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Cycle 1 Report: Volume Optimization for Food Product During

Deep Space Exploration

Tyler Ballard Harold Brooks

Holden Covington Michael Foley Steven Reece Tyler Robins

Jack Satterfield

28 October 2019

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Executive Summary The project goal is to create an autonomous space farm with members from the

mechanical engineering and agriculture departments to be operated on a spaceship in transit to a

distant destination. Our goal as electrical engineers is to create a functioning robotic arm to move

the plants when they reach full growth, use microcontrollers to power mechanical cooling and

watering systems, implement a multispectral camera to monitor plant growth, and configure

power management aspects for controlling the microcontrollers.

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Table of Contents

I. Cover Page……………………………………………………………. 1

II. Executive Summary (Steven Reece)..………………………………... 2

III. Table of Contents……………………………………………………... 3

IV. Introduction (Holden Covington)..………………………………….. 4-5

V. Main Body……………………………………………………………6-17

a. Robotic Arm (Jack Satterfield)…...……………………………… 6-7

b. Communication (Michael Foley)………………………………... 8-10

c. Arduino (Harold Brooks)………………………………………...10-11

d. Multispectral Camera (Tyler Robins)...………………………….12-14

e. Power Management (Tyler Ballard)..…………………………….. 14

f. Budget (Steven Reece)..………………………………………….. 15

g. Goals for Cycle 2……………..…………………………………... 16

h. Timeline (NASA Organized)…………………………………….16-17

VI. Conclusion (Steven Reece)..…………………………………………..17

VII. References……………………………………………………………..18

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Introduction The Automated Plant Farm was proposed by Auburn professors Dr. Dean, Dr. Beale, and

Dr. Sanz Seaz to NASA who intends to use the product to feed astronauts on a deep space

mission to Mars. The goal of the project is to create a machine that can automatically grow plants

from seeds to mature adults and to maximize the number of plants grown within a cubic meter

size constraint.

There are multiple reasons why the automatic plant grower is needed. First, astronauts

need food in space and for deep space missions it would be beneficial to have fresh food instead

of solely relying on pre-packaged freeze-dried food. Second, payload optimization is crucial for

spaceflight. A single pound costs thousands of dollars to launch into space. The Automated Plant

Farm would be able to lower the payload by taking seeds and water from the spaceship and

turning them into food. Third, this system will be a prototype for future automated plant farms.

While the six-month Mars flight might be able to go without the machine, colonization of Mars

and years long deep space missions will need some way of growing food. The Automated Plant

Farm supplies an endless amount of food as seeds can be collected every harvest to grow more

and more plants.

Our project has numerous challenges ahead. One of the most difficult problems will be

watering the plants. Farming is much harder in space due to microgravity which tends to make

water clump and makes any drainage system for water impossible. For this reason, we are

working to develop a system that monitors the moisture level of the growth medium for each

plant so that plants will only be watered when necessary. Over watering plants can lead to death

as the plant will drown if the roots do not have access to oxygen.

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Another problem is monitoring plant health. Seedlings need to be moved after they reach

a certain height to a larger growing area. The system also needs to detect problems with the

plants such as under or over watering or even salt damage. Since we are using a hydroponic

system that delivers a nutrient solution in addition to water, there is a possibility that the salts in

the nutrient solution can accumulate leading to salt deposits in our growth medium. Salts are

toxic to plants and the only way to fix the problem is water the growth medium to dilute the salt

concentration. Ideally, regular watering would prevent this problem from occurring, but it is still

important to have a system in place to detect if it does. We are developing a multispectral camera

to create Normalized Difference Vegetation Index (NDVI) images that determine the health of

the plant. NDVI works by measuring the ratio of red, green, blue (RGB) and Near Infrared Light

(NIR) reflected from the plant. This technique can detect plant stress within fifteen minutes

while the plant may appear healthy to the human eye.

Since plants will need to be moved throughout the growth chamber, we will need to

create a robotic arm with claw attached to accomplish this. The multispectral camera will also be

mounted to the robotic arm to take pictures of individual plants. We decided to model our robotic

arm after a 3D printer. This will allow for greater mobility between levels and reduce the amount

of space the arm will occupy.

The Automated Plant Farm will require computers to process images from the

multispectral camera, to control the robotic arm, and to regulate the environment. We will be

using a combination of a Raspberry Pi and three Arduino Dues: one for the robotic arm, one for

temperature regulation, and one for watering plants. The Raspberry Pi will handle the

multispectral camera and communication between the three Arduinos.

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Robotic Arm Within the growth chamber the primary responsibility of the robotic arm will be the

movement of plants from the incubation chamber to the full-size chamber at the seedlings point

of maturity. The arm will also be responsible for the disposal of dead plants and will have the

camera system attached to it to allow full range of sight for NDVI detection. To complete these

tasks three claw designs are currently being evaluated a 2-pronged claw, an enclosed claw, and a

4-pronged claw which can all be seen below in Figure 1. The 2-pronged claw would provide

more mobility with its compact design while the enclosed and 4-pronged claws would offer more

stability while transporting larger plants.

Figure 1: Robotic Arm Design Concepts

Control for the robotic arm will be handled by an Arduino configured as a motor driver

operating three motors for X, Y, and Z movement. The Arduino will receive coordinates through

I2C from the Raspberry Pi master based on what operation needs to be completed. After receiving

the coordinated the Arduino will adjust the motors into the appropriate locations and carry out the

desired task whether it be plant relocation or disposal. The flow diagram for the process can be

seen below in Figure 2.

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Figure 2: Robotic Arm Operation Flow Diagram

After the Arduino has received the X, Y, Z location and instruction from the Raspberry Pi

it will follow the flow chart in Figure 3. The first step is for the Arduino to take its current

location and calculate the necessary movements to get to the new position. After the robotic arm

is at the correct position it will execute either a pick-up or drop-off command. Once this is

completed the Arduino will check to make sure that the command was completed successfully

and if it was not it will recalibrate the arm and try again. Successful completion of a drop off

command will put the Arduino in an idle state until a new command is issued, a pick-up

command will cause the Arduino to wait for new X, Y, Z coordinates from the Pi so that it can

complete a drop off at a different position.

Figure 3: Arduino Control Loop

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Communication

During Cycle 1 we had to determine the communication protocols and hierarchy to use

for data transfer between the Raspberry Pi, Arduinos, and sensors. We chose to have a master-

slave relationship between the Raspberry Pi/Arduinos and the Arduino/Sensors, allowing us to

clearly separate responsibilities of the Arduinos and Raspberry Pi as well as preventing

congestion of the communication lines going to the Raspberry Pi. Our preference for

communication protocols is SPI between the Arduinos and Raspberry Pi and I2C between the

Arduinos and Sensors as seen in Figure 4, however, some Arduinos may require different

connections depending on the requirements of the chosen sensors.

Figure 4: Communication Diagram

We considered multiple options for communicating between the master Raspberry Pi and

slave Arduinos, including serial, I2C, and SPI, however serial was ruled out due to lack of data

clock and general unreliability of raw serial communication. I2C and SPI are both serial

communication protocols with clocked data transmissions making them reasonable options,

however some models of the Arduino are limited to a single hardware I2C interface and due to

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the prevalence of I2C sensor interfaces I2C was also ruled out. SPI also has other useful features

such as much higher bandwidth (theoretically infinite) than I2C and a separate slave select line

for each slave device. Due to the lack of any of the above options defining a data format for

messages passed between master and slave, we will have to further design and refine a message

format such as the one shown in Figure 5. Initial testing of communication between a Raspberry

Pi and an Arduino is planned for early cycle two.

Figure 5: Example SPI Message Format

Our decision for the communication between the master Arduinos and the slave sensors

was simpler, as the choice of sensors largely determined the communication protocol. I2C is one

of the most common communication interfaces available on sensors, so we chose to plan on

using I2C for communication between the Arduinos and sensors and if any sensors require

special connections to the Arduino they will be implemented as necessary. Sensors define their

message format for communication so it will not be necessary to define a message format.

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Arduino

The Arduino will run a combination of decision tree logic and PID control loops, as

shown in Figure 6, to keep the environment process values on the setpoint pushed from the

master Raspberry Pi. The sensors will communicate with the Arduino via I2C, SPI, and/or 4-

20mA signals. The Arduino will control the state of the outputs via the relay board or directly

through the GPIO pins.

Figure 6: Arduino Decision Tree Logic and PID Control Loops

The Arduino software will be made up of closed loop PID control systems, as seen in

Figure 7. A couple of control structures will be explored with the mechanical engineers to

determine best design and tuning of parameters. The Raspberry Pi will have an interface to

change each setpoints and will send a command with the new value to the Arduino. The Pi will

be requesting a sample of sensor data as well to keep of log of the readings.

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Figure 7: Closed PID Control Loop

The PID loops will follow an implementation similar to Figure 8. Tuning will need to be

performed for each P, I, and D parameter. There may be a need to implement the loops in a

cascade structure since the process values being monitored may be dependent on the control

devices of the other loops. Mechanical engineering team will be consulted in the implementation

of the loops.

Figure 8: PID Loop Implementation

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Multispectral Camera The multispectral camera will be used to create Normalized Difference Vegetation Index

(NDVI) images to monitor plant health. We initially thought of using a thermal camera, but the

plants will likely not generate enough heat to notice a difference in the environment since most

camera come with a range of ±5º C. We also thought of training a neural network with an RGB

camera, but this would require too much testing and numerous plants killed which we simply do

not have the time or resources to do. Finally, we found NDVI images. These indicate plant health

by creating an image using the formula in Equation 1.

NDVI = (NIR – Red) / (NIR + Red)

Equation 1: Red NDVI Calculation

NDVI is a ratio between red light and Near Infrared (NIR) reflectance from the plant.

Values range from -1 to 0 for dead plants or inanimate objects and from 0 to 1 for unhealthy to

healthy plants. [1] Two cameras are necessary for the image: an RBG camera and a NoIR

camera. The RGB camera captures red, green, and blue light with an infrared (IR) blocker so that

IR light does not appear in the red band. The NoIR camera contains no IR (NoIR) filter so that

IR light can pass through. We will attach a red-light filter as well so that only IR light appears in

the red band.

Another problem is that the Raspberry Pi only has one camera input. Since we need two

cameras, this would require at least two Raspberry Pis to properly create an NDVI image.

Ivmech Mekatronik & İnovasyon Ltd makes a multiplexer for the Raspberry Pi cameras as seen

in Figure 9. This will allow us to use a single Raspberry Pi with two cameras. It requires an

additional wire to select the proper camera. There may be a problem with attaching an extra-long

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CSI cable to connect the camera on the robotic arm to the Raspberry Pi, so we also chose to use

the Arducam CSI to HDMI extension cable in Figure 10 to convert the CSI cable to an HDMI

which will be sturdier and easier to move throughout the Automated Plant Farm. Figure 11

shows how the cameras will be connected to the Raspberry Pi.

Figure 9: Raspberry Pi V2 Camera

Multiplexer

Figure 10: Raspberry Pi V2 CSI to HDMI

Extension Cable

Figure 11: Camera Layout Diagram

A second option to the multiplexer would be to use blue NDVI, also known as single

image (SI-NDVI), instead of red NDVI. [2] This allows an NDVI image to be created from a

single NoIR camera with a red-light blocker by using the blue light in lieu of the red light in

Equation 1. However, there is little research on blue NDVI and whether it is as reliable. Since the

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blue NDVI uses the same hardware than red NDVI minus one camera, we can record the blue

NDVI images and how the system would have reacted differently. This data could be useful in

developing future Automated Plant Farms in determining whether red NDVI is worth the extra

cost.

Power Management Power management is essential to controlling multiple high-speed microcontrollers. The

more power saved returns resources that can be utilized on other devices. [3] Clock frequency or

operating speed of the microcontroller requires most of the supplied power. If the

microcontrollers can function on lower frequencies, then power will be retained and effectively

managed. The issue being 6-7 sensors used in plant surveillance will require a lot of power to

collect data. With interrupt service routines we can manipulate the sensors to check run on a

scheduled timer, leaving the microcontrollers in idle for most of the time. The only time a plant

would require higher maintenance is in transition from the seedling to the sprouting stage. Power

consumption can be affected by temperature. The best way to control this issue is by including

an external fan to cool all electronics. Most microcontrollers have a switchback feature that

allows the controller to switch into a higher speed when an interrupt occurs.

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Budget

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Goals for Cycle 2

• Order basic parts such as Raspberry Pi, Arduino Due, cameras, and sensors • Write software for Raspberry Pi for SPI interfacing • Write software for Arduino Due for SPI and I2C interfacing • Build multispectral camera and complete testing • Create control loops in Arduino for environment and water regulation • Determine power requirements and begin developing power management system • Begin working on a user interface for the Raspberry Pi • Create design for robot arm movement and research calibration techniques • Finalize sensor list and sensor count and write software for any new interfacing standards

Timeline (NASA Organized)

DATE ACTION Early August, 2019 Faculty, technicians meet and plan

August 16 – August 30 Classes begin, projects announced, teams formed, first team meeting take place,

student training on Systems Engineering, plant basics, team communication strategies and software. Corporate

greenhouse visits by all participants, e.g. Agrinamics. Planting decision, begin

first planting cycles (1-2 months for leafy vegetables, 2-4 months for fruited plants,

plant growth and multiple cycles continued throughout the schedule)

August 30 – October 8 Kickoff Meeting begin formulating mission objective, system level requirements, tradeoffs, feasible

architectures etc. for SDR. October 8 System Definition Review and Report

October 8 – Nov. 11 Review NASA feedback, complete a preliminary design, PDR hardware and

software, top level requirements, feedback on plant growth and testing

Nov. 11 Preliminary Design Review and Report

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Nov. 11 – Jan. 21 Complete detailed design of all drawings, BOM, interfaces, software, electrical,

mechanical systems. Begin fabrication January 21 Critical Design Review and Report

Jan. 21 – March 11 Fabrication, assembly, verification and integration of subsystems, begin or have

begun last planting cycle for system testing

March 11 Progress Checkpoint Review March 11 - May 6 Complete assembly, testing, and

validation May 6, 2020 Project Completion and Report to NASA

Conclusion In conclusion, our group has done a lot of research with the other departments to

formulate a general idea of what we hope to achieve by the end of the semester. After having our

monthly meeting with NASA, it is evident to start the interfacing early to prevent issues from

occurring later in the design. Therefore, this will be our focus for the next cycle. Though we

maintain constant communication with the mechanical team, we are trying to implement as much

of our work as possible without them since many of their systems are not finalized. For Cycle 2,

we are going to complete interfacing software, build the multispectral camera, write code

outlines for the Raspberry Pi and Arduino, and begin developing a power management system.

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References [1] Soukup, Cara. “NDVI Plant Health and Your Farm.” Sentera, 23 Oct. 2018, https://sentera.com/understanding-ndvi-plant-health/. [2] Beisel, Nicole S., et al. "Utilization of single‐image normalized difference vegetation index (SI‐NDVI) for early plant stress detection." Applications in plant sciences 6.10 (2018): e01186.

[3] Self, Kevin. “Using Power Management with High-Speed Microcontroll.” Maxim, www.maximintegrated.com/en/design/technical-documents/app-notes/7/78.html.