Soil Characterization and Classification: A Hybrid Approach of Computer Vision and Sensor Network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 2, April 2018, pp. 989~995
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i2.pp989-995  989
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Soil Characterization and Classification: A Hybrid Approach of
Computer Vision and Sensor Network
Abrham Debasu Mengistu, Dagnachew Melesew Alemayehu
Departement of Computing, Bahir Dar University, Ethiopia
Article Info ABSTRACT
Article history:
Received Sep 9, 2017
Revised Dec 25, 2017
Accepted Jan 11, 2018
This paper presents soil characterization and classification using computer
vision & sensor network approach. Gravity Analog Soil Moisture Sensor
with arduino-uno and image processing is considered for classification and
characterization of soils. For the data sets, Amhara regions and Addis Ababa
city of Ethiopia are considered for this study. In this research paper the total
of 6 group of soil and each having 90 images are used. That is, form these
540 images were captured. Once the dataset is collected, pre-processing and
noise filtering steps are performed to achieve the goal of the study through
MATLAB, 2013. Classification and characterization is performed through
BPNN (Back-propagation neural network), the neural network consists of 7
inputs feature vectors and 6 neurons in its output layer to classify soils.
89.7% accuracy is achieved when back-propagation neural network (BPNN)
is used.
Keyword:
Arduino uno
BPNN
Computer vision
Sensors
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Abrham Debasu Mengistu,
Departement of Computing,
Bahir Dar University,
Ethiopia.
Email: abrhamd@bdu.edu.et
1. INTRODUCTION
Agricultural production has been highly dependent on natural resources for centuries. The
maintenance of good soil quality is vital for the environmental and economic sustainability of annual
cropping. A decline in soil quality has a marked impact on plant growth and yield, grain quality, production
costs and the increased risk of soil erosion [1]. The field of Computer vision and digital image processing
(DIP) is continuously evolving and is finding many applications in several fields. Soil classification and
characterization is an important aspect of geotechnical engineering which has been given a great amount of
attention since the past few years. Image data in geosciences are common and require processing and
measurement schemes that range from small microscopic scales to large remote sensing scales. They focused
mainly to the first category and specifically in images of thin soil sections. The goal of soil micro
morphology, as a branch of soil science, is the description, interpretation, and measurement of components,
features, and fabrics in soils at a microscopic level. In this paper the author focused on texture analysis and
segmentation techniques of the given soil type [2].
The technology of computer vision and sensor network advancement is gradually finding
applications in different problem domains like in the areas of health and GIS industries. Efforts are being
geared towards the replacement of human operator with automated systems, as human operations are usually
inconsistent and non efficient. Automated systems in most cases are faster and more precise. However, there
are some basic infrastructures that must necessarily be in place in automation. In this research we will apply
both computer vision and sensor network to characterize soils and classifies soil type so as proper
measurement has to be taken to maximize the agricultural production [3].

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 989 – 995
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David C. Marvin, et al., in their work stated that recent research on sensor networks has focused on
networking techniques and networked information processing suitable for highly dynamic environments and
resource-constrained sensor nodes. Sensor nodes have decreased in size and are much cheaper, resulting in
the emergence of many new civilian applications from environment monitoring to vehicular and body sensor
networks [4]
Mingyuan Zhang, et al., in their work entitled as “Applying Sensor-Based Technology to Improve
Construction Safety Management” stated that the development of sensor-based technologies has greatly
improved information collection, data transmission and processing, which can serve as the foundation of the
modernization of construction safety management. After nearly two decades of development, sensor-based
technologies have facilitated the transformation from experimental exploration to practical applications. The
applications of sensor-based technology in construction safety management have become the focus of current
research [5]. In this research work, computer vision and sensor network are considered to characterize soils
and classifies soil type so as proper measurement has to be taken to maximize the agricultural production.
Depending on the knowledge and experience of the expert it is difficult to characterize and classify
soil. So Different methods have been used to classify soil to their corresponding class and characterize the
moisture level. Pravat Kumar Shit, et al., conducted a study to estimate soil crack for moisture analysis, from
the experiment 72.7% is archived [6]. K. Srunitha, et al., conducted a study to classify soil. The authors have
used texture and color as a feature vector and support vector machine to classify soil types. Besides, they
stated that, Soil characteristics identification and classification is very important in agriculture to avoid
agricultural product quantity loss [7]. Therefore this paper focused on classification and characterization of
soil using a hybrid approaches of computer vision and sensor network approaches.
2. LITERATURE REVIEW
Different researchers have been conducted their researches in application of computer vision
techniques related to agriculture. These are discussed as follow.
Ashok and Snehamoy, in their work entitled as “Computer vision-based limestone rock-type
classification using probabilistic neural network” presented Rock-type identification for lime stone mine. In
this paper, a computer vision based rock type classification system is proposed without human intervention
using probabilistic neural network (PNN). In this research paper the authors are used the color histogram
features as an input. In the paper the color image histogram based features includes weighted mean, skewness
and kurtosis features are extracted for all three color space red, green, and blue. In this paper, a total nine
features are used as input for the PNN classification model. Then they found out the error rate for
identification is below 6% [8]. Anastasia & Petros, presented soil image segmentation and texture analysis
using computer vision approach. The author proposed joint image segmentation methods for soil images and
feature measurements [9].
Sun–ok chung, et al., studied Soil Texture Classification Algorithm Using RGB Characteristics of
Soil Images. The authors found that soil texture has traditionally been determined in the laboratory using
pipette and hydrometer methods that require a considerable amount of time, labor, and expense. In this paper,
soil texture classification using RGB histograms was investigated to solve the above mentioned problem. In
this paper, when soils were classified using USDA soil texture classification, the laboratory method and
image processing method produced the same results for 48% of the samples [10]
Małgorzata and Piotr, in this research paper the authors have shown that detection of soil pore
structure using an image segmentation approach. In this study, a density based clustering method on
tomography sections of soil is considered [11].
Bhawna, et al., studied determination of Soil pH by using Digital Image Processing Technique. In
Agriculture sector the parameters like quantity and quality of product are the important measures from the
farmers’ point of view. Soil is recognized as one of the most valuable natural resource whose soil pH
property used to describe the degree of acidity or basicity which affects nutrient availability and ultimately
plant growth [12].
Ali M, et al., in this research paper, Image texture analysis and neural networks for characterization
of uniform soils are studied. Supervised back-propagation neural network is used for this study. The authors
have tested neural network with considerable accuracy [13].
Umesh K, et al., in this paper the authors presented “Testing of Agriculture Soil by Digital Image
Processing”. This paper helps to determine the amount of fertilizer and pH of soil that must be applied. 80
soil samples their pH value tested in government soil testing Lab are considered in this study. In their work,
when the software is tested the software gives 60-70% accuracy [14].

Int J Elec & Comp Eng ISSN: 2088-8708 
Soil Characterization and Classification: A Hybrid Approach of Computer …. (Abrham Debasu Mengistu)
991
A. V. Bilgili, et al., studied on wavelet Analysis of soil reflectance for the characterization of soil
properties. The authors have used Wavelet analysis, hyperspectral near-infrared (NIR) and mid-infrared
(MIR) reflectance spectra of soil material to characterize the given soil [15].
M. Barber, et al., in their work entitled as “A Novel method for 2-D Agricultural soil roughness
characterization based on a laser scanning technique” presented laser profiler the determination of
agricultural soil roughness. When tested with the RMS height S and correlation length L in 1 m x 0,3 m
parcels with a 20-30% error in heights and 1- 10% error in horizontal lengths [16].
Richard J. Flavel, et al., studied about the applications of image processing and analysis in plant root
systems in soil using imageJ plat form. The authors have used x-ray tomography 3D images[17].
Małgorzata Charytanowicz and Piotr Kulczycki, in their work entitled as, “An Image Analysis
Algorithm for Soil Structure Identification” the authors presented an image segmentation approach for
detecting the soil pore structures that have been studied by way of soil tomography sections. In this paper,
density-based clustering and nonparametric kernel estimation methods had been considered for this
study [18].
Masayuki Tamura and Weiping Li, studied detection of soil liquefaction areas in case of Kantou
region of Japan. In this paper, multi-temporal PALSAR coherence data is considered[19].
K. Srunitha and S. Padmavathi, studied the performance of SVM for soil classification using image
processing techniques. In this research paper, the authors stated that soil characteristics identification and
classification is very much important and helps to avoid agricultural product quantity loss. The authors have
used image acquisition, image preprocessing, feature extraction and classification. Texture and color feature
are considered for feature vector; texture feature are extracted using low pass filter and Gabor filter. Besides,
color features are extracted using HSV[20].
According to Mrutyunjaya R. Dharwad, et al., Moisture content in soil is one of the main component
which plays important role in yield of crops. In this paper the authors focused on software development for
soil moisture assessment. The main objective of the authors was to turn the manual process to a software
application using image processing technique. Image of the soil with different moisture content are collected
and preprocessed to remove the noise of source image. The authors have used color and texture feature vector
as an input in soil moisture assessment software [21].
Sanjay Kumawat, et al., in their work stated that the farmers are suffering from the lack of rains and
scarcity of wate. In this paper, the main objective was to provide an automatic irrigation system thereby
saving time, money & power of the farmer. In this work moisture sensors are considered anad installed on the
field. Whenever there is a change in water content of soil these sensors sense the change gives an interrupt
signal to the micro-controller For capturing the images, the phone camera is used and after processing the
captured image the PH value of the soil is determined and accordingly crops or plants are suggested that can
be grown in that field [22].
Nimisha Singh and Rana GillRetinal, studied on identification of Retinal disease In this paper, the
authors have proposed the segmentation and use machine learning approaches to detect the true retinal part in
addition they stated that preprocessing is done on the original image using Gamma Normalization which
helps to enhance the image that can gives detail information about the image then the segmentation is
performed on the Gamma Normalized image by Superpixel method. Finally feature generation must be done
and machine learning approach helps to extract true retinal area and 96% accuracy is achieved [23].
Heru Purnomo Ipung, Handayani Tjandrasa, in this paper the authors focused on an urban road
materials vision system using narrow band near infrared imaging. This paper proposed imaging indexes
evaluation from experiment results to identify those urban road materials. The proposed multi-spectral
imaging indexes were able to show the potential to classify the selected urban road materials, another
approach may need to clearly distinguish between concrete and aggregates [24].
3. RESEARCH METHODS
To collect the data set Canon EOS Digital and IP camera is used to capture the image directly, and
both video and offline images are included in order to have a good data set form all perspective. The data
contains noises because they were captured in uncontrolled environments. Having such types of data set, it
was very helpful to classify and characterize the given soil. This study carried out on Amhara and Oromia
regions of Ethiopia, located at northern and southern part of Ethiopia.
The total of 6 group of soil and each having 90 images are considered for this study. That is, form
these 540 images were record. In addition, each images size is 256 by 256 is taken. Once the data set
collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through
MATLAB, 2014. The other part is measuring the moisture level of soil using sensor. In this study, Gravity

 ISSN: 2088-8708
992
Analog Soil Moisture Sensor is considered because this sensor is easy to interface. Besides, ARDUINO UNO
is used to interface moisture sensor and sketch nov ARDUINO IDE is used to program moisture sensor.
4. SOIL CHARACTERIZATION AND CLASSIFICATION
Soil characterization and classification system consists of three basic parts: computer vision, sensor
and classification. The images of soil samples were captured in different areas of Ethiopia. Back-Propagation
Artificial nueral network was used for classification and characterization of images in to different classes as
shown in Figure 1.
Figure 1. Soil characterization & classification model
4.1. Computer Vision
The images of soil sample were collected in Gonder, Metema, Dejen and Addis Ababa areas of
Ethiopia. To have the same illumination and temperature images are recorded in both in the morning and
afternoon time. In this study, both offline captured and online captured images are considered this helps us to
enhance the computer vision system. After capturing the image the next step is enhancing the contrast of the
image and resizing the image to 256 by 256. The other step in this part is extracting representing features. In
this paper, hsvHist, autoCorrelogram color_moments, meanAmplitude, msEnergy, wavelet_moments are
extracted from the image and moistures are extracted from the sensors. Figure 2 shows the computer vision
prototype.
Figure 2. Computer vision prototype

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4.2. Sensor
Brett Robinson [25], pointed out that devices for measuring soil moisture. Besides, the difficulty of
interpreting soil moisture data, the main limitations to deploying soil moisture sensors in dryland grain
production are likely to be: (a) complexity, (b) cost, (c) uncertainty, (d) safety regulations, (e) installation
problems and (f) operating problems. The authors also pointed that Watermark sensors and tensiometers do
not work in dry soils, and can be excluded from High frequency, buried capacitance sensors (Sentek,
Decagon and Vegetronix) are the best but in this study Gravity Analog Soil Moisture Sensor For Arduino is
considered. Figure 3 shows the moisture sensor.
Figure 3. Moisture Sensor
4.3. Back-Propagation Artificial Neural Network
As shown in Figure 3 the network needs 7 inputs of the combined feature vectors of physical and
moisture of a given soil and 6 neurons in its output layer to classify soils. The hidden layer has 26 neurons.
This number was picked by trial and error methods, if the network has trouble of learning capabilities, and
then neurons can be added to this layer. There is a significant change when we increase the number of hidden
layers neurons until 21, 24 and 26 but there is no change when the number of hidden layer neurons increases
above 26. Each value from the input layer is duplicated and sent to all of the hidden nodes.
5. EXPERIMENT AND RESULTS
In this research, two different methods are used. Namely Computer vision and Sensor are used to
classify and characterize the given soil. To begin with, the physical and moisture level features are used for
both training and testing for BPNN (Back-Propagation Artificial Neural Network) as shown in Figure 4.
There are two basic phases of pattern classification. They are training and testing phases. In the training
phase, data is repeatedly presented to the classifier, in order to obtain a desired response. In testing phase, the
trained system is applied to data that it has never seen to check the performance of the classification. Hence,
we need to design the classifier by partitioning the total data set into training and testing data set. From the
total dataset of 540 images 70% was used to build training and the remaining 30% of the total was used for
testing data. The experiment was conducted for 10, 15, 20, 25 and 30 hidden neurons this help us to examine
the performance of the network. In BPNN, needs 7 inputs neurons of the combined feature vectors of
physical and moisture level features and 6 neurons in its output layer to classify soils to their corresponding
class. The hidden layer has 26 neurons. There is a significant change when we increase the number of hidden
layers neurons until 10, 15, 20, 25, and 30 but there is no change when the number of hidden layer neurons
increases above 26. As indicated in Figure 5, the result showed that there was 89.7% success for 26 hidden
neurons using the combined feature vector of physical and moisture level features. The aim of the research
paper is to classify and characterize soils using the hybrid approaches of computer vision and sensor network.
In this paper, computer vision and sensor network together with BPNN are used and the accuracy of the
system are presented, and the results of BPNN were discussed and promising results were obtained. The
computer vision and sensor network for the characterization and classification of soil can be further
investigated. The work can also be seen in depth and researched by the different machine learning
techniques, characteristics of its physical and chemical in connection to image techogy.

 ISSN: 2088-8708
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Figure 4. BPNN
Figure 5. Result of classifiers
Table 1 shows the comparison of work.
Table 1. Comparison of work
Author Name Methodology Findings
Pravat Kumar Shit, et al. ERDAS Imagine v8.5 and image
processing techniques
The paper focused on crack perdition and the
authors haven’t used any classification
techniques.
Małgorzata Charytanowicz
and Piotr Kulczycki
Complete Gradient Clustering
Algorithm for Soil Structure
Identification.
This paper presents an image segmentation
approach for detecting the soil pore structures
K. Srunitha and
S. Padmavathi
Image processing and SVM classifier
for classification of soils
The paper focused on the performance of SVM
in classification of soils as clay, loam, sandy,
peat. From the experiment 74.4 % accuracy is
achieved.
Ashok Kumar Patel,
Snehamoy Chatterjee
Image processing and Probabilistic
neural network (PNN)
The author focused on rock type identification
using PNN and form the experiment The result
shows that for the GGL rock type, there are
misclassification error of 16%.
Mrutyunjaya R. Dharwad,
et al.,
Digital images to estimate soil moisture
of six soils
The author consider HSV color space to
identify the moisture
Abrham Debasu and
Dagnachew Melesew
Soil classification and characterization
using computer vision and sensor
network approaches. In our research
paper six types of soil is considered
and BPNN is used to classify and
characterize the soil
The main finding on this research is physical
feature vector like texture and color is not
adequate to classify and characterize the
moisture levels. So as to increase the
performance sensor is used. From the
experiment 89.7 % accuracy is achieved

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ACKNOWLEDGMENTS
We gratefully acknowledge Bahir Dar University, Bahir Dar institute of Technology for funding,
generous assistance and valuable information provided to us by our institute.
REFERENCES
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processing techniques”, Springer, 2015.
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[8] Ashok Kumar Patel and Snehamoy Chatterjee, “Computer vision-based limestone rock-type classification using
probabilistic neural network”, Science Direct, 2016.
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Vision Approach”, IEEE Geoscience and Remote Sensing Letters, 2005.
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Algorithm Using RGB Characteristics of Soil Images”, Fukuoka 812–8581, Japan, 2012.
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systems in 3D soil using non-destructive analysis: Root1”, PLoS One, 2017.
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[19] Masayuki Tamura & Weiping Li, “Detection of soil liquefaction areas in the Kantou region using multi-temporal
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Processing”, IRJET, 2017.
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Classification using RBFN”, IJECE, 2016.
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Vision System”, IJECE, 2017
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research and Development Corporation”, 2010.

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