=Paper=
{{Paper
|id=Vol-1830/Paper93
|storemode=property
|title=A Multilingual Translation System for Enhancing Agricultural e-Extension Services Delivery
|pdfUrl=https://ceur-ws.org/Vol-1830/Paper93.pdf
|volume=Vol-1830
|authors=Muhammad Bashir Abdullahi,Ibrahim Shehi Shehu,Yahaya Mohammed Sani
}}
==A Multilingual Translation System for Enhancing Agricultural e-Extension Services Delivery==
https://ceur-ws.org/Vol-1830/Paper93.pdf
International Conference on Information and Communication Technology and Its Applications
(ICTA 2016)
Federal University of Technology, Minna, Nigeria
November 28 – 30, 2016
A Multilingual Translation System for Enhancing Agricultural e-Extension Services
Delivery
Muhammad Bashir Abdullahi1, Ibrahim Shehi Shehu2, and Yahaya Mohammed Sani3
Department of Computer Science, Federal University of Technology, Minna, Nigeria
{1el.bashir02, 2ibrahim.shehu} @futminna.edu.ng, 3saniyahaya84@yahoo.com
Abstract—Agricultural extension is the application of new productivity [1]. More so, the various roles and contributions
knowledge and scientific research findings to agricultural that policy makers and extension workers continue to make
practices through farmer education. As a result, agricultural in the current food production cannot be down played.
extension agents or workers are people from government An effective agricultural extension depends on how fast
research institutes who educate or pass on information to and useful the extension services reach or meet the farmer’s
farmers on how to use the new knowledge and scientific information need. Thus, it is likely that the farmers benefit
research findings. However, the conventional method of from agricultural research findings with the eventual goal of
communicating the agricultural research outputs or findings to improving agricultural productivity.
farmers through only face-to-face meeting has many challenges
Unfortunately, in most developing countries today,
such as geographic dispersion between farmers and extension
workers, poor communication capacity, poor transportation
extension agents still depend largely on traditional extension
facilities, bad roads, inadequate funding and dialectical approaches of transmitting agricultural information to
problems, which create great problems to effective farmers. The traditional approaches of communication are
communication of agricultural information to farmers. classified as one-way multipurpose and two-way
Furthermore, this mode cannot adequately handle urgent (time multipurpose sources. The one-way multipurpose sources
bound) information that should circulate within the farming include: television, radio, public campaign, leaflet, pamphlet,
populace. In this paper, a multilingual translation system was newspapers and magazines. While the two-way multipurpose
developed to enhance agricultural e-extension services communication sources include: village fairs, field
delivery. The system employs a serial integration of rule-based demonstrations, trainings and study tours. Also included in
and statistical machine translation techniques to translate this category are: extension workers, private agencies, para-
agricultural information or scientific research findings from technicians, farm input dealers, non-governmental
the extension workers in English (source language) into organizations (NGOs), credit agencies, fellow progressive
farmer’s registered native or preferred language. Four (4) farmers, output buyers/food processors and primary
target languages were considered, which include Arabic, cooperative societies [2],[3],[4].
Hausa, Ibo and Yoruba. The system was implemented using These sources of transmitting agricultural information to
Per Hypertext Processor (PHP) language version 5.3.5 farmers are no longer effectual for urgent agricultural
and Structured Query Language (MySQL) version 5.0.2. research findings needed by farmers. However, the rising
The system integration test shows 65% accuracy in face up to farmers in this new millennium is how to manage
translating research outputs in English to farmer’s with the information explosion and global trend in agro-
registered native language. It is therefore recommended technology. There is the need therefore, for inter-related
that the implemented system be adopted for its efficiency and systems to diffuse information and technological innovations
accessibility in enhancing agricultural e-extension services to farming populace in developing countries [5].
delivery. In addition to the identified problems, farmers are even
more confronted with myriads of challenges now than ever
Keywords-language translation; agriculture; e-extension before in relation to information generation and
services; farmers; information system communication, which is as a result of lack of sufficient
agricultural knowledge and information that will enhance
I. INTRODUCTION farmer’s participation, collaboration, and integration in
Extension agents or workers are people who possess an agricultural decision making processes. Also, lack of
acceptable level of edification, engaged by government to investment from other agricultural stakeholders, for example,
bridge the fissure connecting the government and the farmers government agro-allied industries, and non-governmental
in terms of agricultural services. These groups of people organizations have lead to low output in both production and
educate or pass on information to farmers on how to use sustainability. It is also arguable that farmers’ illiteracy in
information derived from government research institutes. Information and Communication Technology (ICT) is so
Such information empowers farmers to take control over high in the rural areas as most of the farmers lack access to
decision-making processes and resources for increased agricultural technology, innovations and education. There
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
exists also (i) dialectical disparity in which the research 2) Statistical Machine Translation (SMT) Technique:
findings are communicated, (ii) near absence of training and The SMT is a corpus based approach, where translation is
re-training of both the extension workers and farmers on new generated on the basis of statistical models whose
innovations [6], and (iii) ineffective ICT policies that are parameters are derived from the analysis of bilingual text
targeted towards empowering farmers via the deployment of
corpora [9]. A massive parallel corpus is required for
ICT tools and even where they are available; there is poor
access and reception. training the SMT systems. The SMT systems are built based
Finally, the present understanding of Agricultural on two probabilistic models: language model and translation
extension is supporting people engaged in agricultural model [9]. The merit of SMT system is that linguistic
production by facilitating, empowering and linking them to knowledge is not a requisite for building the system. The
markets and other players in the agricultural value chain; to complexity in SMT system is creating massive parallel
obtain information, skills and technologies to solve their corpus.
problems [7]. 3) Hybrid Machine Translation (HMT) Technique:
The remainder of this paper is organized as follows: HMT was built owing to the drawbacks of the two
section 2 presents the related work. Section 3 describes the approaches and their prospect to be integrated [9]. Statistical
research methodology used. The results and discussion of the
and Rule-Based are two MT techniques, whose methods of
system testing and evaluation were presented in section 4.
Section 5 gives the concluding remarks. translation are orthogonal to one another. SMT do not need
to learn about the language at all, but RBMT is based on
gathering language rules. Due to this difference, integrating
or hybridizing SMT and RBMT gives a better performance.
II. RELATED WORK The hybrid technique can be used in a number of different
ways. In some cases, translations are performed in the first
A. Machine Translation stage using a rule-based approach followed by adjusting or
Machine Translation (MT) is one of the most essential correcting the output using statistical approach. In the other
applications of computational linguistics that uses the way, rules are used to pre-process the input data as well as
computer software or web application to translate text from post-process the statistical output of a statistical-based
one language to another. One of the benefits of machine translation. This technique is better than the previous two
translation is that it helps people to understand an unknown and has more power, flexibility, and control in translation
language without the aid of a human translator. However, [9],[10].
MT is often perceived as low quality based on outdated
perception created by its use of older translation technologies C. Review of Existing E-Extension Services Delivery
or freely available generic translation tools from Google or Systems
Bing that have not been customized for a specific purpose Kalna-Dubinyuk [11] developed an electronic extension
[8]. Many technology advances have been made in recent service in Ukraine using extension service model that links
years that are changing this perception with customized Ukraine’s extension service system and the outside world.
machine translation engines [8]. This system has developed the market economy in Ukraine
and moves forward in increasing the number of farmers and
B. Machine Language Translation Techniques the formation of new forms of agricultural business entities.
The major constraint of this system is that it only capitalizes
A few different types of machine translation are available on the market economy of agricultural produce and ignores
in the market today. According to [9] the most widely used agro-technology innovation and adaptation.
techniques include: Statistical Machine Translation (SMT), A mobile-based Agricultural Extension System was
Rule-Based Machine Translation (RBMT), and Hybrid developed by [12] in Tanzania called M-FAIS (that is
Machine Translation, which combine RBMT and SMT. Mobile Phone Farmers Advisory Information System). The
These techniques are briefly explained as follows [9]: system was designed using a GSM modem and Independent
1) Rule-Based Machine Translation (RBMT) Technique: Service Architecture (ISA). The research finding shows that
The RBMT relies on countless built-in linguistic rules and the M-FAIS allows farmers to get advice in various
millions of bilingual dictionaries for each language pair. The agricultural issues such as agronomic practices, livestock
RBMT system parses text and creates a transitional husbandry, post-harvest operations, veterinary services,
representation from which the text in the target language is forestry, financial and market support services. The system
generated. This process requires extensive lexicons with depends on third party software (Serial Splitter) for sending
morphological, syntactic, and semantic information, and and receiving short messaging service (SMS) operations.
This makes its use on public domain vulnerable to malicious
large sets of rules. The software uses these complex rule sets
attack.
and then transfers the grammatical structure of the source An implementation of e-extension system that uses
language into the target language. [9]. There are no human mobile phones for knowledge sharing was done by [13]. It
interventions during the conversion from one language to uses social network tools to share quick and instant
another language. Human intervention only takes place, if at information. It maximizes the use of information and
all, after translation to manually correct errors in the communication technology to attain a modernize aquaculture
machine translation output. sector. It also focuses on creating an electronic and
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
interactive bridge where farmers, fishers, and other editing, and statistical error checking. These modules are
stakeholders rally and transact to enhance productivity, explained as follows:
profitability and global competitiveness. The shortcoming of 1) Start: This is the beginning of the process.
this system is that not all farmers have internet access and 2) Input Text: When a source text/sentence (in English
ICT literacy to operate in the social media. language) of Agricultural Information (AI) is entered as
An ICT-based agricultural extension service delivery input, the following process ensued:
system was proposed for Nigeria by [14]. The system is to be
3) Deforming and Pre-Editing: This is a preprocessing
used in agriculture center(s) in village(s) where extension
service is required. The purpose is to stimulate farmers module. In this module, deforming is performed as a process
driven extension; by allowing farmers to request for in which the machine checks the part of the source AI
guidance and assistance based on their unique needs. text/sentence that does not require translation such as
However, it does not adopt the use of phone-based pictures, figures, diagrams and identifies only the portion of
application and not all farmers have access to internet in the the source text/sentence that can be translated. Similarly,
rural communities, even where the facilities are available. pre-editing involves fixing up the punctuation marks that
E-sagu was implemented by [15]. It is a web-based does not require translation. This is to make the machine
agricultural expert advice dissemination system, which language translation of the AI easier, faster and efficient.
farmers can use to send a digital photograph of their 4) Analysis: In this module, the source text of AI is
problems to an agricultural expert. The role of extension
analyzed based on the linguistic information provided to
agent is excluded on the system since the agricultural expert
has direct link with the farmers and knows about their produce a complete parsing of a source language sentence.
problems. The shortcoming of this system is however that of Thus, it comprises of two components: tagger and parser.
dialectical problem. a) Tagger: This component identifies the linguistic
Shrikant and Shinde [16] developed a web-based property of individual word of AI in the source text through
information and advisory system for agriculture using the following processes:
software engineering’s classic life cycle method. Classic life (i) Morphological Analysis: This aspect
cycle is also called linear sequential model and it is a widely determines the form of AI word such as
used paradigm for system development. The system provides number, tense or part of speech (POS) tagger.
farmers with relevant and updated crop information. The (ii) Syntactic Analysis: This determines whether
information it provides is restricted to crops. Information AI words are subject verb or object.
regarding Livestock, market and weather are not provided by
this system. In addition, not all farmers can have access to b) Parser: This component breaks AI words/sentence
web-based facilities, since in many rural communities, where into smaller elements, according to a set of linguistic rules
majority of the farmers reside, have no internet facilities. that describe its structure through semantic and contextual
E-agriculture framework was developed by [17]. The analysis which determines the proper interpretation of AI
framework proposes an implementation of an e-farming text/sentence from the result produced by syntactic analysis.
system that can be used in aiding sustainable agricultural This is achieved by using lexical and semantic analyzer
farming practices. The incorporation of IT into farming created by parser.
involves the integration of diverse technologies, with each 5) Transfer: In this module, the syntactic/semantic
capable of positively impacting the efficiency of farming structure of the AI source text is then moved in to the
activities, thereby, promoting sustainability in agricultural
syntactic/semantic structure of the AI target languages.
practices. This framework has overcome farmers’ literacy
level problems since the framework proposed is meant to 6) Generation: In this module, lexical transfer (the
compliment and replace the traditional extension services mapping of a source-language lexical item with an
delivery. The major limitation of this framework is that equivalent target-language item) occurs and mapping
language translation from source language of the agricultural dictionary entries into appropriate inflected forms to yield a
information to the target local language understood by the target-language equivalent term. This is achieved using
rural farmers is not taken in to account. Arabic lexicon, Hausa lexicon, Ibo lexicon and Yoruba
lexicon to ensure proper interpretation.
7) Reforming and Post-Editing: This is a post-
processing module. In this module, once the AI text is
III. RESEARCH METHODOLOGY
translated, the target text is reformed after post-editing. This
involves re-incorporation of non-translated portion of the
A. Multilingual Translation System
source AI to target text for quality and adequate target AI to
The proposed multilingual translation system is based on be disseminated to famers.
the hybrid machine translation technique, which is a serial 8) Statistical Error Checking: In this module, to ensure
integration of the rule-based and statistical machine
accurate grammatical matching of the target output
translation techniques. It is designed to translate source text
from English language into any of the four preferred target produced by the rule-based approach into its statistical
languages: Arabic, Hausa, Ibo and Yoruba. The operational approach equivalent. Translation error checking is done to
process of the multilingual translation system, as shown in ensure good quality translation of the target texts of the AI.
Fig 1, is divided into six modules: deforming and pre- But in a situation where the rule-based translation technique
editing, analysis, transfer, generation, reforming and post- does not correspond with its statistical machine translation
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
equivalent, a decision is taken to consider another set of with the agricultural extension agent, receive/read
linguistic rules starting through the analysis, transfer down agricultural information from their mobile phones and also
to the generation stage using the rule-based technique until send their comment/query/request to AEA.
the target text matches that of statistical based target text.
9) Output Text: The output target texts/sentences are the Start
proper and accurate equivalent translation of the source AI
text (English) in to target AI texts (that is Arabic, Hausa, Ibo Input text
or Yoruba) meant to reach the farmers according to their (that is
English)
registered target languages with the AEA.
10) Stop: This mark the end of the process. Deforming and Pre-editing
B. System Framework Design
Analysis
The proposed framework for a multilingual translation
system to enhance agricultural e-extension services delivery Tagger
- Morphological Analysis
is shown in Fig. 2. The system connects three major -Syntactic Analysis
stakeholders of an agricultural extension services namely the
farmer, researcher/expert and agricultural extension agent. Parser
Semantic and
The role of each stakeholder is explained as follows: Contextual Analysis
1) Researcher/Expert: This stakeholder provides critical
research output in response to specific needs of the farmers. Transfer:
The research output cut across different aspect of farming Re-Ordering Syntactic and
Semantic of source text
including crop farming, livestock farming and the rest.
Whenever a research request get to the research institute, it Generation
Syntactic and Semantic
is handled by an expert in the area requested who after his generation of target texts using
findings relays a feedback through extension agents to target languages lexicons
farmers. In this system, the researcher/expert login to the Arabic lexicon
system, read information/research request and communicate
research request and finding to the AEA. The medium of Hausa lexicon
communication between the researcher and AEA is through Yes
Ibo lexicon
the web and their language of communication is English.
2) Agricultural Extension Agent (AEA): This is a trained Yoruba lexicon
expert who serves as a link between the farmers, research
institute and farm input firms. They convey information
responsive to the requirement of any component in the Reforming and post-editing
system. The AEA usually pay regular visit to farmers,
interact with them in order to know their problems and
concerns and then send it to an expert requesting for a Statistical error checking using corpus to
check for language translation conformity
solution. The functions of the AEA in this system includes with the result obtained by rule-based approach
login, update of information, register and manage farmers,
send updates of agro information via SMS to farmers and Arabic Corpus
also send research request to researcher/expert. In addition,
Hausa Corpus
the AEA interact with researcher through the web-based
system and send agro information to farmers from the Ibo Corpus
system to their mobile phones (it could be ordinary phones
Yoruba Corpus
or android phones).
3) Farmers: This is the consumer of agricultural
information. This is the component that receives agricultural Error
information from research institutes and farm input firms
via agricultural extension agent relevant to their farming No
requirement(s). This is because timely delivery of Output target
agricultural information to farmers means empowerment to texts
them. The multilingual translation system for enhancing
agricultural e-extension services delivery is aimed to Stop
provide farmers with this timely information they required
based on their registered local languages (Arabic, Hausa, Figure 1. Flowchart for Multilingual Translation System
Ibo or Yoruba). The role of the farmers includes: registering
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
Farmers · Register with the AEA
· Request for agricultural
information
· Receive/Read agricultural information
Multilingual Translation
· Login Technique
System Environment
· Read agricultural research
request
· Update agricultural research
request or finding to the AEA
Web Interface
Web server database
Expert/Researcher
· Login
· Update personal profile
· Register/manage farmers
· Post updates on agricultural
research information via SMS
to farmers
· Send agricultural research
request to experts
Agricultural Extension Agent
(AEA)
Figure 2. Proposed system framework
MySQL database management software that store
C. System Framework Representation collection of information and organized them so that it
A three (3) tier model was adopted for the proposed can easily be accessed, managed, and updated. MySQL
multilingual translation system which consists of front-end command is used to insert, update, fetch and delete data in
(presentation tier), middle tier and back-end (data tier) as this system database.
explained below:
1) Presentation Tier: This is also known as the front IV. SYSTEM TESTING AND EVALUATION
end and at this level, information is presented to client
(i.e. Researcher or AEA) via browsers. This tier was A. System Integration Analysis
developed using Per Hypertext Processor (PHP) language
To ascertain the workability between unit functions of
version 5.3.5.
the implemented system (interoperability of function
2) Middle Tier: This tier is also known as server side.
between components), system integration testing was
It is used for processing request through the My Structure carried out. Four (4) test cases were tested hundred (100)
Query Language (MySQL). MySQL 5.0.2 was used. This times each. The test result shows how each component
is also where the language translation takes place. responded to an event that identifies specific functions of
3) Data Tier: This tier is also known as back-end. the design to whether or not the responses are as expected.
This is the data center for the system which uses the Integration testing analysis is shown in Table I.
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
Test case 01 shows that 80.3% SMS from AEA were Some questions were directed to the selected farmers,
sent and received while 19.7% were not. Test case 02 extension agents and experts and their responses were
shows that 74.6% of the web messages from AEA to collected instantly. Simple percentage method (SPM) was
experts and from experts to AEA were successful while used for the calculations as shown in Table II. Out of the
25.4% were not. forty eight (48) numbers of validation forms distributed
On test case 03, 73% experts and extension agent’s only forty three (43) were filled and returned.
login to the web application were authenticated by Results obtained from the evaluation of the usage of
granting access to the web application functions while the developed system shows that respondents believed the
27% was not. Test case 04 tested the accuracy of system will bridge the information gap amongst
translation from source language (research output in researchers, extension agents and farmer functionalities,
English) to farmer’s registered native language, 65% 41(95.3%) are satisfied that the linkage provided by the
translation was achieved to each of the target languages implemented system is adjudged by the respondents as the
while 35% were not. best ever, while 2(4.7%) thought otherwise. Meanwhile,
33(76.7%) are satisfied with usability and accessibility of
B. Evaluation and Acceptance Satisfaction Analysis the system for enhancing agricultural e-extension services
The evaluation of the implemented system was done in delivery while 10(23.2%) were unsatisfied.
order to validate what the research work proposes, and to 24(55.8%) were satisfied that the system provides
have a thorough understanding of how well it is working. outmost confidentiality on the researchers, extension
In other to ascertain the effectiveness, efficiency and agents and farmers information as well as security of the
capability of the implemented system for enhancing system. 19(44.2%) were not satisfied. Similarly,
agricultural e-extension services delivery using the 32(74.4%) believe the implemented system is effective
multilingual translation technique, the implemented and efficient in enhancing agricultural e-extension services
system was used as a pilot scheme with forty eight (48) delivery while 11(15.6%) were not satisfied.
respondents. That is thirty (30) farmers, ten (10) extension
agents and eight (8) experts or researchers were selected
within Suleja and Minna, Niger State, Nigeria.
TABLE II. RESULTS OF SYSTEM EVALUATION ANALYSIS
TABLE I. ANALYSIS OF INTEGRATION TEST OF THE
IMPLEMENTED SYSTEM S/N Question Response Response (in
(in number number and
Test Test event Description of Expected Result in and %) %)
case test result Percentage Yes/Satisfie No/Unsatisfie
d d
01 Farmers AEA sends Farmer’s 80.3% sent 1 Are you satisfied that the 41 (95.3% ) 2 (4.7% )
mobile phone research mobile and system has bridged the
receives SMS findings SMS phone received. communication gap
from AEA via the system receives 19.7% not among researcher,
to farmer’s research sent and extension agent and
mobile phone findings received farmers?
SMS
02 AEA sending Sending Send and 74.6% sent 2 Are you satisfied with the 33 (76.7% ) 10 (23.2%)
research research receive and user friendliness (easy to
request and request to web received. use) and accessibility of
receiving experts messages 25.4% not the system as adequate
research sent and for enhancing agricultural
information received e-extension services
and findings delivery?
via web
3 Are you satisfied with the 24(55.8% ) 19 (44.2% )
03 Users login to Authenticate Users Login was level of security and
web user (experts achieved confidentiality of
application and 73% to the farmers, extension agents
agents) web while and researchers
have 27% was information on the
access to not system?
web-based
system 4 Are you satisfied with the 32 (74.4%) 11 (15.6%)
efficiency and
04 System Research Research 65% effectiveness of the
translates output output translation system for enhancing
research translated from should be was agricultural e-extension
outputs to English to received achieved to services delivery?
farmer’s native farmers native in farmers each of the
language language registered target 5 Are you satisfied with the 34 (79.06%) 9 (20.9%)
native languages functionalities of the
language while 35% system?
failed
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
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