AGRICULTURAL ENGINEERING BASED ON SYSTEM
ANALYSIS AND ELECTRONIC IMAGING
Mohamed
Najeh Lakhoua
ENICathage,
University of Carthage, Tunisia
E-mail: MohamedNajeh.Lakhoua@enicarthage.rnu.tn
Jamel
Ben Salem
University
of Carthage, Tunisia
E-mail: bsj_jamel@yahoo.fr
Tahar
Battikh
University
of Tunis, Tunisia
E-mail: btahar@yahoo.com
Imed Jabri
University
of Tunis, Tunisia
E-mail: imedjabri@yahoo.com
Submission: 12/7/2020
Revision: 12/15/2020
Accept: 1/11/2021
ABSTRACT
In this paper, we present the need for system
analysis and electronic imaging for the study of Agricultural Engineering. In fact, the system analysis aims to
structure the analysis of agricultural systems. In fact, we present a
literature review of system analysis and the need for electronic imaging. Then,
two applications of Agricultural Engineering are presented. In fact, the basic
principle of the application of stock management of a grain weighing system is
presented. An application of the identification of the behavioral criteria of
dairy cattle through electronic imaging is presented. Finally, the different
results obtained from these applications are discussed.
Keywords: Agricultural
Engineering, system analysis, electronic imaging, weighing
system,
stock management, dairy cattle
1.
INTRODUCTION
The Agricultural Engineering
field is the engineering of agricultural production and
processing. It combines the disciplines
of mechanical, civil, electrical, food
science and chemical engineering principles with comprehension
of agricultural principles according to technological principles (Field, Solie & Roth, 2007). A key objective of
this discipline is to advance the efficiency and sustainability of agricultural
practices.
Agricultural
engineers may achieve tasks such as planning, supervising and managing the
building of dairy effluent
schemes, irrigation, drainage, flood water control systems,
performing environmental impact
assessments, agricultural product processing and interpret
research results and implement relevant practices (Brown, 1988).
The first use of Agricultural Engineering was the
introduction of irrigation in large level agriculture. The practice would not
develop until the industrial revolution.
With the augment of tractors and machines in the
industrial revolution, a new age in Agricultural Engineering began. Over the
course of the industrial revolution, mechanical harvesters and planters would
change field hands in most of the food and cash crop industries. In the 20th
century, with the rise in reliable engines were implemented to disperse
pesticides. The introduction of these engineering concepts into the field of
agriculture allowed for a huge boost in the productivity of crops, dubbed a
"second agricultural revolution" (DeForest, 2007).
The integration of an
automation and supervision of grain weighing is very central because of the
advantages that it presents in terms of clutter, and exploitation suppleness
(Bleux, 1998). The monitoring of the weighing system of grains contributes to
the development of the storage speed and by optimization in the time of the
grain silos handling (Lakhoua,
Battikh & Jabri, 2019; Lakhoua, 2019).
A variety of studies and reflections on the development
of the agricultural sector have stressed the importance of upgrading of farms.
Exploitation of automated production systems is a actuality especially in the
cattle breeders structured at the level of the treaty and complementation food
rooms, the use of ICT (information and communications technology) remains shy
in Tunisia (Lakhoua, 2013; Lakhoua, 2012).
Certainly, with the expansion of herds and the desire to
develop offspring, cows are more covered by bulls but are artificially
inseminated after their observation in heat (Jemmali,
2016; Lakhoua, 2009).
In computer science, digital image
processing is the use of a digital
computer to process digital images through
an algorithm. As a subcategory or field of digital signal
processing, digital image processing has many advantages over analog image
processing (Tillett, 1991). It allows a much
wider range of algorithms to be applied to the input data and can avoid
problems such as the build-up
of noise and distortion during processing. Since images are
defined over two dimensions (perhaps more) digital image processing may be
modeled in the form of multidimensional systems. The generation and
development of digital image processing are largely affected by three factors:
first, the development of computers; second, the development of mathematics;
third, the demand for a wide range of applications in environment, agriculture,
military, industry and medical science has increased (Zhen & Yang, 2011).
This paper can be heavily divided into six parts: after a
presentation of an introduction on Agricultural Engineering, we present the
system analysis and electronic imaging. In sections three and four, we present
two applications of system analysis and imaging in Agricultural Engineering
field. Finally, the last presents conclusion and future work.
2.
PRESENTATION OF SYSTEM ANALYSIS
The systemic approach, sometimes called systemic
analysis, represents a relative interdisciplinary field to the study of objects
in their complexity (Cavelery,
1994; Lakhoua, 2018a). It enables to present an object of study in its environment,
in its working, in its mechanisms and in what doesn’t appear while doing the
sum of its parts.
Among the techniques of system analysis (Lakhoua, 2018b), we mention: (1)
methods of analysis that enable to systematize and to canalize the various
perceptions, (2) specification languages possessing syntax and very definite
semantics, and (3) simulation languages.
According to the method and the tool applied, additional
parameters can be defined (Lakhoua
et al.,
2016) (Figure 1).
Figure1: Global diagram of a system
The model developed, want to be a complete one;
whenever, to validate this model, we applied it to the situation of the
agricultural systems. This choice was taken in thought the significance of
various management problems and the physical information (Lakhoua, 2012;
Lakhoua, 2008).
3.
STUDY OF A WEIGHING SYSTEM IN ORDER
TO MANAGE A GRAIN SILO
The analysis of the weighing system of a grain silo and
the study of the stock management is essential in view to develop the
existing system. This is why we present in this paper an example of Djebel
Djloud grain silo (near to Tunis) which is exploited by the SMCSA-GC (Société
Mutuelle Centrale des Services Agricoles- Grandes Cultures) and plays a
significant role in general in the agricultural sector of the country and in
particular in the grain domain (Lakhoua, 2018c).
In fact, the weighing activity is an essential activity
in the grain storage process. The device
to ensure weighing in the grain storage silo is unique, it is the bridge.
In fact, weighing devices can be categorized into two
categories: circuit scale and weighbridge (Lakhoua, Battikh & Jabri, 2019). These two devices are usually connected to an
interface operator for controlling the weighing process whose measurement
signal is provided by electronic stress gauge sensors and a printer to edit
weighing tickets. In the case of the circuit scale, the measurement signal is
provided via an Industrial Programmable.
The weighbridge of our grain silo is constituted of
electronic sensors. It enables to do the weighing activities in the entrance
and in the exit of grain trucks. This
weighing system is ordered by a measuring of load and it is currently connected
to a printer allowing the tickets management (Figure 2).
Figure 2: The weighbridge of the grain silo
Since the control and control parameters of a storage
device are diversified (weight, temperature, humidity, gas release, level
control, aeration...), weighing is the central activity because it controls the
flow in entry and exit. In this way, we present the study of the weighing
system.
The production management of grain stock consists of at
designing, conducting and supervising production and distribution systems (Lakhoua, Balti & Ettriki, 2013) (Figure 3).
Figure 3: The weighing engineering software
The grain silo
management adopted a pragmatic gait on the basis of a case study. The elaborate methodology,
the developed tools as well as the gotten results incite us to continue in the
objective to develop a strategy of performances assessment of a weighing system
of grains and help to the decision.
4.
IDENTIFICATION OF THE BEHAVIORAL
CRITERIA OF DAIRY CATTLE ON THE BASIS OF IMAGING
Various studies and reflections on the development of the
agricultural sector have stressed the importance of upgrading of farms.
Exploitation of automated production systems is a reality especially in the
cattle breeders structured at the level of the Treaty and complementation food
rooms, the use of ICT remains shy in Tunisia.
Indeed, with the expansion of herds and the desire to
improve offspring, cows are more covered by bulls but are artificially
inseminated after their observation in heat (Bouraoui et al., 2002).
The production of milk and meat of a milk cow is directly
related to their reproductive function. It is the birth of the newborn that
triggers the production of milk in this animal. This birth called calving is
direct function of the success of the artificial insemination following
accurate detection of the reentry period in heat of the cow in question.
However the heat at farms size detection is often
provided by workers and therefore prone to errors of observation. It is the
result of a multitude of factors related to the accuracy of the observation of
the signs of heat, to the effectiveness of artificial insemination as well as
the fertility of the seed. To have a calving every 12 months, 90% of the cows
must go into heat within 60 days after their calving. The interval between
calving and fertilising insemination must be on average between 50 and 60 days.
Milk production plays a key role in the agricultural
sector and in the Tunisian economy (Hammami, 2004). Since the 1990s, the State
encourages investment in the breeding of dairy cows to meet the growing demand for milk and its derivatives. This
encouragement enabled Tunisia to achieve self-sufficiency in milk since 1999.
Although the number of livestock has increased
significantly and that the existing races in Tunisia, through import, can have
very high yields, we're still far from the optimal production rate. This is due
mainly to a low detection rate of cows in heat (OEP, 2015).
Farming occupies, in Tunisia, the most important
agricultural production part. It represents between 32% and 37% of the value of
agricultural production with 769 thousand cattle heads, 7 million sheep, 1.5
million goats and 70 thousand units females of camelids.
The livestock sector plays an important socio-economic
role. He contributed 22% of permanent positions in agricultural activities. It
affects a total of 112 000 cattle producers, 274 000 herders of sheep and 2300
breeders of camels in addition to farms poultry and rabbits.
Three classes of farmers are met, small (75%), the means
and the ranchers. The latter (20%) are often advanced in the application of
modern breeding methods (parlours, cold to the farm, Artificial Insemination
and improved Genetics). As for the dairy cattle industry, it includes a total
of 235 milk collection centers and dairy processing units, 43.
Meat production is considered as a by-product of milk and
helps to supply a total of 183 slaughterhouses and 20 units of cuts of red
meat. It has a tendency to increase in cows of pure breed at the expense of
local and cross cows. In 2010, the purebred cows represented 51%. In 2015, they
accounted for 65 percent. The dairy cow in Tunisia has become more specialized.
In developed countries, several technological attempts
have been adopted to assist farmers better manage dairy cattle herds based in
particular on the use of pedometers. In Tunisia, the breeding of dairy cows
continues to pose problems due mainly to a bad heat detection and insemination
time opportune (50, 60 days after calving). This has led to calving intervals
exceeding 14 months.
It is in this context that this work was proposed to
identify the criteria of behavior of dairy cattle in a barn through the
application of systemic analysis and electronic imaging (Salem et al., 2006).
Figure 4: The current barn studied
Figure 5 presents faces detection by
the cascad object detector.
Figure 5: Faces detection by cascad object detector
The dairy cattle sector, like any other agricultural
sector in Tunisia, can only develop if appropriate technologies are adopted.
Information processing technologies generated in large barns and the
application of imaging technologies could strengthen and strengthen the
capacity of the agricultural sector in general and in particular the livestock
sector.
This work has taken a step forward in a vital area that
is agriculture and in particular cattle farming with a spirit of innovation.
This breeding that has made our work target is both modern is classic: modern
by its new barn and its automatic distribution of food as well as its milking
room and classic in the methods of observing cows in heat that depends on the
vigilance of workers. The latter is often lacking revealed by the results
obtained where only 17% of the intervals between successive heats are normal,
leading to a low heat detection rate (28%). Heifers did not pose detection problems due to their low numbers
5.
CONCLUSION
In
this paper we presented the need for system approach and electronic imaging for
the study of Agricultural Engineering.
Then,
we presented a study of two applications of Agricultural Engineering in
particular grain silo management and the use of imaging in dairy cattle.
Starting
from this study of the need for system analysis and imaging in Agricultural
Engineering presented in this paper, we will analysis and model many examples
of complex agricultural systems.
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