Estimation of Paddy Leaf Nitrogen Status using a Single Sensor

Multispectral Camera

Muliady Muliady

, Tien Sze Lim

, Voon Chet Koo

and Nathaniel Pius Winata

Department of Electrical Engineering, Maranatha Christian University, Jl. Surya Sumantri 65, Bandung, Indonesia

Faculty of Engineering and Technology, Multimedia University, Malacca, Malaysia

Keywords: Paddy Leaf Nitrogen Status, Single Sensor Multispectral Camera, Normalized Difference Vegetation Index

(NDVI), SPAD Meter, Regression.

Abstract: Rice consumption will be increasing by 26% in the next 25 years since 2010. The common practice to achieve

high rice production is by fertilizing the paddy with a proper quantity of nutrients, especially nitrogen (N). A

lot of previous researches were done to estimate the paddy N status, starting from using a simple Leaf Color

Chart (LCC) to high technology hyperspectral images taken from a satellite. This research used a MAPIR

Survey3 multispectral camera, which is affordable and gives the advantage of a quick and efficient practice.

The problem came out due to the impossible to fully separate the spectral channels of the images, which

causes low accuracy and imprecise data. This research objective is to correct the data by relating it with a

SPAD meter. A total of 75 paddy plants were sampled in the panicle initiation stage from two paddy fields

located at Margaasih and Cimahi, Jawa Barat, Indonesia. The Normalized Difference Vegetation Index

(NDVI) for each image was calculated after calibrated, cropped, and segmented. The result is a regression

of a 2

order equation with 6.96% of mean error. The regression equation was used to create a SPAD color

map to estimate the paddy leaf N-status.

https://orcid.org/0000-0003-0377-1524

https://orcid.org/0000-0002-7899-8750

https://orcid.org/0000-0002-3617-1069

https://orcid.org/0000-0001-6686-9305

1 INTRODUCTION

World rice demand is predicted will be increasing by

26% from 2010 to 2035 (Riaz & Hussain, 2020). The

prediction was projected based on the population

growth data from the United Nations and the income

data from Food and Agricultural Policy Research

Institute (FAPRI). The success in increasing the rice

yield by the previous research is still needed to follow

by a faster rice yield growth to compensate for the

pressure on paddy lands in the developing world from

urbanization, and climate change. One of the most

important factors is crop management, particularly

fertilizing practice. The appropriate time and amount

of fertilizer will significantly improve rice

productivity and reduce production costs. The

conventional practice in implementing fertilizer

management is by predicting the greenness level of

the paddy leaves. The leaf greenness is directly

related to leaf nitrogen (N) status, which is needed to

promote the growth of the paddy. Farmer experiences

and skills to predict the leaf greenness level will be

varied to each other, inaccurate, and imprecise. In

addition, the complexity arises because the level of

leaf greenness depends and changes on the life phase

of the paddy. An inexpensive and easy-to-use tool,

the Leaf Color Chart (LCC), has successfully

improved the farmer decision-making process in N

management and optimize N use at reasonably high

yield levels (Ahmad et al., 2016). The disadvantage

in using LCC is the need for a well-trained skilled

farmer to interpret the color chart. A high

technology device called Soil Plant Analysis

Development (SPAD) chlorophyll meter is widely

used to determine the leaf N level and increased the

efficiency of N fertilizer (Hussain et al., 2009). The

SPAD meter works on measuring the absorbances of

the leaf in the red and near-infrared light wavelength

Muliady, M., Lim, T., Koo, V. and Winata, N.

Estimation of Paddy Leaf Nitrogen Status using a Single Sensor Multispectral Camera.

DOI: 10.5220/0010743900003113

In Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science (ICE-TES 2021), pages 26-31

ISBN: 978-989-758-601-9

and calculates a SPAD value that is proportional to

the chlorophyll in the leaf. This will need a lot of work

and time if implemented in a large area of paddy

fields. The modern electronic and computer device

can be used to reduce time and work, (Zhang &

Zhang, 2018) introduced several imaging

technologies for plant high-throughput phenotyping,

including detection of canopy chlorophyll content,

leaf, and canopy senescence. (Muliady et al., 2021)

gave a solution by using a smartphone camera with a

light sensor, and a k-Nearest Neighbor (k-NN)

machine-learning algorithm to estimate the paddy N

status. The paddy leaf N status estimation becomes

easy and affordable since almost everyone owns a

smartphone with a camera. The work (Peter et al.,

2017) demonstrated how a digital camera can be a

low-cost and effective device for estimating the

paddy leaf N status under field conditions. For a large

paddy field area, the application of Unmanned Aerial

Vehicle (UAV) in crop monitoring and pesticide

spraying was evaluated (Mogili & Deepak, 2018).

Finally, a promising result in developing low-cost

multispectral imaging with a UAV system to create

a Normalized Difference Vegetation Index (NDVI)

map (Natividade et al., 2017). Farmers in low-middle

income countries wish to have modern but affordable

technology to assist their fertilizer management of

large paddy fields efficiently.

2 METHODS

The use of high technology devices or high-cost

technology with the support of computer science

does not guarantee the quality in estimating the

paddy leaf N content result. The comparison of a

commercial multispectral camera Parrot Sequoia that

costs USD3,500 while a low-cost multispectral

camera Mapir Survey3 only costs USD400. This

research used Mapir Survey3 camera with a 3.37mm

wide lens which is affordable and can minimize the

effect of the visible light in estimating the paddy leaf

N status but still has the advantage of quick and

efficient field practice.

The main weakness of this affordable

multispectral camera is it only has one sensor to

collect three light wavelengths simultaneously. This

will cause contamination between each light

wavelength and sensitivity to the noise that comes

from the surrounding environment. Another downside

of using is it gives a lower NDVI value than it is

supposed to, even a shaded area gives a higher NDVI

value than the unshaded area. Normally at the

beginning of the panicle phase, paddy will have a

0.63 to 0.72 NDVI value (Lestari et al., 2020). This

research objective is to correct and map the

calculated NDVI value of multispectral image from

a Mapir Survey3 Camera with a SPAD meter.

The experiments were taken in two paddy fields

in Jawa Barat - Indonesia, which is located in the

southern and northern part of Bandung city. The first

paddy field is located at Ciawitali, Citeureup,

Kecamatan Cimahi Utara-Cimahi, and the second

one is located at Cibisoro, Nanjung, Kecamatan

Margaasih-Bandung. The work consists of three

steps which are processing and calculating the

multispectral images into NDVI value, measuring

the leaf’s SPAD value, and regression analysis. All

the data was taken at the vegetation stage of the

paddy, right before the panicle stage, about 67 days

after transplanting. It is usually considered as the

time for the farmer to fertilize their paddy field, and

high concern about the nutrition is needed to prepare

the paddy for the reproductive phase. The

multispectral images were taken manually at a high

angle position. This position allowed the canopy of

the paddy plant to be captured for estimating the leaf

N status as suggested in (Yu et al., 2013).

2.1

Multispectral Images

The selection of Mapir Survey3 Camera filter will

highly influent the contrast between the soil and the

paddy plant. As suggested in Mapir’s manual guide,

the one with Orange Cyan Near-Infrared (OCN)

filter has better contrast than the generally used Red

Green Near-Infrared (RGN) filter. One of the most

frequently used Vegetation Indices (VIs) is a

normalized ratio between the red and near-infrared

bands be known as the Normalized Difference

Vegetation Index (NDVI) (Xue & Su, 2017). The

NDVI simply shows the plant photosynthetic

activity in values between − 1 and 1. A low NDVI

value indicates moisture-stressed vegetation and a

higher value indicates a higher density of green

vegetation.

The field experiment shows that the multispectral

images were affected by the intensity and the

direction of the sunlight. A calibration target in

Figure 1 is supplied by Mapir, was used to

compensate for the light intensity of the paddy

images, and then calibrate them in a computer using

Mapir Camera Control application. The calibration

target has a QR code on the right side and four

pieces of calibration surface on the left side. The

calibration process uses a linear regression between

4 points comparing pixel values to known target

reflectance. The calibration target and the paddy

Estimation of Paddy Leaf Nitrogen Status using a Single Sensor Multispectral Camera

images should be taken under the same or similar

light intensity.

Figure 1: The calibration target.

The paddy plants with a minimum of 10 tillers

were randomly chosen for data analysis, 25 paddy

plants from Ciawitali field, and 50 paddy plants

from Cibisoro field. Each paddy plant was marked

with a number and captured in 3 different canopy

angles to minimize the effect of sunlight direction.

From these 3 images, their NDVI value will be

calculated ang then averaged to represent the NDVI

value of the paddy plant. Figure 2 shows the image

sample of OCN paddy plant, the one located at the

center of the image is considered as the object that

will be analyzed.

Figure 2: The paddy plant in OCN image.

The direction of sunlight usually generates a

shaded area on the image that should be avoided. By

referring to the NDVI color chart on the right side, the

leaf with a shaded area in Figure 3 has a higher NDVI

value (dark green) compared to the other areas. To

minimize this error the images should be taken in the

mid-day, about 10.00 A.M. to 2.00 P.M, which will

give a minimum shaded area for the leaf canopy.

After the images were processed and calibrated

using Mapir Control Camera application, they were

cropped manually to have only one paddy plant.

Before the NDVI value is calculated, the image will

be segmented.

Figure 3: The shaded area increases the NDVI value.

2.1.1

Segmentation

The cropped OCN images were segmented in two

steps process, the first one is to eliminate the soil

background by separating the pixels with the Near-

Infrared (NIR) intensity value of more than 8000 as

paddy leaves. The second segmentation is to classify

the leaves into several groups based on the nearest

NIR band intensity which is shown in Figure 4. The

group with the most pixels is selected to represent

the paddy plant for example the violet pixels in

Figure 4.

Figure 4: The image segmentation process.

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

2.1.2

NDVI Calculation

The leaf NDVI value was calculated using Equation

1 after the segmentation process was done. The

result is shown in Figure 5 with the color map. The

final NDVI value that represents the paddy plant

was obtained by calculating the average NDVI of all

segmented leaf pixels. The calculation and color

plotting was done in Matlab.

(𝑁𝐼𝑅 − 𝑂)

𝑁𝐷𝑉𝐼 =

(𝑁𝐼𝑅 + 𝑂)

(1)

Where:

NIR is a reflectance in the near-infrared band; O is a

reflectance in the orange band

Figure 5: The color map of NDVI value.

2.2

SPAD Value

TYS-4N model of the SPAD meter was used as a

benchmark to be compared with the NDVI value.

The reference (Yuan et al., 2016) suggested

measuring the SPAD value at one-third from the

fourth leaf tip. To minimize the human error in

measuring the SPAD value, each leaf was measured

a minimum of 5 times, and conduct the measuring to

a minimum of 5 tillers with a full open fourth leaf.

The average value was calculated after eliminating

the outlier and represent the SPAD value of the

paddy plant.

2.3

Regression Analysis

The sampled data were collected from the Ciawitali

paddy field is 25 paddy plants and the other 50

paddy plants from the Cibisoro paddy field. Since

each paddy plant was captured 3 times, the total of

images was 225. Although the sampled data were

carefully taken and repeated several times, some of

the data need to be checked. The imprecise and

untrusted data will be eliminated. The imprecise data

was defined by a significant difference in NDVI

value between 3 OCN images. As an example, the

paddy plant number 11 in the Ciawitali field has

calculated NDVI from the first until third OCN

images respectively, 0.092, 0.017, and 0.042.

Finally, only 15 data set from the Ciawitali field in

blue color, and 49 data set from the Cibisoro field in

orange color were selected and plotted in Figure 6.

The paddy in the Ciawitali has a relatively lower

SPAD value than the paddy in the Cibisoro field.

Figure 6: The final data set distribution.

The regression equation was expressed in 1

and

order using Equation 2. The results were shown

in Figure 7, with the red and blue line is constructed

from the 1

order and 2

equation respectively.

Both of the equations have several similarities,

which are the same mean error of 6.96%, and

correlation of 0.39, a closed value of slope (B), and

y-axis intercept (C).

𝑦 =

𝐴

𝑥

+ 𝐵𝑥 + 𝐶

(2)

order: A=0, B=42.55, C=32.84

order: A=-21.72, B=45.27, C=32.77

Figure 7: The regression line.

Estimation of Paddy Leaf Nitrogen Status using a Single Sensor Multispectral Camera

3 RESULTS AND DISCUSSION

The 2

order regression was implemented to create

a color map that shows the SPAD value of the

Cibisoro paddy field. Figure 8 is a Cibisoro paddy

field stitching image from the OCN images were

taken by DJI Phantom 4 Pro Obsidian. The field in a

red rectangular indicates the new paddy just planted.

The yellow rectangular is kale vegetable field.

Figure 8: OCN image.

Figure 9 shows the NDVI color map of the

Cibisoro paddy field, with the NDVI value between

0.1 to 0.3. The map also shows the heterogenous of

the paddy leaf N status, but could not gives more

information about the fertilizer sufficiency regarding

the inaccurate NDVI value.

Figure 9: NDVI color map.

Figure 10 is the SPAD color map and shows the

distribution of the paddy N status. A high yield

paddy usually has a SPAD value between 35.4 to

40.1 (Swain & Jagtap Sandip, 2010). The grey

marking on the map is the paddy with a low SPAD

value of about 30 that indicates the deficiency of N

fertilizer. In practice, this SPAD color map will help

the farmers to analyze their paddy field, and the

treatment needs to conduct to gain a high rice yield.

Figure 10: SPAD color map.

4 CONCLUSIONS

The research work process has successfully built a

SPAD color map that can be used to estimate the

paddy leaf N status. The SPAD value is presented

clearly, and informatively in high contrast color.

This quick and efficient analysis process starts with

capturing the NDVI image using a drone attached

with Mapir Survey3 OCN camera when the paddy

enters the panicle initiation phase and processes the

images with a computer to have a SPAD color map.

Future work can be improved by reducing the mean

error using a machine learning algorithm.

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Estimation of Paddy Leaf Nitrogen Status using a Single Sensor Multispectral Camera