在農業方面，農民的經驗非常寶貴，但卻難以取代和傳承。對許多農業國家來說，缺乏大量的人力資源也是一個嚴重的問題，也因為人力資源的考量，導致農民的事業規模無法擴大或增加產值。其中對於有機作物而言，灌溉是最關鍵的步驟之一，但也是一項勞動相對密集的工作。如何有效利用自動控制系統來解決人力不足的問題，是這個系統最主要的目的。
本論文提供了一種基於機器學習的智能灌溉系統，該系統使用LoRa P2P模式來建構LoRa網路，此系統能夠自動學習農業專家和農民對溫室有機農作物的灌溉經驗。首先系統會根據訓練後的灌溉模型結合環境數據（如氣溫/濕度，土壤溫度/濕度，光照強度等）計算每次農作物需要灌溉的水量，然後利用無線長距離低功耗的LoRa P2P網路自動灌溉作物進行灌溉。在LoRaWAN 網路內使用的是非同步傳輸 (asynchronous communication), 是使用 random access 常見的 ALOHA method，並不適合即時的自動控制。我們使用LoRa P2P網路來實現自動灌溉系統，這是一種主從式的關係也是基於TDM (Time-Division Multiplexing)的MAC協議。我們的實驗結果顯示，我們所提出的智慧灌溉系統非常適合現代農業，不僅能優化農民澆水的方式也能更大幅度的縮減人力的需求。我們也提供了手機應用程式來觀看相關的環境數據跟操作此控制系統。

In agriculture, the experience of farmers is very valuable but difficult to replace and pass on. The lack of working power is also a serious problem for many agriculture countries. For organic crops, the irrigation is one of the most critical steps but also a very labor intensive work. How to effectively use the automatic control system to solve the problem of insufficient manpower is the most important purpose of this system.
This thesis provides a machine learning-based smart irrigation system with LoRa P2P networks to automatically learn the irrigation experiences from expert farmers for greenhouse organic crops. The proposed system will firstly calculate the amount of water for each irrigation based on the trained irrigation model combined with the environment data, such as air temperature/humidity, soil temperate/humidity, light intensity, etc., and then irrigate the crops automatically via the long-distance and low-power wireless LoRa P2P networks. The MAC protocol of LoRaWAN is Aloha based (random access) and may not be suitable for real-time automatically control. We implement the automatic irrigation system with LoRa P2P network which is a master-slave and TDM-based MAC protocol. Experimental results show that the proposed smart irrigation system is very suitable for modern agriculture. Not only can optimize the way farmers water their plants, but also can reduce the need for manpower. We also provide a mobile application to view relevant environmental data and operate this control system.

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