臺灣博碩士論文加值系統

近年來，少子化現象對農業產業帶來了不小的挑戰，為了解決農民的負擔並提高農業生產效率，物聯網（Internet of Things，IOT）技術成為了一個重要的解決方向。本研究針對農業領域應用了溫度濕度感測器、土壤溼度感測器、太陽能系統的電流監測以及植物影像辨識技術進行了實驗，在實驗中，我們採用了物聯網的概念，通過利用感測器收集環境數據並將其傳送到樹莓派上。樹莓派作為資料傳輸的中心，將數據傳送到Node-RED平台。在Node-RED平台上，我們實現了多項功能，包括視覺化界面的構建、資料存儲和Line通知等。同時，我們將收集到的數據存儲到資料庫中，以便日後進行查詢和分析，並能夠根據需要隨時解決問題，本文還引入了植物影像辨識技術，通過對植物病蟲害的辨識，能夠及時發現和處理問題。然而，我們也意識到在實際影像辨識方面，在圖片的影像辨識中，在YOLOv5l6中BrownSpot（稻胡麻葉枯病）信心度0.76，Healthy（健康）信心度0.29，Hispa（鐵甲蟲病）信心度0.35，LeafBlast（稻熱病）信心度0.78，YOLOv5x6中BrownSpot（稻胡麻葉枯病）信心度0.55，Healthy（健康）信心度0.35，Hispa（鐵甲蟲病）信心度0.36，LeafBlast（稻熱病）信心度0.48，對於植物病蟲害的信心度仍有待加強。因此，我們提出了未來可能的改進方向，如實地考察、搜尋多方面的植物病蟲害資料庫以增加植物辨識的多樣性。

In recent years, the phenomenon of declining birth rates has posed significant challenges to the agricultural industry. To alleviate the burden on farmers and enhance agricultural productivity, Internet of Things (IoT) technology has emerged as a crucial solution. This research focused on applying temperature and humidity sensors, soil moisture sensors, current monitoring of solar energy systems, and plant image recognition technology in the agricultural field. In the experiments, we adopted the concept of IoT by using sensors to collect environmental data and transmit it to Raspberry Pi. The Raspberry Pi served as the central data transmission hub, sending the data to the Node-RED platform. On the Node-RED platform, we implemented various functionalities, including building a visual interface, data storage, and Line notification. Simultaneously, the collected data were stored in a database for future querying and analysis, enabling us to address issues promptly as needed. This study also introduced plant image recognition technology, allowing for the timely detection and handling of plant pests and diseases. However, we acknowledged the challenges in actual image recognition. In YOLOv5l6, the confidence scores for BrownSpot (rice blast), Healthy, Hispa (beetle infestation), and LeafBlast (rice blast) were 0.76, 0.29, 0.35, and 0.78, respectively. In YOLOv5x6, the corresponding scores were 0.55, 0.35, 0.36, and 0.48. There is still room for improvement in the confidence levels for plant pest and disease recognition. Therefore, we proposed potential future improvements, such as conducting on-site investigations and searching diverse plant pest and disease databases to enhance the diversity of plant recognition.

摘要......i
Abstract......ii
誌謝......iii
目錄......iv
表目錄......vi
圖目錄......vii
第一章 緒論......1
1.1 研究背景......1
1.2 研究動機與目的......2
1.3 論文架構......2
第二章 文獻探討......3
2.1 物聯網......3
2.2智慧農業......4
2.3 Node-Red......6
2.4 MQTT......6
2.5 資料庫......8
2.6太陽能......9
2.7稻米病蟲害資料庫......10
2.8 卷積神經網路......11
2.9 物件偵測......14
第三章 系統規劃與製作......15
3.1系統規劃......15
3.1.1 系統架構......15
3.1.2 溫溼度感測器流程......17
3.1.3 土壤感測器流程......19
3.1.4 ACS712電流感測器流程......21
3.1.5 影像辨識流程......23
3.2模型評估......25
3.3軟硬體介紹......27
3.3 影像辨識實驗環境......37
3.3.1標籤與訓練前置作業......38
第四章 實驗成果......40
4.1 溫溼度感測器實驗......40
4.2 土壤溼度感測器實驗......42
4.3 太陽能系統與ACS712電流感測器實驗......44
4.4 植物病蟲害影像辨識實驗......46
第五章 結論與未來研究......55
參考文獻......56
Extended Abstract......60

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