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Nanodrone For IoT Environmental Data Collection

A "Nanodrone" for environmental data collection and a Ground Control PSoC6 to interface the data to the cloud.

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The project idea is based on the evolution of the "Nanodrone" project I recently published on the Element14 Community.
I already own an AWS account (IoT Console, SiteWise web, EC2 server. and some virtual storage); I want to set the drone module to detect the color(s) the pointed subject – a vegetable, fruit, plant, etc. ) from different distances with the onboard Arduino Nano Sense, collect ripening data of the plant or cultivation and send them with GPS information to the PSoC6.
Data are integrated by the PSoC6 and sent via a mobile WiFi to the AWS IoT cloud. For this project, I plan to use the PSoC6 WiFi BT Kit with the TFT screen and my AWS IoT environment, as well as the AWS EC2 server I own by some years.

Introduction

The project idea is making a relatively low-cost system to monitor plants and cultivation areas to detect the grown state as well as potential parasite and damaged plants.

The device can be easily installed on a semi-autonomous UAV to cover large areas of terrain, as well as installed on a ground robotised device. The use of a UAV represents the most flexible solution compared to the ground mobile unit. 

Some of the most challenging issues if adopting the ground mobile solution:

  1. Difficulty to move on the non-regular terrain
  2. Limitations due to the kind of plants
  3. Slower and more difficult mobility
  4. Reduced field of operation
  5. Slower back-to-home maneuvres

The video below shows the first proof of concept of the nanodrone, developed with an Arduino Nano 33 implementing visual recognition with Tensorflow Lite installed on a DJI Mavic Mini drone.

This project is co-sponsored by Elegoo for the 3D printers and printing material and Digitspace for the sensors and actuators.

Drafting the Project

The image above shows the draft of the project through a typical workflow:

  • A drone with onboard the collection/inspection device will move along a field following a predefined path.
  • The collected information – including visual data – coming from several sensors are integrated realtime and saved locally on a microSD card.
  • The device has a GPS to save the acquisition points, independent by the drone navigation system
  • Every return-to-home cycle of the drone data the information are updated via BLE to the PSoC6 Pioneer Kit (a small mobile station to the ground) that collect every inspection fly session (minimum one).
  • At the end of the series of inspections of the field (may need more fly cycles, depending on the extension) the whole inspection set of retrieved information are sent to the AWS IoT Console via MQTT, certificates and several shadows to monitor several inspections acquired along the time.
  • The full data retained by the PSoC6 Pioneer Kit station are sent (when the boards are on the same network) via WiFi to a Raspberry Pi that can process more detailed information.

Real-World Project Applications

Accordingly to the kind of data it is possible to acquire and the position repatability of the sampling there are at least three  main areas of application of this project, that in my opinion can offer the opportunity to grow the prototype to a product level:

  • Plants and cultivated trees inspection for small and medium-size farming
  • Architectural structures variation on time and deformation analysis.
  • Environmental impact changes

Agriculture inspection

Pest, parasites, growing stage of fruits and grasps, and more can take advantage from this kind of local, medium-range inspection where – in a similar way – satellite specific-range visual information are acquired for large terrain areas, wildlife zones etc.

Integrating the visual inspection information together with the environmental conditions (temperature, humidity, etc.), weather conditions, and time-of-day it is possible to track growing curves of the evolution of some phenomenons curves that for some reason are impacting the productivity level of the cultivations.

Sensors data collected can be integrated locally (on the ground Raspberry Pi machine) with drone photos acquired in the same position to provide more specific and detailed information, as well as a visual history of the acquisition. The core information collected and pre-processed by the PSoC6 Pioneer Kit ground unit instead, are sent to the AWS IoT Console for changes over time analysis.

Above: the PSoC6 Pioneer Kit box case designed with Fusion360 and 3D printed with the Elegoo LCD Saturn 3D printer.

Structural Variations and Environmental Impact Changes

The possibility to precisely repeat along a timeline (maybe daily lot less frequent, depending on the kind of inspection) gives the nanodrone project the possibility to acquire comparable series of data during periods. Based on this...

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web-IMG_8672.JPG

The drone board of the first experiment was based on Arduino Nano 33 implementing a small camera and a recognition system based on Tensorflow Lite.

JPEG Image - 365.92 kB - 06/03/2020 at 21:00

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web-IMG_8637.JPG

The first prototype of the Nanodrone, demonstrating the proof of concept has been made on a DJI Mavic Mini (with a payload of about 100 gr).

JPEG Image - 340.33 kB - 06/03/2020 at 20:57

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  • 1 × PSoC6 WiFi BT Pioneer Kit (CY8CKIT'062'WiFi'BT) It is the main component to make the outdoor ground unit collecting the inspection data retrieved by the inspection drone operating nearby in the field
  • 1 × PSoC6 WiFi-BT Prototyping Kit )CY8CPROTO-062-4343W PSoC6) This is the main components of the device onboard of the inspection drone
  • 1 × DJI Mavic 2 Pro This drone has a payload of about 1 Kg more than sufficient to support without difficulty the inspection module
  • 1 × iPad Air 2 With the proper application is used to control the drone, in particular to set predefined fly paths that are reproducible more than once.
  • 1 × AWS IoT Service Manages and present the data collections to see the evolution of the inspected areas along the time.

  • Experiment #1: Targets Along a Path

    Enrico Miglino06/09/2020 at 23:37 0 comments

    The video below shows the first experiment of creating a path that can be repeated by the drone with a series of targets, ideally representing the POI. The drone take a shoot every target point while the PSoC6 should collect data that will be integrated with the image analysis.

  • PSoC6 WiFi BT Pioneer Kit 3D Case Design

    Enrico Miglino06/04/2020 at 18:35 0 comments

    Rendering of the images of the case, designed with Fusion360 and 3D printed with Elegoo Saturn 4K LCD 3D printer

  • Fields of Application

    Enrico Miglino06/03/2020 at 20:46 0 comments

    Added the field of application (agricultural small and medium areas) as the environment where the first experiments of the prototype will be conducted. Added other kind of application that only need software customization.

  • Project Evolution

    Enrico Miglino06/02/2020 at 12:14 0 comments

    Stage 1 - Project Details

    In this first phase I define the project characteristics and features, components and the development workflow

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