=Paper=
{{Paper
|id=Vol-3293/paper112
|storemode=property
|title=Integrated Soil Mapping System for Directed Soil Sampling
|pdfUrl=https://ceur-ws.org/Vol-3293/paper112.pdf
|volume=Vol-3293
|authors=Aristotelis C. Tagarakis,Lefteris Benos,Ioannis Menexes,Dimitrios Kateris,Giorgos Vasileiadis,Dionysis Bochtis
|dblpUrl=https://dblp.org/rec/conf/haicta/TagarakisBMKVB22
}}
==Integrated Soil Mapping System for Directed Soil Sampling==
https://ceur-ws.org/Vol-3293/paper112.pdf
Integrated Soil Mapping System for Directed Soil Sampling
Aristotelis C. Tagarakis 1, Lefteris Benos 1, Ioannis Menexes 1, Dimitrios Kateris 1, Giorgos
Vasileiadis 1,2 and Dionysis Bochtis 1
1
Institute for Bio-Economy and Agri-Technology (iBO), Centre of Research and Technology-Hellas (CERTH),
6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece
2
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124
Thessaloniki, Greece
Abstract
Soil properties vary spatially, thus, reducing the application efficiency of inputs like fertilizers
and irrigation, in case they are uniformly applied. Consequently, there is a need to develop
systems that can acquire information from in-field measurements and convert it into advice for
appropriate site-specific management. Toward that direction, the concept of an integrated soil
information management system is described in this paper. In particular, the proposed
information system receives data associated with soil properties, processes the available
information by fusing the data form various layers of information acquired in the field and
produces zone maps that determine the locations for targeted soil sampling. The measurements
concern the pH, electrical conductivity and compaction of soil and are conducted either by
sensors attached to an unmanned ground vehicle or manually by a user through exploiting the
developed Android mobile application to guide them in the field to the sampling points.
Keywords 1
Soil management, precision agriculture, integrated information system, site specific
management
1. Introduction
Nowadays, there is a plethora of challenges placing pressure on agriculture, including demographics,
climate change, and natural resources depletion. Toward addressing these challenges, it is urgently
necessary to increase the overall degree of efficiency of the farming practices by simultaneously
reducing their environmental burden. Precision agriculture is a fundamental ingredient of sustainable
agricultural systems that uses effective management strategies which exploit state-of-the-art
Information and Communication Technologies (ICT) [1]. It aims at customizing management for small
regions based on field variability, instead of managing the whole field as a single unit. The
accomplishment of the goals of precision agriculture strongly relies on applying accurate techniques
for determining the in-field soil properties, as soil constitutes one of the most vital aspects of agricultural
production with a dominant influence on crop growth, yield and quality [2]. The conventional approach
of grid soil sampling may no longer regarded adequate, since it can be time consuming and labor
intensive [3]. On the other hand, random sampling without taking into consideration the field variability
may be insufficient. Managing fields as a whole, can result in production inequality, which in some
parts of the field, may be as low as to lead the producer to financial losses. As a consequence, it is
essential to determine and analyse the specific characteristics of the soil across the field. This can be
the basis for the application of inputs in variable doses with the intention of maximizing the yield and
economic benefit in each part of the field, while minimizing the environmental footprint.
Proceedings of HAICTA 2022, September 22–25, 2022, Athens, Greece
EMAIL: a.tagarakis@certh.gr (A. 1); e.benos@certh,gr (A. 2); i.menexes@certh.gr (A. 3); d.kateris@certh.gr (A. 4); g.vasileiadis@certh.gr
(A. 5); d.bochtis@certh.gr (A. 6)
ORCID: 0000-0002-5731-9472 (A. 1); 0000-0003-2150-5166 (A. 2); 0000-0002-6075-8150 (A. 5); 0000-0002-7058-5986 (A. 6)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
547
Taking into advantage the advancement of new sensing technologies for agricultural applications,
field variability can be examined and managed accordingly by implementing the right treatment in the
right time and place. Nevertheless, the use of these sensors remains limited mainly owing to the
specialized knowledge which is necessary for both operation and analysis of the produced field data.
Hence, one of the main barriers in adopting precision agriculture technologies is the lack of automated
procedures for data analysis as well as for the fusion of the various layers of information received from
the fields. In this paper, the concept of an integrated Soil Information Management System (SIMS) is
presented, which receives soil properties and other information from a number of sensors, fuses the
gathered data and produces zone maps in order to determine the locations for targeted soil sampling
and for site specific management.
2. Infrastructure of SIMS
SIMS is based on a modular structure toward enabling adaptability and flexibility, while, by using
open standards it facilitates interoperability and data sharing. The suggested system incorporates
integrated information management in conformity with the system-of-systems (SoS) approach. The SoS
approach favors the interconnection of individual subsystems for sensing, decision-making and action
taking, into a single system providing to the producer all the required elements to apply the optimal
agricultural treatments [4].
As illustrated in Figure 1, SIMS consists of three main subsystems:
• The “Initial Data Acquisition” subsystem;
• The “Data Analysis & Decision Making” subsystem;
• The “Targeted Sampling” subsystem.
Figure 1: Graphical representation of the infrastructure of SIMS illustrating the three main subsystems
and the basic elements contained in each of them as well as a summary of the implemented
methodologies.
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2.1. Initial Data Acquisition Subsystem
The objective of the first subsystem is the acquisition of the initial data for the delineation of
sampling and management zones. The procedures include the mapping of field topography and soil
electrical conductivity. The examination of these parameters is of major importance in agriculture, as
they are related to soil properties affecting yield. In SIMS, soil mapping is made by utilizing high
precision geolocation device (RTK-GNSS) to produce Digital Terrain Models (DTMs). Further, a three-
dimensional (3D) point cloud is generated for 3D visualization of the topography and the various
volumes and objects in the field, using photogrammetry from an RTK-equipped Unmanned Aerial
Vehicle (UAV). Also, a soil conductivity mapping sensor is used to map the soil electrical conductivity
at different depths. The system can either be attached to a UGV or manually operated by a human.
2.2. Data Analysis and Decision-Making Subsystem
This subsystem is the heart of SIMS. It interacts with both the “Data Acquisition” and “Targeted
Sampling” subsystems and is responsible for data processing and fusion of all levels of information. In
particular, th is subsystem initially receives the data from the soil EC and landscape mapping, performs
the pre-processing and produces the 3D representation of the soil topography and the soil variability
maps. With the utilization of Fuzzy clustering algorithms, it performs the fusion of the data layers and
produces the zone maps based on which the soil sampling and measurement locations are defined (for
soil pH and compaction in this study). In addition, this subsystem generates sampling plans and routing
plans for the UGV. For manual sampling and measurement operations, the sampling plan is received
by a specifically designed Android application (app), supported by a mobile phone or tablet that directs
the farmer or worker in the field to the sampling points. In the same vein, the UGV requests and receives
the routing plan from the central information subsystem.
The aim of these operations is to optimize the efficiency of the process by taking the minimum
possible samples, which are as representative as possible. As shown in Figure 1, the data flow from the
“Data Analysis and Decision Making” subsystem to the “Targeted Sampling” subsystem is
bidirectional. This means that in addition to the aforementioned outputs, the "Data Analysis and
Decision Making" subsystem receives as feedback the results at the targeted sampling/measurement
locations (soil analysis results and pH and compaction measurements). Thus, the “Data Analysis and
Decision Making” subsystem converts the above information into advice for enabling variable rate
applications to manage within-field variability according to the needs of each location in the framework
of applying the concept of precision agriculture [5,6].
2.3. Targeted Sampling Subsystem
This subsystem deals with the targeted measurements of soil properties, namely pH, and soil
compaction (using soil penetrometer) and the targeted soil sampling. Parts of this subsystem may be
fully automated utilizing UGV, or manual, human operated.
2.3.1. Unmanned Ground Vehicle (Case Study)
The robotic platform being used in the development of this system is Thorvald (SAGA robotics,
Norway), which is a modular mobile agricultural robot both in terms of hardware and software [7]. In
the context of the present system, the UGV is equipped with a ZED 2 depth camera (Stereolabs., San
Francisco, CA, USA), a 3D laser scanner (Velodyne Lidar Inc., San Jose, CA, USA), an RTK-GPS
(S850 GNSS Receiver, Stonex Inc., Concord, NH, USA) and Inertial Measurement Units (IMU) (UM7
IMU, RedShift Labs, Studfield, Victoria, Australia). The above equipment is used to provide
information on the velocity, acceleration and position of the robotic system (with latitude and longitude
549
coordinates) for optimal robot localization, navigation and obstacle avoidance. The UGV along with
the implemented equipment is depicted in Figure 2a. The selection of the aforementioned sensors was
based on their capability to be connected to the ROS (Robot Operating System) [8], which is an open
source framework appropriate to robotic applications and is widely applied in various fields, including
agriculture [9]. In brief, the robotic system receives a file from the “Data Analysis and Decision
Making” subsystem with the routing plan. In order for the UGV to successfully maintain a safe distance
and speed from potential obstacles, including humans, the open source package "ROS Navigation
Stack" is used similar to previous studies [10]. A ROS node generates a time frame (tf) emitter from
the robot for detected obstacles and, according to the tf values, a velocity controller keeps a safe
distance. The measurements conducted by the robotic system in this case study are those of soil
compaction using a digital penetrometer (FALKER, Porto Alegre, Brazil) (Figure 2b) in target
locations, and soil ECa using the soil apparent electrical conductivity mapping sensor EM38-MK2
(Geonics Limited, Ontario, Canada) (Figure 2c). The corresponding sensors are fitted to the robotic
vehicle to fully automate the measurements.
(a) (b)
(c) (d)
Figure 2: The utilized (a) Unmanned ground vehicle, (b) Soil penetrometer, (c) Soil ECa mapping
sensor, and (d) Portable electronic pH meter, toward facilitating the measurements.
2.3.2. Human
Part of the “Targeted Sampling” subsystem include manual human-based operations, who, via an
Android app, installed on a mobile phone or tablet, can be guided in the field to the sampling points
determined by the "Data Analysis and Decision Making" subsystem. The objective is twofold; a) to
acquire the manually measured soil parameters such as pH directly from the given points and record
the measurements to the app. The SIMS app retrieves information from the SIMS information system
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and, via using the Google Maps and the device's GNSS, can direct the user to the sampling points.
Alternatively, it is possible to measure the electrical conductivity and soil compaction manually.
3. Conclusions
In conclusion, this work presents the concept of SIMS system as an integrated soil management
system operating based on the principles of digital agriculture. The procedures for directed
measurements of soil properties (pH, electrical conductivity and compaction) were also described in
the present paper. The system consists of three main subsystems; (a) "Initial Data Collection", (b) "Data
Analysis and Decision Making" and (c) "Targeted Sampling", which, in turn, are composed of
individual subsystems. The objective of the system is to function as an integrated decision support tool
to manage field variability aiming to maximize yield, by using state-of-the-art sensors as well as data
fusion and decision support algorithms.
4. Acknowledgements
This research was carried out as part of the project “Soil information management system – SIMS”
(Project code: ΚΜΡ6-0190726) under the framework of the Action “Investment Plans of Innovation”
of the Operational Program “Central Macedonia 2014-2020”, that is co-funded by the European
Regional Development Fund and Greece.
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