Research 研究
AIoT Smart Monitoring System for Integrated Pest and Disease Management
Our research has yielded a smart, real-time insect pest monitoring system. It keeps tabs on environmental factors like temperature, humidity, light intensity, CO2, and TVOC levels, alongside capturing images of sticky traps via a Raspberry Pi camera module. Employing deep learning for automated insect pest identification, it delivers prompt recognition results about insect pest and micro-climate information for farmer for integrated pest and disease management.
本研究發展即時與自動化的智慧蟲害監測系統。它能夠監測環境因素，如溫度、濕度、光強度、CO2和TVOC等，並通過樹莓派攝影機模組捕獲黏蟲紙影像。此系統利用深度學習進行自動化害蟲辨識與計數，快速為農民提供有關蟲害和微氣候資訊，以利農民進行作物栽培之整合性病蟲害管理。
Intelligent Dairy Cattle Health Monitoring System
Intelligent Dairy Cattle Health Monitoring System
We’ve introduced a comprehensive ‘Dairy Cow Health Monitoring System’ consisting of automated body temperature and feeding monitoring systems. The former records each cow’s daily temperature, while the latter tracks the duration of their feedings. These systems enable farm staff to effortlessly oversee the health of dairy cows, propelling dairy farming towards greater automation and intelligence.
我們開發了一套乳牛健康監測系統，該系統由「自動化乳牛體溫監測系統」及「自動化乳牛飲食監測系統組成」。體溫監測系統可以記錄每天每隻牛的體溫數據，而飲食監測系統可以記錄每天每隻牛的飲食時長。藉由這套系統，牧場工作人員可以用更輕鬆的方法監測乳牛的健康狀況。
Intelligent Beehive Health Monitoring System Based on Multi-Sensor Fusion
Intelligent Beehive Health Monitoring System Based on Multi-Sensor Fusion
Our intelligent beehive health monitoring system, equipped with an array of sensors, measures parameters like temperature, humidity, weight, sound, and bee traffic. It provides beekeepers with real-time data, offering actionable insights and early warnings using machine learning and multi-sensor fusion technology. This system empowers beekeepers to fine-tune hive conditions, safeguarding bee colony health and enhancing agricultural sustainability.
此研究開發一套完整的智慧蜂箱健康偵測系統，可以即時收集蜂箱內溫度、濕度、重量、音訊及進出量資訊以即時提供給蜂農了解蜂群的健康情態。透過感測器融合技術與預警系統的回報可以讓蜂農及時優化蜂箱環境以確保蜂群健康，不僅降低蜜蜂死亡率也可以提升花粉授粉率。
Visual-Based Autonomous UAV in Greenhouse
Our research concentrates on the deployment of autonomous UAVs within GPS-limited greenhouses, with a focus on precision monitoring, labor efficiency, and scalability. We’re developing vision-based UAVs to facilitate tasks like crop phenotyping and localization, specifically tailored for greenhouse flowers, muskmelons, and other fruits. Our goal is to integrate these UAVs into smart agriculture systems for efficient and automated crop management.
本研究著重於在溫室中開發基於視覺之自主導航無人機(UAV)，以協助執行如作物表型分析和定位等任務，並將其應用於溫室花卉、甜瓜以及其他水果。我們的目標為將自主導航無人機與智慧農業整合，以有效進行自動化作物栽培管理。