為解決蘭花溫室內感測器裝置的不足，無法實施溫式蘭花種植之全面性的監控，進而加強病害的防治，因此，本研究於蘭花溫室中建立LoRa物聯網(Internet of Things)系統，並將其所連結的感測器作為終端節點，獲取更多溫室內主要影響蘭花生長的三大要素-溫度、濕度以及光照度之資料，將其發送至閘道器進行存儲備分，並傳至伺服器，建立溫室氣候之資料庫。再運用循環神經網路進行訓練，藉以建立預測模型，用來預測未來一段時間後之溫度、濕度之變化趨勢，以及軟腐病害可能發生之嚴重性，提升蘭花栽培之產能。同時，將預測結果與過去接收資料一併顯示於本研究所建立之使用者互動介面，提供即時監控各項環境資訊，藉由提前預警輔助使用者判斷溫室環境之現況，可維持溫室環境之穩定並減少蘭花種植的損失。本研究所有蒐集的溫度、濕度以及光照度之數據皆取自合作廠商之蘭花培養溫室，確保其數據皆為實際產業中所應用之資料，將來此系統研發成功，可順利投入產線，提升生產品質與效率。

To improve the drawbacks for not able to be effectively monitored, this research establishes a LoRa Internet of Things system in orchids greenhouse, to collect three mainly features affecting the orchids growing, the data of temperature, humidity, and light strength, by the wireless sensor nodes embedded in the orchids greenhouse. The collected environmental data are sent to the gateway for storage, and then transmitted into the server to build a database. The data in the database will be used to train a prediction model by recurrent neural network in order to predict the trends of future temperature, humidity prediction and soft-rot diseases damage. In this research, a user interface is constructed to show the predicted results as well as the environment information in real time for easily and friendly provide process the proposed systems, such as displaying early warning and judging the current environmental situations. Finally, all collected data are obtained from the cooperated orchid growing greenhouse to ensure the future use for the proposed system in real situation.

摘要----------------------------------------------------I
Abstract------------------------------------------------II
致謝----------------------------------------------------III
圖目錄--------------------------------------------------VI
表目錄--------------------------------------------------VIII
第一章 緒論---------------------------------------------1
1.1 前言------------------------------------------------1
1.2 研究動機及目標--------------------------------------1
1.3 文獻回顧--------------------------------------------2
1.3.1 IoT與LoRa簡介-------------------------------------3
1.3.2 物聯網於環境監控、氣候預測與病蟲害防治相關文獻----7
1.3.3 遞迴神經網路相關文獻------------------------------10
1.4 本文架構--------------------------------------------19
第二章 蘭花溫室物聯網系統概述---------------------------20
2.1 硬體設備--------------------------------------------22
2.1.1 感測終端節點與閘道器設備--------------------------22
2.1.2 無線傳輸模組--------------------------------------24
2.2 軟體套件--------------------------------------------25
2.3 感測終端節點與閘道器架設----------------------------26
2.4 使用者介面設計--------------------------------------28
第三章 深度學習模型實現---------------------------------30
3.1 資料收集與前處理------------------------------------30
3.2 溫室微氣候預測及軟腐病發預測------------------------32
3.2.1 建立資料集----------------------------------------33
3.2.2 預測模型建立--------------------------------------35
第四章 深度學習模型實驗結果-----------------------------38
4.1 溫室微氣候預測系統----------------------------------38
4.2 軟腐病發預測系統------------------------------------45
4.3 使用者介面------------------------------------------49
第五章 結論與未來工作-----------------------------------53
5.1 結論------------------------------------------------53
5.2 未來工作--------------------------------------------54
參考文獻------------------------------------------------56

[1] https://agrstat.coa.gov.tw/sdweb/public/trade/TradeCoa.aspx, May, 2021.
[2] M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, "A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming," IEEE Access, vol. 7, pp. 156237-156271, 2019, doi: 10.1109/ACCESS.2019.2949703.
[3] "What is LoRaWAN® Specification." https://lora-alliance.org/about-lorawan/ (accessed 7/26, 2021).
[4] A. Zourmand, A. L. K. Hing, C. W. Hung, and M. Abdulrehman, "Internet of Things (IoT) using LoRa technology," 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 324-330, 2019.
[5] M. Erazo, D. Rivas, M. Pérez, O. Galarza, V. Bautista, M. Huerta, R. José Luis, "Design and implementation of a wireless sensor network for rose greenhouses monitoring," in 2015 6th International Conference on Automation, Robotics and Applications (ICARA), 17-19 Feb. 2015 2015, pp. 256-261, doi: 10.1109/ICARA.2015.7081156.
[6] T. Truong, A. Dinh, and K. Wahid, "An IoT environmental data collection system for fungal detection in crop fields," 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, 2017.
[7] H. Wani and N. Ashtankar, "An appropriate model predicting pest/diseases of crops using machine learning algorithms," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1-4, 2017.
[8] D. Davcev, K. Mitreski, S. Trajkovic, V. Nikolovski, and N. Koteli, "IoT agriculture system based on LoRaWAN," 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), pp. 1-4, 2018.
[9] H. Benyezza, M. Bouhedda, N. Faci, M. Aissani, and S. Rebouh, "Greenhouse Monitoring and Fuzzy Control System based on WSAN and IoT," in 2019 International Conference on Applied Automation and Industrial Diagnostics (ICAAID), 25-27 Sept. 2019 2019, vol. 1, pp. 1-5, doi: 10.1109/ICAAID.2019.8934984.
[10] A. Ali and H. S. Hassanein, "Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments," in 2019 International Conference on Smart Applications, Communications and Networking (SmartNets), 17-19 Dec. 2019 2019, pp. 1-5, doi: 10.1109/SmartNets48225.2019.9069766.
[11] A. Khattab, S. E. D. Habib, H. Ismail, S. Zayan, Y. Fahmy, and M. M. Khairy, "An IoT-based cognitive monitoring system for early plant disease forecast," Computers and Electronics in Agriculture, vol. 166, p. 105028, 2019/11/01/ 2019, doi: https://doi.org/10.1016/j.compag.2019.105028.
[12] D.-H. Jung, H. S. Kim, C. Jhin, H.-J. Kim, and S. H. Park, "Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse," Computers and Electronics in Agriculture, vol. 173, p. 105402, 2020/06/01/ 2020, doi: https://doi.org/10.1016/j.compag.2020.105402.
[13] D. H. Hubel and T. N. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex," (in eng), J Physiol, vol. 160, no. 1, pp. 106-154, 1962, doi: 10.1113/jphysiol.1962.sp006837.
[14] F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, vol. 65, no. 6, pp. 386-408, 1958, doi: 10.1037/h0042519.
[15] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 6088, pp. 533-536, 1986/10/01 1986, doi: 10.1038/323533a0.
[16] J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proceedings of the National Academy of Sciences, vol. 79, no. 8, p. 2554, 1982, doi: 10.1073/pnas.79.8.2554.
[17] M. I. Jordan, "Chapter 25 - Serial Order: A Parallel Distributed Processing Approach," in Advances in Psychology, vol. 121, J. W. Donahoe and V. Packard Dorsel Eds.: North-Holland, 1997, pp. 471-495.
[18] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[19] I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Advances in neural information processing systems, 2014, pp. 3104-3112.
[20] K. Cho, B. Van Merriënboer, D. Bahdanau, and Y. Bengio, "On the properties of neural machine translation: Encoder-decoder approaches," arXiv preprint arXiv:1409.1259, 2014.
[21] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.
[22] D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," arXiv preprint arXiv:1409.0473, 2014.
[23] M.-T. Luong, H. Pham, and C. D. Manning, "Effective approaches to attention-based neural machine translation," arXiv preprint arXiv:1508.04025, 2015.
[24] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio, "Show, attend and tell: Neural image caption generation with visual attention," in International conference on machine learning, 2015, pp. 2048-2057.
[25] Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, and G. Cottrell, "A dual-stage attention-based recurrent neural network for time series prediction," arXiv preprint arXiv:1704.02971, 2017.
[26] https://web.tari.gov.tw/techcd/花卉/蘭花/國蘭/病害/國蘭-軟腐病.htm, August, 2017.