS] Theses
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