本研究設計了一台結合影像辨識玉米生長狀態以及自動灌溉應用的自主移動機器人。該機器人的前輪部分採用平行四連桿機構，並搭配電動窗馬達進行轉向控制，後輪則透過齒輪和鏈條的驅動方式實現前後控制。
本研究的控制中樞為機器人頂部的 Intel NUC 小型電腦，並在系統 Ubuntu 18.04 的 ROS 平台上運作，透過 ROS 中的分析影像中心，可以判斷機器人中心的偏移角度，再搭配 Teensy 4.0 微型處理器（MCU），對前輪進行左右角度修正，使得機器人能夠在田間道路中順利移動。
為了實現更精確的灌溉，田間道路的兩側各設置了四個感測盒，共計八個用於收集土壤資訊。每個感測盒每分鐘負責接收並換算校正三組環境資料訊號，這些資料被整合成字串，並通過 HTTP 協定封裝，使用 ESP-8266 模組上傳至雲端資料庫。控制中樞的電腦可以每 30 秒從資料庫中獲取當前土壤濕度值，並搭配機器人透過影像辨識獲取當前區域的玉米生長狀態，判定出各區域的需求水量並換算缺水量，再進行機器人速度控制。如此，便能實現機器人自主灌溉系統之情境。
最後，本研究建立一網站，實現物聯網資訊的透明化。農民可以通過手機或電腦直接觀察田地中作物的生長狀態以及相關的土壤資訊，提供了方便和即時的資訊。
此研究，成功地開發出一個整合影像辨識與自主移動機器人的自動灌溉應用系統，為農業生產提供了一種高效、智能且節省人力及時間資源的解決方案。整體澆灌實驗成功率為 84.8%，實際澆灌濕度總體誤差率 6.55%，未來， 希望能進一步改進系統的性能和功能，以滿足不同地區和作物的需求。

This study designs an autonomous mobile robot that combines image recognition of corn growth status with automatic irrigation application. The front wheels of the robot utilize a parallel four-bar linkage mechanism and are controlled for steering using vehicle window motors, while the rear wheels achieve forward and backward motion control through a gear and chain drive system.
The central control unit for this study is an Intel NUC mini-computer located on
the top of the robot, operating on the ROS platform of Ubuntu 18.04 system. By
analyzing the image center in ROS, the offset angle of the robot's center can be
determined. With the Teensy 4.0 microcontroller unit (MCU), the angles of the front
wheels are adjusted for left and right turns, enabling the robot to navigate smoothly on field roads.
To achieve more precise irrigation, four sensor boxes are installed on each side of the field road, totaling eight boxes for collecting soil information. Each sensor box is responsible for receiving and converting three sets of environmental data signals every minute. These data are integrated into strings and encapsulated using the HTTP protocol, and then uploaded to a cloud database using the ESP-8266 module. The central control computer can retrieve the current soil moisture value from the database every 30 seconds and, combined with the robot's image recognition of the current corn growth status in the area, determine the water demand and calculate the water shortage for each region. This information is used for robot speed control, enabling the realization of an autonomous robot irrigation system.
Finally, a website was created to achieve the transparency of IoT information.
Farmers can directly observe the growth status of crops and related soil information in the field through their mobile phones or computers, providing convenient and real-time information.
This study successfully developed an automatic irrigation application system that
integrates image recognition with autonomous mobile robots, providing an efficient,
intelligent, and resource-saving solution for agricultural production. The overall
success rate of the irrigation experiment was 84.8%, with an overall error rate of 6.55% in the actual irrigation humidity. In the future, improvements in system performance and functionality are desired to meet the requirements of different regions and crops.

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