CloverDroid : A Smart Urban Farm

Hi, i made some changes in the last post. After a deep consideration, i plan to switch from NFT to Ebb and Flow system. The farm will be indoor without a grow tent. I live in tropical climate, so a grow tent or climate controlled room might be unnecessary. BUT, i managed to finish my system (70% done)... it might be ugly, but that's what i manage to do in between my classes and labs.

I going to post the setup later.

The application server and data visualization will be handled by Blynk and Metabase. The method to install both server are described in forum. 

Specification:

Device : Raspberry Pi 3B+

OS : Raspbian

Blynk Server on port 8080 and 9443.

PostgresSQL on port 5432.

Metabase on port 3000.

Blynk UI pending...

Metabase UI pending...

One of the sensor node for the smart farm is the environment sensor located on the top of the farm. In outdoor farm, environment parameter like humidity, temperature and light intensity is crucial since it can't be controlled like an indoor farm. There few existing environment sensor that available for DIY projects and here a few of them.

Temperature and Humidity

Few common temperature and humidity sensor like DHT11, DHT22/AM2302, BME280, BME180, and SHT31. DHT11 and DHT22 sensor is extremely cheap while BME and SHT sensor are more expensive. 

Some article provide a good comparison for these sensor like:

1. Adding Sensirion SHT31 to the range of test hygrometers.

2. DHT11 vs DHT22 vs LM35 vs DS18B20 vs BME280 vs BMP180

DHTs sensor is pretty good for common application that does not require precise sensor reading. While the error is not catastrophically large, the device does not support i2c communication protocol that I planned to use in this project. Moreover, DHTs sensor accuracy degrade quicker overtime when exposed to elements.

Light(Lux, PPFD, PAR, DLI)

The light sensor measure the intensity of light. In common climate sensor, the light is not a necessary parameter to measure. However, in farming light intensity provide the information that may able to help determine plant growth rate. Different from usual light meter that measure luminosity in lux for candela, light meter used to measure plant growth measure the PPFD or PAR. I won't be explaining the difference in PPFD or PAR compared to Lux as there are load of article explaining them already like this, this, and this.

The issue with different measurement unit is that Lux and PAR measure different object response to light. Lux measure "human eye" light response, while PAR measure "plant" light response. While most article explain that it's NOT POSSIBLE to measure PAR with luminosity sensor like TCS3425, BH1750, MAX44009, or VEML7700,  that's true if the sensor is used to compare different light source like sunlight to LED light or some other grow light. In this case, the light source is the sunlight during the day thus it's POSSIBLE to use Lux or lumens to calculate PAR or PPFD. And thanks to Apogee Instrument to provide a comparison chart that remove unnecessary expense to buy an PAR or PPFD meter to convert my sensor reading.

The last question now is which sensor to use. TCS3425 is a RGBC light sensor, it use 4 different sensor with color filter to measure different light spectrum. MAX44009 and VEML7700 is a lux sensor with no filter. While an integrated "Lux sensor" is good enough, TCS34xx RGBC light sensor provide a larger light spectrum bandwidth which able to provide more accurate reading of "Lux for plants". Moreover, there is this research article that create a PAR meter with TCS34715-FN sensor (the same sensor family with TCS34725).

Here is the plan. I plan to turn this

into this

And here is the design block diagram.

There will be 3 sensor node and a nutrient control system. All of them is developed with ESP8266 board (either WeMos D1 mini or NodeMCU). I plan to use Blynk IoT Platform for easy and solid mobile app UI. Futhermore, with "Metabase" as website data visualization platform.

The system can be divided into 3 parts:

1. Sensor Network and data acquisition.

2. Nutrient Control System with Blynk Bridge to fetch data from pH and EC sensor.

3. Metabase Website Data Visualtization.