洋天科技洋天科技
公司簡介訂購方式匯款確認檔案下載 聯絡我們保固說明訂單查詢討論區
電子郵件:

密碼:

忘記密碼
加入會員
  首頁 | 原廠 Arduino® | 特殊服務設計 | 轉接座及轉接板/麵包板 | 開發板/燒錄器/模擬器 | 相容 For Arudino® 週邊及配件 | OKdo系列 | Saleae 系列 | Adafruit 系列 | ArduCam 系列 | Camera 攝像頭 | ROCK 系列 | Debix系列開發板 | Raspberry Pi 樹莓派 | Banana Pi 香蕉派 | BeagleBone 狗骨頭 | M5Stack系列 | Micro:bit (BBC)系列 | NVIDIA Jetson Nano系列 | Pololu 系列 | Pycom 系列 | Seeed 系列 | Sparkfun 系列 | WeMos 系列 | 傳感器 | Cubieboard/CubieTruck系列 | Firefly 系列 | Microduino系列 | Orange Pi 香橙派 | PCB板 | PLC 系列 | Robot 機器人 | UDOO 系列 | RedBearLab 系列 | LattePanda系列 | LittleBits 系列 | Libelium 系列 | Luxonis 相機系列 | PCduino | RobotElectronics 系列 | MageDok 顯示屏 | LCD/LCM/TFT/LVDC | Dimension Engineer 系列 | 通訊模組 | 影音器材(含轉換器) | 線材/連結器/轉換器 | 測量儀器 | 馬達/馬逹控制器/電源模組 | 其他 | 焊接/維修工具 | IC零件 | LED燈-裝飾燈 | 工作站迷你電腦 mini PC | 擴大器 | 雕刻機 | 電池 | 電腦周邊 | 檢定考套件 | 停售商品
  首頁 » 商品目錄 » Seeed 系列 » Grove Kit 套件 » 21103
商品搜尋 進階
 |  購物車內容  |  結帳   
商品分類
  聯發科 LinkIt
  Grove Kit 套件
  Grove 傳感器系列
  Grove 擴展板/轉接板
  ReSpeaker 系列
  RF Explorer系列
  Solar Panel 太陽能板 系列
  Wio系列 Wio Link/Wio Node/Wio LTE
  Bluetooth Bee藍芽 (LoRa)Wireless無線
  Bus Blaster 系列
  Crazyflie系列
  DSO 系列
  FiPy 系列
  flick系列
  GPS GSM GPRS 系列
  LCD OLED 屏
  LED燈
  RFID/WIFI系列
  Xadow 系列
  套餐
  Al Linke
  Dangerous Prototypes
  RePhone系列
  Seeed Studio
  Tessel 系列
  Machine Vision
  Prototyping
  線材
  Robot Kit
  停售/停產
  MicroPython系列
  ODYSSEY - X86 系列
Arduino
Pololu
Seeed
Sparkfun
robot-electronics
dimensionengineering
libelium
adafruit
udoo
redbearlab
Arducam
goembed
Saleae
okdo
服務台
公司簡介
退換貨服務
訂購方式
聯絡我們
匯款確認
[<< 前一頁]  瀏覽相同分類產品 30 / 34  [下一頁 >>]
Grove Smart Agriculture Kit for Raspberry Pi 4(110061284)
NT$3,559
運費NT$50
條碼21103
產品說明0

※本產品原廠代理從國外進口,有些交期較長,下訂前請詢問!

Grove Smart Agriculture Kit for Raspberry Pi 4 - designed for Microsoft FarmBeats for Students

SKU 110061284

This kit consists of multiple Grove sensors measuring soil temperature, soil moisture, sunlight, and air temperature & humidity, etc. Designed for Microsoft FarmBeats for Students, aiming at bringing the fundamentals of AI, machine learning, IoT, and data science into the classroom, the kit comes with FREE curricula and rich educational resources for teachers and students.

PRODUCT DETAILS

 

Features

Easy-to-use and low-cost hardware kit: combines an affordable hardware kit with FREE curriculums and activities for students’ hands-on experience in precision agriculture techniques to food production. 

New tools for STEAM Education learners: students learn about AI, Machine learning, and IoT by building a garden monitoring system. 

Easily use with Raspberry Pi 4: with atmospheric and environmental sensors to understand their soil's health, analyze data, and make decisions.  

Real-time data collection: The student-built IoT devices connect to custom Microsoft Excel workbooks that collect real-time data using Excel’s Data Streamer. 

Building your own Machine Learning models: using Lobe.ai, students apply the technique to predict nutrient deficiencies in their plants and identifying pests in their garden.

Introducing Microsoft responsible AI framework: engaging students with some of the social and ethical challenges raised by this new technology.

 

This Grove Smart Agriculture Kit is a hardware kit that consists of an array of multiple sensors measuring soil temperature, soil moisture, sunlight, and air temperature & humidity, etc., the parameters that are crucial for plant growth. With a relay, you can also configure the hardware kit with other hardware modules, to further extend the function from monitoring to controlling such as turn on/off the switch for irrigation or turn on/off the lighting. 

With the combination of software, hardware kit, and curricula resources, the students get a hands-on and immersive experience in the process of learning, to learn about sensor technology, how the changes of data collected from different sensors affect the growth of the crops; thus they understand soil condition and crop health, etc., and make better decisions with data-driven insights.

        

Connect to Excel Data Streamer

At the same time, this hands-on experience enables students to learn about AI, Machine learning, data science, and the Internet of Things (IoT) by building a garden monitoring system. They assemble a Raspberry Pi equipped with atmospheric and environmental sensors to understand their soil's health, understand the environmental parameters that affect plant growth, analyze the data, and make decisions. The student-built IoT devices connect to custom Excel workbooks that collect real-time data using Excel's Data Streamer. They can see the visualized data and further analyze it, thus they can gain insights and make data-driven decisions for their crops.

 

 

Use Lobe.ai to build Machine Learning Models

With Lobe.ai, students are introduced to building their own Machine Learning models. They build, train, and apply machine learning models to predict nutrient deficiencies in their plants, and identifying pests in their garden. There are activities where students set up an agent and others where they work with a curated big data set. The learning progression enables students to easily see the connections between these modern agriculture tools and the opportunities they afford. 

 

 

 

Plus, this FarmBeats for Students program ends by introducing a responsible AI framework, engaging students with some of the social and ethical challenges raised by this new technology.

 

Part List

Product Name

Quantity

Grove Base Hat for Raspberry Pi with a Fan

1

One Wire Temperature Sensor

1

Grove - Capacitive Soil Moisture Sensor

1

Grove - Sunlight Sensor

1

Grove Temperature & Humidity Sensor

1

Grove - Relay

1

Grove - Dual Button

1

micro SD Card with Card Reader - 32GB

1

USB to TTL Serial Cable

1

Screwdriver

1

 

 

 

問與答

目前沒有任何商品問答!
本商品上架日期:2021-06-18.
評價
購物車 更多
空的...
查詢訂單狀態
 
請輸入您的訂單編號
商品通知狀態 更多
通知Grove Smart Agriculture Kit for Raspberry Pi 4(110061284)
更新時通知我
推薦給朋友
 
推薦這個商品給朋友

聯絡方式:手機:0933807110 或 0968222607
E-mail:i0104@ms13.hinet.net(主要信箱) & i03070309@yahoo.com.tw(次要) & a_te0307@hotmail.com & A9215017@mail.ntust.edu.tw & r94922042@ntu.edu.tw