Estimation of Calcium, Magnesium and Sulfur Content in Oil Palm

using Multispectral Imagery based UAV

Muyassar Allam Suyuthi

1,*

, Kudang Boro Seminar

and Sudradjat

Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia

Department of Agronomy Horticulture, IPB University, Bogor, Indonesia

Keywords: Oil Palm, Multispectral Images, Nutrition, UAV.

Abstract: Oil palm is a commodity which contributes to the largest foreign exchange. In Indonesia, the area of oil palm

plantations has a large area compared to other commodities. Proper and efficient fertilization is needed to

reduce production costs. This study aims to estimate the nutrient content of calcium, magnesium, and sulfur

in oil palm plants using UAV-based multispectral cameras. The method used is divided into three stages, data

preparation, pre-processing, and data analysis. At the data preparation stage, the things done are leaf sampling,

sample coordinate points, and multispectral image capture. In the pre-data processing stage the things done

are stitching, georeferencing, and digitizing. The last stage is data analysis like multiple linear regression

analysis to get a model of the relationship of multispectral images and actual nutrition of lab test results. The

results of this study obtained a model for predicting of calcium content Ca = 0.994+0.00723*GREEN-

0.00863*RED EDGE, for magnesium content is Mg = 0.693+0.00531*RED-0.00541*RED EDGE, and for

sulfur content is S = -0.222+0.00338*RED EDGE. The results of overall accuracy using a confusion matrix

of 66.7% in the calcium model, 63.3% in the magnesium model, and 36.6% in the sulfur model.

1 INTRODUCTION

Oil Palm (Elaeis guineensis Jacq.) is a commodity of

plantation crops that contributes to the country's

largest foreign exchange because oil palm

commodities have high economic value in

agribusiness. Palm oil has benefits and advantages

compared to other commodity vegetable oils.

Indonesia is the country with the largest palm oil

production in the world with a contribution of 44.46%

of the total world CPO. This contributes to the quite

large Gross Domestic Product (GDP) reaching

13.14% in 2017. In Indonesia, the area of oil palm

plantations in 2017 according to the Central Statistics

Agency (BPS) in 2018 is estimated to reach more than

twelve million hectares, so to increase productivity

requires proper fertilization. Fertilization on oil palm

plants aims to provide nutrient needs for plants so that

plants can grow well and be able to produce optimally

and produce good quality oil (Adiwiganda and

Siahaan 1994). To get the right fertilization results,

fertilizer recommendations for the palm oil

commodity are needed. Fertilization

recommendations are to apply fertilization in a

manner and dose that have been determined in the

plantation area so that the most efficient absorption of

nutrients occurs in plants and can integrate the use of

mineral fertilizers and oil palm residues and minimize

environmental impacts associated with excessive

fertilization such as land degradation (Goh and

Hardter 2003). In addition to maximizing the

nutrients absorbed, it can also save production costs,

because according to Gerendas and Heng (2011) a

large proportion of the total production costs is

fertilization costs.

Essential nutrients are essential nutrients for

plants and their functions cannot be replaced by other

elements so plants cannot grow normally if they are

not available in sufficient quantities in the soil. The

essential nutrients studied in this research are calcium,

magnesium, and sulfur. Sudradjat (2016) states that

the main macro-nutrients needed by oil palm plants

are nitrogen, phosphorus, potassium, and magnesium.

While the micro-nutrient that often become obstacles

are copper, iron, manganese, and boron.

Chlorophyll greatly affects the level of light

reflectance in leaves. The reflectance of plants in

visible light (red, green, blue) (400-700 nm) and NIR

(700-900 nm) are strongly influenced by chlorophyll

and leaf cell structure (Stark et al. 2006). The light

Suyuthi, M., Seminar, K. and Sudradjat, .

Estimation of Calcium, Magnesium and Sulfur Content in Oil Palm using Multispectral Imagery based UAV.

DOI: 10.5220/0009978700002833

In Proceedings of the 2nd SEAFAST International Seminar (2nd SIS 2019) - Facing Future Challenges: Sustainable Food Safety, Quality and Nutrition, pages 127-134

ISBN: 978-989-758-466-4

127

reflectance pattern can be used to assess plant health

conditions related to the available of calcium,

magnesium and leaf sulfur nutrients. In this research,

parrot sequoia cameras are used to capture the light

reflectance of the leaves. The Phantom 4 Pro drone

with a Parrot Sequoia camera is used to take

multispectral images that can capture 4 color

spectrum bands (green, red, red edge, and near-

infrared). Table 1 shows the specifications of the

Parrot Sequoia camera.

Table 1 Specifications of Parrot Sequoia cameras.

Spesifikasi kamera Parrot Sequoia

Pixel 1.2 mp (1280x960)

Field of View 61.9

Green ban

530

–

570n

Red ban

640

–

680 n

Red Ed

e ban

730

–

740 n

Near Infrared ban

770

–

810 n

RGB 400

–

700 n

Sumber: Parrot SA (2017)

Aerial photography using Unmanned Aerial

Vehicles (UAV) is an alternative technology to get

more detailed, real-time, fast and cheaper data

(Shofiyati, 2011). UAV is a flying robot with remote

control that is able to carry payloads according to its

purpose and designation. This drone is capable of

carrying cameras to photograph and record and can be

flown to reach certain locations by remote control by

pilots. Many advantages if monitoring is carried out

with UAVs, including low investment and

operational prices, fast and flexible information

acquisition times, and information generated can be

more detailed than satellite data. In addition, the UAV

in transition flies under the cloud so that its image is

cloud free when compared to satellite imagery which

depends more on atmospheric conditions (Dony

2014).

This research was conducted to estimate the

calcium, magnesium, and sulfur macro-nutrient

content of oil palm plants quickly and accurately by

utilizing phantom drones 4. Taking images using

multispectral parrot sequoia cameras that have high

resolution. As well as leaf analysis to determine the

nutrient content of calcium, magnesium, and sulfur in

oil palm. The results of the image and nutrient content

are correlated and interpreted in the form of a model.

2 RESEARCH METHODS

2.1 Time and Location

The study was conducted from February 2019 to

April 2019 at the IPB-Cargill Oil Palm Education and

Research Plantation, Singasari, Jonggol, Bogor, West

Java with coordinates 06

28,319' South Latitude,

107

01.103' East Latitude and located 116 m above

sea level. The research was also conducted at the

Bioinformatics Engineering Laboratory of

Mechanical and Biosystem Engineering, Faculty of

Agricultural Technology, IPB University and Testing

Laboratory of the Department of Agronomy and

Horticulture, Faculty of Agriculture, IPB University.

2.2 Materials and Tools

The tool used in this research is a computer with i7

processor speed of 2.4 GHz and has 8 GB of RAM.

Software used to process data are Microsoft Excel,

Microsoft Word, QGIS 2.18, Pix4DMapper, Agisoft

PhotoScan, and Minitab 18. Primary data used in this

study were leaf nutrition obtained from leaf analysis

testing at the Testing Laboratory of the Department of

Agronomy and Horticulture, Faculty Agriculture,

Bogor Agricultural University. Another primary data

used is aerial photography from a 0.076 resolution

Parrot Sequoia multispectral camera with the help of

a Phantom 4 Pro drone.

2.3 Research Stages

The research began with the preparation phase of the

data consisting of leaf sampling, leaf sample

coordinate points and Ground Control Point (GCP),

and drone image capture. Then the leaf samples were

analyzed at the Laboratory to determine the nutrient

content used as the dependent variable. The drone

image data obtained must be carried out in the pre-

data processing stage. This stage consists of photo

stitching, georeferencing, and digitization of garden

boundaries and canopy of sample plants. The results

of the pre-processing data are in the form of reflectant

values in each sample plant and will be used as an

independent variable. After that the data analysis is

processed by multiple linear regression with stepwise

methods using independent variables and dependent

variables. The results of the regression model are then

displayed in the form of an nutrient estimation layer.

2nd SIS 2019 - SEAFAST International Seminar

128

2.3.1 Data Preparation Stage

In the data preparation stage, several things are

carried out, namely literature study, method

determination, discussion, and data collection. After

the research planning is done, the data collection

stage is carried out. The data collection phase is done

by three things, namely sampling, drone imagery, and

GCP. Leaf sampling is carried out in a spread where

the sample plants are proportionally determined.

Guidelines for sampling leaves on oil palm plants

based on Winarna et al. (2005) where the leaves used

as the main sample must meet a number of provisions,

namely they are not the mains of inserts, grow

normally, do not lie adjacent to roads or ditches/rivers,

do not coexist with insertion trees and are not attacked

by pests or diseases.

Leaf samples taken were leaves from the 17th

midrib. According to Chapman and Gray (1949) in

Pahan (2006) said that the leaves of the 17th midrib

are the most sensitive leaves because they show the

greatest difference in nutrient levels. In addition,

nutrient status on the 17th leaf has a better correlation

to crop production when compared to other younger

leaves. The leaves of the 17th midrib are taken by six

leaflets (three strands on the left and three strands on

the right at the meeting point of the two sides of the

midrib). Leaves that have been taken are stored in

envelopes that have been labeled according to the

location of the sample. The selected sample plants

were given raffia to indicate the tree was a sample

plant. Then the sample plants are marked on GPS

which will be used to correct the geometry between

the map and the image results. Samples that have been

obtained were analyzed for nutrients of calcium,

magnesium, and sulfur in the Testing Laboratory of

the Department of Agronomy and Horticulture.

Multispectral image capture using a Phantom 4

drone with a Parrot Sequoia camera. Drone flight

planning automatically uses the Pix4D Mapper

application which is adjusted to the taking land area

and hours of time that can be taken by the drone.

According to Kasih (2012) the optimum shooting is

done in the morning because the effect of reflected

light from the sun is still weak. Besides the wind

speed in the morning still tends to be low, thereby

reducing the risk of UAV shake while shooting which

can cause poor quality captured images.

GCP determination aims to reduce errors or

changes in position when integrating the results of the

image into a map that can make the data change. GCP

can be in the form of objects, buildings, or forms of

certain locations that can be clearly seen on the image

so that the coordinates of the object can be measured

to be used as a reference point when uniting images

into a complete map. (Adillah 2018). A tool to draw

GCP coordinates can be by using a handheld GPS.

2.3.2 Pre-processing Stage

The objectives of the pre-processing stage such as

normalization and noise reduction are to produce

clean and ready-to-use data (Nanda et al. 2019; Nanda

et al. 2018a; Nanda et al. 2018b). At this stage, the

stitching and georeferencing process is performed

using pix4Dmapper software. The stitching process is

basically a combination or combination of two or

more different images to create or form one image

called a panorama (Kale and Singh 2015). Before

stitching, georeferencing needs to be done, namely

the process of giving geographic references to raster

or images that do not yet have a coordinate system

reference. The coordinate reference used is WGS 84 /

UTM zone 48S with EPSG: 32748.

Then do bordering of the garden and canopy of the

sample plants done using QGIS software. The results

of the bordering of the sample plant canopy are used

to extract the reflectance value in the form of a digital

number at each pixel. The tool used to extract these

values is Zonal Statistics on the Raster menu.

2.3.3 Data Analysis Stage

Estimation using multiple linear regression analysis

with the stepwise method is performed by Minitab

which aims to determine the factors that effect and

make a estimation model of calcium, magnesium, and

sulfur nutrition. At this stage two types of variables

are needed, namely the independent variable and the

dependent variable. The independent variable

contains the reflectance value data which is the

average value of the drone image in the digitizing

attribute of the sample plant. While the dependent

variable contains the actual calcium, magnesium, and

sulfur nutrition data from the 17th midrib nutrient

analysis results in the sample plants.

The model obtained is then evaluated to see the

strength of the estimator model. The method used to

evaluate the model is the Mean Absolute Percentage

Error (MAPE). MAPE shows how big the difference

between the actual results and the predicted results.

Table 2 shows the criteria used to decide the

predictive power of an estimator model.

Estimation of Calcium, Magnesium and Sulfur Content in Oil Palm using Multispectral Imagery based UAV

129

Table 2: Criteria for estimating the strength of the model.

MAPE

(

)

Power of

rediction

<10 Ver

Goo

–

20 Goo

20-50 Moderate

>50 Ba

Source: Wang et al. (2012)

After that, the nutrient estimation layer is made

which contains the estimated nutrient value for each

pixel. The estimated nutritional value is the

application of the model obtained using the raster

calculator tool in the QGIS application. Then the

estimate value is classified based on the criteria in

Table 3.

Table 3: Nutrition concentration of 17th fronds on the age

of oil palm is more than 6 years.

Nutrition Unit Deficienc

Optimu

Excess

Ca %DM <0.25 0.50-0.75 >1.00

Mg %DM <0.20 0.25

–

0.40 >0.70

S %DM <0.20 0.25-0.35 >0.60

Source: Von Uexkull and Fairhurst 1991

The results of the nutrient estimation layer that has

been classified will be tested for accuracy to see the

size of the miscalculation of nutrient estimates so that

it can be determined the percentage of accuracy of

nutrient estimation. Accuracy tests are performed

using a confusion matrix.

3 RESULT AND DISCUSSION

Overall data were collected on 6 February 2019 at the

Cargill IPB Oil Palm Education and Research

Plantation. The oil palm plantation, which was

cultivated starting in 2012, has an area of 59 hectares

which is divided into 5 plantation blocks. There are 3

types of data taken in this study, namely the 17th

frond leaf sample, the coordinates of the sampling

location and GCP, and the multispectral image.

Retrieval of data is done in a day because the data

needed is dynamic. Retrieval of image data using a

DJI Phantom 4 drone with a Parrot Sequoia

multispectral camera. The drone was flown in

accordance with the Parrot Sequoia handbook, which

is 110 meters above the ground, with a drone speed of

10 m/s and a pause time of 2.1 seconds for shooting.

The next step is the pre-processing of data in the form

of stitching, georeferencing and bordering. Before

that stage aerial photo printing results are selected

first using Agisoft PhotoScan then stitching and

georeferencing using QGIS. Restrictions on oil palm

plantations and sample canopy are done manually

using the add feature tool after creating a new

polygon layer in QGIS software. Figure 1 and Figure

2 show the results of bordering the sample plant

canopy digitized to extract the average value of the

digital number (DN) at each pixel. The extraction

process is carried out on all layers of stitches using

Zonal Statistics in QGIS. Extraction results can be

seen in appendix 1.

Figure 1: Results of the borderingon oil palm plantations.

Figure 2: Results of the bordering canopy of sample plants.

Nutrition estimation is represented in the form of

mathematical models. Modeling uses the Minitab 18

application with stepwise multiple linear regression

analysis. Modeling involves 23 samples from 30

samples. While the remaining 7 samples are used for

model validation. The independent variable used is

the average DN value of each pixel obtained from the

digitized of the plant canopy sample and the

dependent variable is the nutrient content of calcium,

magnesium, and sulfur laboratory test results. The

results of the stepwise multiple linear regression

model are as follows:

Ca= 0.994 + 0.00723 GREEN- 0.00863 RED EDGE

Mg= 0.693 + 0.00531 RED- 0.00541 RED EDGE

S = -0.222 + 0.00338 RED EDGE

The model obtained is then predicted with stat>

Regression> Regression> Predict. Table 4 shows a

2nd SIS 2019 - SEAFAST International Seminar

130

comparison of actual and predicted nutrition.

Evaluate the model using Mean Absolute Percentage

Error (MAPE). Evaluation of the model is done to see

how strong the estimation of the model is made. The

results of the model evaluation obtained the value of

MAPE in the Ca model, Mg model, and the S model

respectively 22.72%, 11.62%, and 46.5%. Based on

the strength estimation criteria of the model according

to Wang et al (2012) in table 2, the evaluation results

show that the Ca and S models are categorized as

medium and the Mg model is categorized as good.

The model obtained is then interpreted in the form of

an estimated nutrient layer that has been processed

and classified according to table 3. Classification of

nutritional assumptions is presented in the form of a

color that matches the nutrient content. The red color

indicates that the oil palm tree on the grid lacks

nutrients, yellow under optimal conditions, green at

optimum conditions, light blue at above optimum

conditions, and dark blue at excess nutrient conditions.

Figure 3 shows the results of the calcium nutrient

estimation layer, Figure 4 for magnesium nutrition,

and Figure 6 for sulfur nutrition.

The result of nutrient estimation layer is

performed by accuracy test using a confusion matrix.

In the confusion matrix the predicted nutrition of the

model results is compared with the actual nutrition of

the lab test results. From the results of the accuracy-

test using a confusion matrix, Overall Accuracy

obtained in the nutrients of calcium, magnesium, and

sulfur was 66.6%, 63.3%, and 36.7%. According to

Jaya (2015) a good accuracy value is an accuracy

value that has reached a score of> 85%, so the

estimation of calcium and magnesium nutrients has

moderate accuracy and for sulfur has poor accuracy.

Table 4: Comparison of actual nutrition and prediction of the 17th midrib leaf laboratory results.

Sample

nesium

(

%DM

)

Kalsium

(

%DM

)

Sulfur

(

%DM

)

Actual Prediction Actual Prediction Actual Prediction

1A 0.36 0.296 0.51 0.643 0.15 0.231

1B 0.34 0.296 0.69 0.594 0.17 0.282

1C 0.19 0.304 0.53 0.616 0.17 0.266

1D 0.32 0.316 0.82 0.668 0.13 0.193

1E 0.32 0.284 0.69 0.622 0.43 0.264

2A 0.32 0.383 0.39 0.621 0.18 0.193

2B 0.42 0.356 0.59 0.642 0.14 0.218

2C 0.32 0.378 0.69 0.649 0.13 0.184

2D 0.41 0.390 0.91 0.658 0.40 0.167

2E 0.39 0.353 0.79 0.651 0.17 0.203

2F 0.35 0.351 0.68 0.638 0.17 0.233

2G 0.34 0.379 0.67 0.683 0.07 0.156

2H 0.44 0.386 0.42 0.668 0.09 0.181

3A 0.40 0.357 0.48 0.632 0.46 0.220

3B 0.38 0.370 0.68 0.603 0.40 0.265

3C 0.36 0.424 0.82 0.671 0.16 0.125

3D 0.30 0.375 0.79 0.615 0.14 0.236

3E 0.40 0.385 0.61 0.633 0.22 0.192

3F 0.41 0.389 0.66 0.661 0.34 0.157

3G 0.47 0.355 0.62 0.647 0.23 0.202

3H 0.45 0.416 0.55 0.629 0.20 0.182

3I 0.41 0.461 0.60 0.582 0.23 0.218

3J 0.49 0.458 0.51 0.583 0.28 0.221

0.44 0.410 0.70 0.570 0.19 0.286

3L 0.33 0.375 0.67 0.591 0.36 0.263

4A 0.48 0.362 0.56 0.608 0.26 0.247

4B 0.50 0.423 0.72 0.634 0.10 0.147

4C 0.37 0.387 0.87 0.625 0.17 0.169

4D 0.43 0.413 0.45 0.619 0.15 0.165

4E 0.40 0.382 0.52 0.577 0.15 0.243

Estimation of Calcium, Magnesium and Sulfur Content in Oil Palm using Multispectral Imagery based UAV

131

Figure 3: The calcium nutrient estimation layer.

Figure 4: The magnesium nutrient estimation layer.

2nd SIS 2019 - SEAFAST International Seminar

132

Figure 5: The sulfur nutrient estimation layer.

4 CONCLUSION

The nutritional estimation model was made by using

stepwise multiple linear regression analysis. The

independent variable used is the average

multispectral reflectance value in the form of a digital

number in the canopy of the sample plant and the

dependent variable used is the nutrient content of

calcium, magnesium, and sulfur on the 17th midrib

leaf obtained from analysis in the testing laboratory

of the Department of Agronomy and Horticulture,

IPB. The model obtained from multiple regression

analysis for calcium nutrition is Ca= 0.994 + 0.00723

*GREEN - 0.00863*RED EDGE, for magnesium

nutrition is Mg= 0.693 + 0.00531*RED - 0.00541

*RED EDGE, and for sulfur nutrition is S= -0.222 +

0.00338*RED EDGE . Evaluate the model using

Mean Absolute Percentage Error (MAPE). MAPE

values in the Ca, Mg, and S models are respectively

22.72%, 11.62%, and 46.57%. The results of the

model evaluation show that the strengths of the Ca

and S models are in the moderate category and the Mg

model is in a good category. While the accuracy of

the nutrient estimation layer using confusion matrix

obtained Overall Accuracy value for estimating

calcium nutrition by 66.7%, magnesium nutrition by

63.3%, and sulfur nutrition by 36.7%. The results

show the accuracy of estimation of calcium and

magnesium nutrition including moderate grade, while

the estimation of sulfur nutrition is poor.

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APPENDIX

Digital number values of digitization of the sample plant

canopy.

Sample Green NIR Re

Red Edge

1A 97.696 150.290 55.234 126.499

1B 99.226 162.430 60.269 128.360

1C 107.011 158.916 62.020 128.823

1D 100.055 149.172 54.509 120.953

1E 101.391 162.627 55.288 125.934

2A 92.608 131.776 71.301 127.583

2B 110.319 123.704 75.585 139.960

2C 103.668 124.700 73.264 132.114

2D 104.833 123.975 73.732 129.730

2E 107.398 130.823 70.143 133.121

2F 118.497 135.772 75.499 137.183

2G 112.261 130.371 69.891 125.834

2H 122.389 129.942 77.669 131.776

3A 107.613 133.104 73.033 134.595

3B 125.211 138.418 85.341 141.739

3C 102.690 124.847 73.835 121.059

3D 114.509 134.506 80.459 137.144

3E 103.420 133.144 73.862 128.594

3F 101.884 131.221 69.520 123.551

3G 105.717 133.299 69.451 131.241

3H 108.704 131.464 80.579 128.028

3I 121.342 135.507 97.446 132.733

3J 122.091 132.585 98.171 135.082

134.299 137.072 99.472 146.368

3L 114.651 134.315 84.555 141.575

4A 111.116 137.428 77.785 136.967

4B 90.891 131.026 72.818 119.278

4C 79.805 129.379 66.129 122.606

4D 85.741 126.997 73.204 123.770

4E 96.247 141.433 77.157 131.028

2nd SIS 2019 - SEAFAST International Seminar

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