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Arduino Edge Control

SKU AKX00034 Barcode 7630049203044 Show more
Original price €0
Original price €249,94 - Original price €249,94
Original price
Current price €249,94
€249,94 - €249,94
Current price €249,94
VAT included

A remote monitoring and control solution, optimized for outdoor environments.
Collect real-time data from smart sensors and leverage AI on the edge.

Overview

It can be positioned anywhere and is suitable for precision farming, smart agriculture, and other applications requiring intelligent control in remote locations. Power can be either supplied via solar panel or DC input.

Remotely control your application through the Arduino Cloud (or third-party services) using a choice of connectivity options suitable to the location. The Arduino Edge Control features built-in Bluetooth® and its connectivity can be expanded with 2G/3G/CatM1/NB-IoT modems, LoRa®, Sigfox, and WiFi by adding anyone of the MKR boards.

The Arduino Edge Control is capable of connecting sensors and drive actuators like latching valves (common in agriculture). Moreover, it has the capability to provide real-time monitoring over the entire process, thereby reducing production-related risks.

Particularly suited to smart agriculture, the sensors can collect real-time data such as weather conditions, soil quality, crop growth, amongst others. Once sent to the Arduino Cloud, the data value chain becomes valuable analytics that supports business processes at various levels (e.g. crop yield, equipment efficiency, staff performance, etc.). The Arduino Edge Control has the capability to improve crop quality and reduce human effort/error by automating processes like irrigation, fertilization, or pest control.


Application Examples
  • Automated Greenhouses

Automatically manage the humidity and temperature to ensure the best environment for crop growth, minimising carbon emissions and increasing economic yield. The inclusion of an Arduino MKR GPS Shield allows for optimum crop rotation planning and acquisition of geospatial data.

  • Hydroponics/Aquaponics

Since hydroponics involves the growth of plants without soil, delicate care must be taken to maintain the conditions required for optimum growth. The Arduino Edge Control can be set-up to control these conditions with minimal manual labour.
The Arduino Edge Control can help match the even higher requirements of Aquaponics, by providing automated control over the internal process and reducing production risks.

  • Mushroom Cultivation

Mushrooms are notorious for requiring the perfect temperature and humidity conditions to sustain spore growth, while also preventing competing fungi from growing. Thanks to the numerous watermark sensors, output ports and connectivity options available on the Arduino Edge Control, this precision farming can be achieved on an unprecedented level.


Tech specs

Microcontroller nRF52840 (64 MHz Arm® Cortex-M4F)
Digital Input 6x edge sensitive wake up pins
Digital Output 8x latching relay command outputs with drivers
8x latching relay command outputs without drivers
Relays 4x 60V/2.5A galvanically isolated solid state relays
Analog Input 4x 4-20mA inputs
8x 0-5V analog inputs
16x hydrostatic watermark sensor input
Terminal Block Connectors 6x 18 pin plug in terminal block connectors
Power Supply 12 V Acid/lead SLA Battery Supply
(Recharged via solar panels)
Power Consumption Low power (up to 34 months on a 12V/5Ah battery)
200uA Sleep current
Memory 1 MB onboard Flash memory
2 MB onboard QSPI Flash memory
SD Card Interface for SD Card connector (through expansion port only)
Connectivity

Bluetooth
Wifi*
3G*NB-IoT*
LoRaWAN®*

* Requires Arduino MKR board
Peripherals Full-speed 12 Mbps USB
Arm CryptoCell CC310 security subsystem
QSPI/SPI/TWI/I²S/PDM/QDEC
High speed 32 MHz SPI
Quad SPI interface 32 MHz
12-bit 200 ksps ADC
128 bit AES/ECB/CCM/AAR co-processor
Operational Temperature -40° C to +85° C (-40° F to 185°F)
Length 104 mm
Width 86 mm

Conformities

The following Declarations of Conformities have been granted for this board:
RCM
RoHS
CE
FCC
UKCA
REACH
WEEE
For any further information about our certifications please visit docs.arduino.cc/certifications

Resources for Safety and Products

Manufacturer Information

The production information includes the address and related details of the product manufacturer.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/ 

Responsible Person in the EU

An EU-based economic operator who ensures the product's compliance with the required regulations.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc

 

Documentation

Study how the Arduino Edge Controls works using following files:

SCHEMATICS IN .PDF DATASHEET IN .PDF

 

Pinout Diagram

Download the full pinout diagram as PDF here.

Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

read more
BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

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