Lupine Publishers |Agriculture Open Access Journal
Abstract
The primary objective of agricultural production is to provide an
economical, sustainable and productive industry in plant and
animal production. For this purpose, alternative solutions are provided
to the problems that need solution or improvement and to
facilitate agriculture in various areas such as increasing productivity
and product quality, minimum input usage, food reliability,
protection of natural resources and environment in agricultural
production. In this study, the technologies which are successfully
applied in plant production and animal breeding were addressed by taking
into consideration the advances made especially in
recent years.
Keywords: Precision agriculture; Smart farming; Precision livestock farming; Autonomous tractor; Unmanned aerial vehicles
Introduction
The agricultural sector has been adversely affected by global
market instabilities, economic crisis, animal diseases and climate
changes in recent years. In addition, structural problems such as
the average size of farms not allowing adequate investments to
increase productivity, absence of large-piece agricultural lands, lack
of education, agricultural employment and population growth as
well as the emergence of alternative uses of agricultural products
such as biofuels cause inefficiencies [1]. Due to the rapid increase
in the world population and urbanization, agricultural land per
capita and natural resources such as water are decreasing due to
the decrease in agricultural areas. For this reason, it has become
necessary to increase productivity in agricultural production
through technological and genetic methods. Excessive use of
chemicals and fertilizers, during the intensive agricultural
practices made to increase efficiency, has caused problems such
as environmental pollution in soil and ground water and the loss
of the production power of the field over time. Today, increasing
product quality, minimum input usage, food reliability, protection
of natural resources, increased environmental awareness,
economic production and sustainable agriculture concepts have
become a priority, despite the previous goals of increased yield and
productivity.
As a result of the rapid developments in information technology
following the mechanization, automation, and control technologies
during the development period of agricultural production, today,
intelligent machines and production systems that control machines
have begun to take over traditional production methods. Information
technology consists of hardware, algorithms and software developed
for the management of the collection, processing, storage, transfer
and use of information processes. The implementation of present
knowledge and experiences in agriculture together with the
machine learning, deep learning, artificial intelligence, modeling
and simulation applications enabled the development of real-time
and automated expert systems, autonomous tractors or agricultural
machines and agricultural robotics applications.
Precision Agriculture
Precision agriculture technologies, combining with control,
electronics, computer and data base with the account data, present
an advanced system approach. Using global positioning system,
geographic information system, variable rate application and remote
sensing technologies, precision agriculture technologies, contrary
to common fixed-level application methods which are applied at
all same to whole land, use the variable-level application methods
(based on application of fertilizer and chemicals to each section to
its own needs, tillage at different levels, planting at different norms,
irrigation and drainage at different levels) determining land and
plant characteristics of small sections (soil moisture, nutrient level
of soil, soil structure, product requirements, yield, etc.). As a
result, Precision agriculture technologies are agricultural production
and
management methods whose targets are more economic and more
environmentally sensitive production [2].
Precision agriculture practices start with the acquisition of data
through the use of various sensors and remote sensing technologies
and continue with the determination of soil properties of the
production area through soil tests. All information such as yield
values, fertilizer and pesticide application norms, climatic data,
topographic data, weed density, disease status of the previous
production seasons are associated with their actual location in
the production area. Then, the applications to be done are decided
using appropriate hardware and software. And, it ends with the
application of variable-level practices in the field according to the
application form decided. In addition, variable rate application
systems and real-time product monitoring systems have been
developed as a result of the sensors and software developed
by the manufacturers of precision agricultural equipment and
technologies:
a) Increased production efficiency,
b) Improved product quality,
c) The use of more effective chemicals and other inputs,
d) Energy saving,
e) The soil and ground water protection.
In addition to the production of field crops, precision agriculture
technologies have been successfully applied in vineyards and
orchards, pasture and meadow management and in animal
production. Applications vary from tea industry in Tanzania and
Sri Lanka to sugar cane production in Brazil, rice in China, India
and Japan, grain and sugar beet production in Argentina, Australia,
Europe and the United States [3]. Although it is expressed using
different terms such as precision agriculture, precision farming,
smart farming, variable rate application, site specific farming, site
specific management, computer aided farming and prescription
farming, the term smart farming has become more widely used
recently.
Figure 1: A typical crop growing cycle in precision agriculture [5] modified [4].
The precision agriculture, or the knowledge-based management
of agricultural production systems, has emerged in the mid-1980s
as a method for implementing the right process at the right time
in the right place. The increased awareness of the variability in
soil and product conditions has been combined with emerging
technologies such as global navigation satellite systems, geographic
information systems, and microcomputers. In the beginning,
precision agriculture has been used to adapt the fertilizer
distribution to the variable soil conditions in the agricultural area.
Since then, additional applications have been developed, including
the automatic steering applications of agricultural vehicles,
autonomous machinery and processes, product monitoring, farm research and software for the general management of agricultural
production systems. A typical crop growing cycle in precision
agriculture is shown in Figure 1 [4].
Precision Livestock Farming
The first desired condition in animal production is breeding
races with higher meat and milk yield. Second one is to make sure
that the highest level of individual potential of animals is achieved
through an adequate and balanced nutrition. The third is to take
preventive health measures against diseases that cause the major
losses in animal production and to minimize the use of drugs with
the early detection of diseases and the necessary intervention
[5]. Precision livestock production practices have contributed
significantly to the solution of the problems experienced in animal
breeding and in increasing the desired yield and quality in meeting
the increasing animal food needs. Effective decisions are made by
using precision livestock production practices in animal production
and by monitoring individual animal conditions (amount of
mobility, water consumption, milk conductivity value, amount
of milk, etc.); necessary health measures are taken as soon as
possible with the early identification of negative changes in animal
health; and, sustainable and productive management is provided
by ensuring that the individual potential of the animals is utilized
at the highest level by making the herd management applications
accurate and timely [6].
Precision livestock production allows collecting data at
individual cow level as well as precision (individual) nutrition,
regular milk recording (yield and components), pedometer, pressure
plates, milk conductivity indicators, automatic oestrus detection,
body weight, temperature, lying behavior, ruminal pH, heart rate,
feeding behavior, blood analysis, respiratory rate, rumination time
and movement skill scoring using image analysis. In this way, it
minimizes drug (antibiotics) use and provides and proactive animal
health strategy through preventive health by focusing on health
and performance [7]. Benefits from precision animal production
technologies include increased efficiency, reduced cost, improved
product quality, minimized negative impacts on the environment
and improved animal health and welfare. These technologies are
likely to have a major impact on health, reproduction and quality
control [8]. Figure 2 shows the areas observed in dairy cattle in
precision livestock production.
Figure 2: The areas to monitor in dairy cattle in precision livestock production [9].
Figure 3: The tasks of the automated control systems for dairy farming [10].
Automatic control systems developed for dairy cattle farms
provide solutions to the following tasks (Figure 3) [10]:
a) Getting the current information about animals;
b) Fast access to the animal history;
c) Increasing the milk yield because of the preclinical disease
diagnosis;
d) Structure analysis of the herd and the animal physiological
condition;
e) Reducing veterinary medicine costs;
f) Detection of the breaches in the herd reproduction
technology;
g) Reducing the number of unpregnant animals and
increasing the calf’s productivity;
h) Increasing the feeding effectiveness;
i) Reducing work costs and the improvement of work
culture.
Autonomous Tractor
The concept of autonomous refers to the functions performed
by the tractor without any human intervention. The concept of
autonomous tractor and automatic steering should not be confused
with each other. A tractor with automatic steering requires an
operator for safety, avoiding unknown obstacles and performing
unspecified tasks. An autonomous tractor can operate without
the operator in overcoming the numerous uncertainties in the
agricultural environment. In autonomous tractors, the necessary
hardware and software are developed for obstacle avoidance,
localization and mapping in addition to determining algorithms,
models and methods for movement control. In order to implement
route planning and navigation for this purpose, it is necessary to
accurately estimate the position of the vehicle and to detect the
environment sensitively during the movement of the vehicle.
Figure 4: Safety sensor for an autonomous tractor [11].
Various equipment and systems are used to determine
the position of autonomous tractors, to set the desired route
correctly, and to map the obstacles and objects around correctly.
The information collected from the sensors should allow the
autonomous tractor to move safely. Since autonomous tractors
operate in outdoor and in diverse environments, errors may
occur due to inability to receive information from some of the
sensors or due to the errors in the information received. For this
reason, it is preferable to process the data from different sensors
together and unique advantages of the different types of sensors
are used together to obtain a more comprehensive perception
(Figure 4). In this way, the information from the sensors provide
more detailed information about the location, environment and
surrounding objects during the movement of the tractor. And, in
order to turn this information into useful information, advanced
decision mechanisms, utilizing applications such as image, audio
and video processing algorithms, neural networks, machine
learning, statistical data analysis, are used and the autonomous
tractor is operated successfully in this way. The equipment used in
autonomous tractors are listed below:
a) Radar Sensors
b) Laser Scanners,
c) Lidar,
d) GPS / Inertial Navigation System,
e) Ultrasonic Sensor,
f) Cameras.
When developing an autonomous tractor, combining a large
number of tasks to increase operational success will relatively
facilitate the task. These tasks include [12]:
a) Coordination: The coordination of multiple vehicles can
be done centrally. Each vehicle operates independently and does
not know necessary information about other vehicles, but it has its
own tasks to fulfill.
b) Solidarity: Solidarity refers to the awareness of multiple
vehicles, working in the same field, from each other and tasks of
the others. For example, if three vehicles carry out the same task,
such as clearing the same area from the weeds mechanically, then
each vehicle needs to know the rows in which other vehicles were
running before selecting a new row to begin. It would not make
sense to have two vehicles come to head-to-head at the same time.
Real-time communication is needed between the paired vehicles.
c) Cooperation: It refers to multiple vehicles sharing the
same task at the same time. Using multiple vehicles to pull a large
trailer that a vehicle cannot pull alone is an example of cooperation.
Agricultural Robots
Agricultural robots are classified as indoor and outdoor robots,
in general. Outdoor robots include GPS assisted steering systems,
meadow robots, pruning robots, spraying robots, seeding/planting
robots and silage robot. Indoor robots include harvesting robots,
milking robots and barn robots [13]. Autonomous agricultural
robots are now an alternative to tractors in the fields. Breeding
operations can be carried out by the fleets of autonomous
agricultural robots in the future, such as seed sowing, spraying,
fertilization and harvesting robots. Agricultural robots must
have some basic capabilities and the ability to support multiple
applications. A navigation system is required for safe and
autonomous navigation as a basic capability [14]. When different
applications of autonomous vehicles in agriculture have been
compared with conventional systems, it has been found that the
first three main groups of potential practical applications include
plant cultivation, plant care and selective harvesting [15].
In the last two decades, special sensors (machine vision, GPS,
RTK, laser-based devices and inertial devices), actuators (hydraulic
cylinders, linear and rotary electric motors) and electronic
equipment (embedded computers, industrial PC and PLC) have
integrated into numerous autonomous vehicles, especially the
agricultural robots. These semi-autonomous/autonomous systems
provide correct positioning and guidance in precision agricultural
tasks, when equipped with appropriate equipment (agricultural
tools or equipment) [16]. Field map can be generated by estimating
the location of the plants in the surrounding environment through
image processing and recorded data detected by sensors. The
position estimation of the robot can be done by a navigation system
or relative calculation of the movements of the robot. The distance
of the plants to the robot can also be detected by sensors or image
processing, and the calculated positions can be marked on a map
[17].
The Use of Unmanned Aerial Vehicles in Agriculture
Aerial vehicles that can operate through remote control or
autonomously with its own power system, and that can load and
unload payloads depending on the place of use are called Unmanned
Aerial Vehicles (UAV). There are two types of aerial vehicles,
including UAVs that can fly autonomously on a certain flight plan
and remote controlled drones. Although these vehicle names are
commonly used interchangeably, the term UAV is a general term
for all unmanned aerial vehicles, whether autonomous or remotecontrolled.
A typical UAV system consists of the aircraft, one or more
ground control stations and/or mission planning and control
stations, payload and data connection. In addition, many systems
include launch and recovery subsystems, aerial vehicle carriers and
other ground services and maintenance equipment. A very simple general-UAV system is shown in Figure 5. Being more complex
and having more parts than drone systems increase [18] the cost
of system installation of UAVs. In drone systems, however, drones
can be used immediately after purchasing drones together with
the apparatus without the need for any other costs. Due to the
lower cost of purchasing than the UAVs, their ease of use and their
capabilities, drones are preferred in agricultural applications.
Figure 5: Generic UAV system [18].
Drone systems provide fast and safe solutions and analysis
for numerous situations, particularly for military applications,
including natural disasters, monitoring of various sports activities,
traffic control, wildlife monitoring, and agricultural applications.
Therefore, drone systems are produced in different formats according
to their area of use. One of the most preferred applications of drone
systems is the four-rotor drone system known as the quadrotor
shown in Figure 6. Quadrotor, as the name suggests, is a general
term of the drone systems with four independent rotors. The most
important advantage of the quadrotor is its high maneuverability.
This superiority gives the quadrotor the capability of vertical takeoff
and landing in dangerous and confined spaces. Due to the highpower
consumption of four rotors of a quadrotor, it cannot perform
long-term flight duty. The capacity of the device can be increased by
increasing the number of rotors. Six-rotor hexacopters and eightrotor
octocopters are the examples of different forms of quadrotor
obtained by increasing the number of rotors [19].
Figure 6: Four-rotor drone system [20].
Increasing productivity and improving product quality in
agricultural production depends on good monitoring of the plants’
development process and taking the necessary actions at the most
appropriate time. Drone systems, which have a simple technical
structure and are easy to use, offer farmers an opportunity to make
plans in agricultural activities using their embedded sensors and
cameras, providing high quality and 3D images. Varies studies
are carried out with drone systems, such as product development
monitoring, plant species separation, crop harvest determination,
automatic harvest, drought, detecting diseases, agricultural pests,
etc., damage detection, fruit and vegetable and soil moisture
classification, field management, organization of agricultural
activities, and agricultural insurance [21].
Drone systems have 5 effective use areas in agriculture. These
are [22]:
a) Product status monitoring: Farmers can inspect their
growing products faster and more effectively with drones with
NDVI or NIR sensors.
b) Irrigation systems monitoring: Large enterprises are
able to monitor irrigation systems for the supply of water needed
for certain products such as corn, which are spread over large areas,
after having reached specified sizes.
c) Weed identification: Weed maps are generated by postprocessing
the flight images and NDVI sensor data. In this way,
farmers can easily distinguish between high density weeds growing
together with healthy plants.
d) Variable rate applications: Variable-rate maps are
rapidly and practically generated with the use of NDVI sensors in
drone systems, instead of using variable-rate application maps
prepared by ground-based or satellite images. In this way, it is
possible to increase the efficiency by decreasing fertilizer costs.
e) Herd management and monitoring: The amounts
and activity levels of free-bred ovine or bovine animals can be
monitored from above through a drone.
Conclusion
Agriculture is a vital industry due to its contribution to the
sustainability of lives of people, to national income and employment
and its provision of raw materials to other industries. Therefore,
the agricultural sector has a direct impact on all segments of the
society with its economic, social and environmental dimensions.
Economically, subjects such as increasing agricultural production
and farmer revenues, minimum use of production inputs,
improving marketing conditions, etc. are addressed. Socially, there
are topics such as food quality and safety, agricultural employment,
socio-economic sustainability of rural areas, animal welfare,
etc. And, environmental issues include biodiversity, protection
of wildlife, meadow-pasture, forests, underground and surface
waters, and soil resources. Utilizing the opportunities offered by
advanced technologies is becoming increasingly mandatory in
order to achieve high success in studies conducted on all these
comprehensive issues, due to the importance of the subjects and
difficulties involved.
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