Automatic Plant Health Monitoring Device based on NDVI Analysis

using Raspberry Pi for Water Apple Plant

Rizky Pratama Hudhajanto

, Nanta Fakih Prebianto

, Muchammad Fajri Amirul Nasrullah

and Jesy

Neland

Electrical Engineering, Politeknik Negeri Batam, Batam, Indonesia

Infomatics Engineering, Politeknik Negeri Batam, Batam, Indonesia

Keywords: NDVI, Raspberry Pi, Agriculture, Water Apple Plant

Abstract: Indonesia is a tropical country with abundant agricultural products. Therefore, the increase of efficiency and

productivity of these crops is needed. Normalized Difference Vegetation Index is an index used to monitor

plant health. To find out the NDVI value, a special camera is used to capture Near Infrared (NIR) light

spectrum. This special camera is usually used by the professional plantation industry and is very expensive.

In this study, we used a raspberry pi and a low cost Pi Noir camera to create a system that can predict the

NDVI value of plants. The plant used as testing object is a Water Apple Plant (Syzygium aqueum). The result

was that the system was able to identify the health of plant based on their NDVI value.

1 INTRODUCTION

As a nation that has a tropical nature, Indonesia is

provided with tremendous natural resources. One of

the natural resources that can be utilized as an energy

source is agricultural farm. This comprises of

plantations of rubbers, coconuts, oil palm, and so on.

This plantation spread in excess of 20 million

hectares of land area. From this number of land area

dedicated for agriculture, it means should be

considered to increase the efficiency and productivity

of the plantations.

Precision farming is a method to increase the

efficiency and productivity of farms or plantations.

Precision farming uses technology to identify plant

and manage its variability. As the results, the farmers

can determine the use of fertilizers, seeds, waters, or

pesticides efficiently. Many precision farming

technologies use the camera to identify the health of

the plant. The camera used is a special multispectral

camera. This multispectral camera can capture non

visible light spectrum. This non visible spectrum

sometime has information about plant’s condition and

health. If one can understand plant health precisely,

the use of fertilizers, waters, or pesticides can be more

efficient. This also will increase the productivity with

less capital cost.

There are many vegetation indices used in

precision farming. The most popular vegetation index

is Normalized Difference Vegetation Index (NDVI).

This NDVI can be used to determine the health of

plant. The high number of NDVI will indicate the

healthier the plant is. NDVI uses information from

light that is reflected by the plant leaves. This light

information is captured by special camera. The

special camera is a normal camera modified to

capture special light spectrum. Most of the light

spectrum captured is a non-visible light spectrum. So

NDVI uses the image processing technology to

calculate the index. This index indicates the

chlorophyll concentration of leaves, which means it

may also indicate the health of plants (Yang et al.,

2017).

Normal green leaves in a plant absorbs red light

and reflect near infra-red (NIR) light. This Red light

has 600 nm to 700 nm of wavelength, and NIR light

has 700 nm to 110 nm. Its absorption is the part of

photosynthesis process. This absorption process is

happened due to the presence of chlorophyll.

The reflected near-infrared light can indicate the

health of some type of plant. Equation (1) is the

equation to compute NDVI using near-infrared and

red captured by camera.

𝑁𝐷𝑉𝐼 

𝑁𝐼𝑅  𝑅

𝑁𝐼𝑅



𝑅

(1)

Hudhajanto, R., Prebianto, N., Nasrullah, M. and Neland, J.

Automatic Plant Health Monitoring Device based on NDVI Analysis using Raspberry Pi for Water Apple Plant.

DOI: 10.5220/0010352701150118

In Proceedings of the 3rd International Conference on Applied Engineering (ICAE 2020), pages 115-118

ISBN: 978-989-758-520-3

115

NIR is the average of color signal in the

wavelength of 800 to 1000 nm. R is the average of

color signal in the wavelength of 560 nm to 670 nm

range. The value of NDVI is in the range of +1 and -

1. NDVI value close to zero indicates that the health

of the plant is not god, while NDVI value +1 means

the plant is in good health condition. Figure 1 shows

the NDVI color range and its value.

Figure 1: NDVI color range.

Nowadays, the standard normal camera usually is

equipped with NIR blocking filter. The reason is that

the NIR light can make the photos taken from that

camera become unnatural. There are many ways to

make the camera can capture NIR light. The first

method modifies the lens of camera to unblock the

NIR light (Beisel et al., 2018; Rabatel et al., 2011;

Variyar et al., 2015). This method is sometime very

difficult. In some cameras, the lens is so small and

brittle. Modify the lens can make the camera broken

and unusable. The second method is to use a

specialized camera (Ritz et al., 2020; Vidoni et al.,

2017). This camera is made specially for capturing

NIR light. This camera is solution for who do not

want to modify the lens. However, the price of this

camera is so high. This camera is also rarely found on

many countries’ local market.

Distinct studies demonstrated that NDVI can be

measured by using only single camera (Rabetel et al.,

2011). The camera used by Rabatel et al. (2011) is a

Single Lens Reflect (SLR) Camera which is modified

by removing its Near Infrared Blocking Filter. This

modification is not easy. One must understand the

camera body parts and lenses. One mistake can make

the camera unusable. Another research from Glenn et

al. (2018), showed the measurement of NDVI by

using single Pi Noir Camera. This camera is a special

camera built for raspberry pi. This camera does not

employ an Infrared Filter, so that the resulting image

contains infrared information that is reflected by

objects. In spite of that, this camera stiil need a blue

filter. The Glenn NDVI measurement is calculated by

using NIR value and blue value. This means, Glenn

et al. measured blue NDVI in their research. Many

researches showed that NDVI calculated using blue

spectrum has less good result than using red

spectrum.

The development of the mini PC technology has

encouraged the use of the mini PC as the main

computer in monitoring plants. Wang et al. (2020)

have conducted research on the use of raspberry pi

and pi noir camera to see the NDVI value of corn

plants. At a cost of only 70-85 USD, they managed to

capture NDVI values as well as predict nitrogen

levels precisely. The Pi Noir camera as a camera to

monitor NDVI is also used by Avotins et al. (2020)

and Bicans et al. (2019). Avotins et al. (2020) uses the

Raspberry Pi Model 3 as the main computer to

capture and process the images. The captured data is

then sent using an internet connection to the cloud.

NDVI values, which are numbers, sometimes

cause the reader to have difficulty understanding

them. Therefore, Wijitdechakul et al. (2017) grouped

plant index values such as NDVI, NDWI, and SAVI

into semantic keyword groups. So that it can be

concluded directly by the system whether the plant is

drought, or the soil moisture of the plant is not good.

In this research, we propose NDVI calculation

by using two cameras. The first camera is standard

webcam camera, and the second is Pi NoIR Camera.

We use raspberry pi as a main image processor. From

the first camera, the red pixel information obtained

and from the second camera, The NIR pixel

information is obtained. The tests are carried out on

several conditions of leaf. The results are grouped in

several keywords such as “Healthy”, “Unhealthy”,

and “Dead”. These keywords are then showed to the

user via web applications.

2 METHODOLOGY

In this project, two cameras are used. The first camera

is standard RGB camera and the second one is Pi

NoIR camera. Figure 2 shows the system used in this

project. As an image data processor, Raspberry pi

type B is used. Rapsberry pi captures image from two

cameras. These two cameras have different resolution

and different view angle. Before image being

processed, the two images (stereo image) need to be

matched. After these images matched, the red pixel

information is extracted from first RGB camera.

Then, the NIR pixel information is extracted from

second camera.

Figure 2: Raspberry Pi system.

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The NDVI is calculated using equation (1). Then,

the result, is colormaped by using information shown

in Figure 1. The resulting NDVI image and NDVI

number are shown on the web application. This web

application is served by web server in the Raspberry

Pi. This application can be accessed by user from

everywhere. For the testing purpose, a leaf is placed

in front of the cameras. We used Water Apple

(Syzygium aqueum) leaves as testing object. The

leaves used for the test are shown in Figure 3.

Figure 3: Water apple leaves as testing object.

3 RESULT AND DISCUSSION

In the first test, the green healthy water apple leaf is

placed in front of the cameras. The resulting image is

shown in figure 4. As we can see in figure 4, the red

pixel dominates the image color. This red color is a

representation of the chlorophyll intensity in the leaf.

From the results of the NDVI calculation, the value

was 0.479, which means that the leaf is in good

health.

In the second experiment, an object of water

apple leaf that is torn and slightly yellow is placed in

front of the camera. The result captured by the system

is shown in figure 5. In contrast to the previous

healthy leaf, this leaf has more dominant blue color.

Only a few red pixels are visible. Many red pixels are

clustered on the outer side of the leaf. From the results

of NDVI calculations, the value is 0.142 which means

the plant is in an unhealthy state.

In the third experiment, the water apple leaf

object which looked dry was used as the test object.

The results of the received image is shown in figure

6. From the observations, it can be seen that almost

all leaf colors show blue. This means that the leaf is

dry, there is no indication of chlorophyll at all. From

the NDVI calculation, the value of -0.9 is obtained,

which means that the leaf is in a dead condition.

Figure 4: NDVI image of healthy green water apple leave.

Figure 5: NDVI image of not healthy green water apple

leave.

Figure 6: NDVI image of death green water apple leave.

In the fourth and final experiment, dummy plant

is used as testing object. The resulting image is shown

in figure 7. It can be seen that the image result shows

an all blue color. No red pixel is detected. This is

because in dummy plants, there is no chlorophyll

intensity. The result is the plant in dead condition.

Figure 7: NDVI image of dummy plant.

4 CONCLUSIONS

Nowadays, there are many NDVI measurement

solutions on the market, they are usually very

expensive and need very skilful personnel to operate.

The proposed system described in this paper presents

an affordable alternative to one who needs to know

the health of the plants from NDVI measurement

perspective. From the experimental results, this

system has succeeded in identifying the health of

water apple leaves and predicting the average NDVI

Automatic Plant Health Monitoring Device based on NDVI Analysis using Raspberry Pi for Water Apple Plant

117

value of the leaf. Furthermore, further research will

be carried out on the effect of NDVI values on crop

yields and water requirements of plants.

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