Computer based dissemination of agricultural
information, expert Systems and decision support systems
(DSS) play a pivotal role in sustainable agricultural
development. The adoption of these technologies requires
knowledge engineering in agriculture. Diversification in
application, spatio-temporal variation, and uncertainty in
environmental data pose a challenge for knowledge
engineering in agriculture. Wheat production management
decision in Pakistan requires acquisition of spatio temporal
information, capturing inherent uncertainty of climatic data
and processing information for possible solution to problems.
In this paper a frame work for engineering of knowledge base
and soft computing model for production management of
wheat crop is presented The frame work include an ontology
based knowledge representation scheme along with structured
rule based system for query processing. A soft computing
model for acquisition and processing of wheat production
information for decision support is presented along with
knowledge delivery through semantic web.
Mobile phone an instrument of disseminating requisite agricultural informat...eSAT Journals
Abstract Bangladesh is densely an over populated country where a large number of people (more than 60 percent) are still living their livelihood directly or indirectly through the agro based jobs. The environment is very much favorable for agriculture. The contribution of agricultural sector to GDP is 20.60% In this regard, if the initiatives are been taken to provide the right farmers with the right information by the right way at the right time within the least cost, the success will be definite. Decision making is a very crucial part in every activity to be performed in an excellent manner. Any system applied for getting information and knowledge for making decisions in any industry should deliver accurate, complete, concise information in time or on time. The information provided by the system must be in user-friendly form, easy to access, cost-effective and well protected from unauthorized accesses. And to ensure all these there is no alternative other than telecommunication and networking technologies. Different collaboration and communication tools are available to share information throughout the world to ensure improved decision making. And those tools are also playing an important role to disseminate agricultural information. Mobile phone is one of the most popular electronic media among the natives of this country for news and information. Mobile phones significantly reduce communication and information costs for the rural poor in developing countries. This not only provides new opportunities for rural farmers to obtain access to information on agricultural technologies, but also to use ICTs (Information and communication technologies) in agricultural extension systems. Since 2007, there has been a proliferation of mobile phone based applications and services in the agricultural sector, providing information on market prices, weather, transport and agricultural techniques via voice, short message service (SMS) and internet. Therefore, this paper attempts to analyze the contribution of mobile phones on the dissemination of agricultural information for the farmers for their agricultural enrichment with emphasis on analyzing quantitative data gathered in a survey using a structured questionnaire that was generated on the basis of my theoretical study. Findings indicate a positive curve towards the dependency on and contribution of mobile phones. Keywords: Agricultural Information, Mobile Phones in Bangladesh, Agricultural Information Service (AIS), Technology Adoption, E-agriculture.
DESIGN AND IMPLEMENTATION OF ONTOLOGY BASED ON SEMANTIC ANALYSIS FOR GIS APPL...cscpconf
The Agricultural Census information is a leading source of facts and figures about a country’s
agricultural development. Such information is used by many who provide services to farmers
and rural communities including federal, state and local governments, agribusinesses etc. Also
such information when integrated with other agricultural surveys and statistics can help in
monitoring progress towards the achievement of Millennium Development Goals (MDGs) of a
country. But such huge volumes of census data are available at various geo-spatial portals
either in proprietary formats like shape files, .dat files etc or in form of database tables, word
documents, PDF’s etc. In order to do analysis or to just see the progress of a particular area
such huge datasheets have to be scanned. This paper provides solutions to various problems
related to Geo-spatial Agricultural Census data in three aspects: (1) Storage / Organization of
census data using enhanced methods such as ontologies. (2) Visualization of data using Google Maps and Column Charts. (3) Analysis of data using interactive methods like Column Charts
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Mobile phone an instrument of disseminating requisite agricultural informat...eSAT Journals
Abstract Bangladesh is densely an over populated country where a large number of people (more than 60 percent) are still living their livelihood directly or indirectly through the agro based jobs. The environment is very much favorable for agriculture. The contribution of agricultural sector to GDP is 20.60% In this regard, if the initiatives are been taken to provide the right farmers with the right information by the right way at the right time within the least cost, the success will be definite. Decision making is a very crucial part in every activity to be performed in an excellent manner. Any system applied for getting information and knowledge for making decisions in any industry should deliver accurate, complete, concise information in time or on time. The information provided by the system must be in user-friendly form, easy to access, cost-effective and well protected from unauthorized accesses. And to ensure all these there is no alternative other than telecommunication and networking technologies. Different collaboration and communication tools are available to share information throughout the world to ensure improved decision making. And those tools are also playing an important role to disseminate agricultural information. Mobile phone is one of the most popular electronic media among the natives of this country for news and information. Mobile phones significantly reduce communication and information costs for the rural poor in developing countries. This not only provides new opportunities for rural farmers to obtain access to information on agricultural technologies, but also to use ICTs (Information and communication technologies) in agricultural extension systems. Since 2007, there has been a proliferation of mobile phone based applications and services in the agricultural sector, providing information on market prices, weather, transport and agricultural techniques via voice, short message service (SMS) and internet. Therefore, this paper attempts to analyze the contribution of mobile phones on the dissemination of agricultural information for the farmers for their agricultural enrichment with emphasis on analyzing quantitative data gathered in a survey using a structured questionnaire that was generated on the basis of my theoretical study. Findings indicate a positive curve towards the dependency on and contribution of mobile phones. Keywords: Agricultural Information, Mobile Phones in Bangladesh, Agricultural Information Service (AIS), Technology Adoption, E-agriculture.
DESIGN AND IMPLEMENTATION OF ONTOLOGY BASED ON SEMANTIC ANALYSIS FOR GIS APPL...cscpconf
The Agricultural Census information is a leading source of facts and figures about a country’s
agricultural development. Such information is used by many who provide services to farmers
and rural communities including federal, state and local governments, agribusinesses etc. Also
such information when integrated with other agricultural surveys and statistics can help in
monitoring progress towards the achievement of Millennium Development Goals (MDGs) of a
country. But such huge volumes of census data are available at various geo-spatial portals
either in proprietary formats like shape files, .dat files etc or in form of database tables, word
documents, PDF’s etc. In order to do analysis or to just see the progress of a particular area
such huge datasheets have to be scanned. This paper provides solutions to various problems
related to Geo-spatial Agricultural Census data in three aspects: (1) Storage / Organization of
census data using enhanced methods such as ontologies. (2) Visualization of data using Google Maps and Column Charts. (3) Analysis of data using interactive methods like Column Charts
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
Data mining is a process of extracting knowledge from a vast database using tools and techniques. Data
mining plays an important role in decision making on issues related to many real-time problems such as
business, education, agriculture etc. Data mining in agriculture helps the farmers to decide on crop yield ratio,
water resource management, pesticides management and fertilizer management. Nowadays, climatic change is
one of the challenging problems in agriculture which has a greater impact on productivity. Many
researchers have contributed in the field of agriculture data mining i) To predict crop productivity, ii) water
management, iii) air pollution using the naïve bias and decision tree algorithms. The Proposed work is to
predict the diseases due to Climatic changes and recommended pesticide for the disease. Decision tree
algorithm is used to develop a recommendation system which helps to the farmer in the usage of pesticide for
the incidence of crop diseases.
Technical Efficiency of Smallholder Sorghum Producers in West Hararghe Zone, ...Premier Publishers
This study was aimed at analyzing the technical efficiency of sorghum producing smallholder farmers in Chiro district. It was based on cross-sectional data of 130 sample sorghum producing households randomly selected. The estimated results of the Cobb-Douglas frontier model with inefficiency variables shows that the mean technical efficiency of the farmers in the production of sorghum is 78 percent. This implies that sorghum producers can reduce current level of input application by 22 percent given the existing technological level. The discrepancy ratio γ, which measures the relative deviation of output from the frontier level due to inefficiency, was about 84.6% and while the remaining 15.4% variation in output, was due to the effect of random noise. The estimated stochastic production frontier (SPF) model also indicates that Organic fertilizer, DAP fertilizer, Area, Labor and seed are significant determinants of sorghum production level. The estimated SPF model together with the inefficiency parameters shows that age, Frequency of extension contact, Household size, Slope, Fertility of soil and Livestock holding significantly determine the efficiency level of the farmers in sorghum production in the study area. Hence, emphasis should be given to improve the efficiency level of those less efficient farmers by adopting and using practices of relatively efficient farmers in the area so that they can be able to operate at the frontier. Beside this, a strategy of the government needs to be directed towards the above-mentioned determinants.
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Economic Efficiency Analysis of Smallholder Sorghum Producers in West Harargh...Premier Publishers
The study was aimed at analyzing the economic efficiency of sorghum producing smallholders in West Hareghe zone. It was based on cross-sectional data of 200 sample sorghum producing households randomly selected. The estimation of stochastic frontier production function indicated that labor, DAP fertilizer, area, seed and oxen power affects sorghum yield positively. The estimated results showed that the mean technical, allocative and economic efficiencies were 78.9%, 38.6% and 33.6% respectively which indicates the presence of inefficiency in sorghum production in the study area. Among factors hypothesized to determine the level of efficiencies, frequency of extension contact had positive relationship with technical efficiency and it was negatively related to both allocative and economic efficiencies, while soil fertility was also found to significantly influence technical efficiencies positively and experience has positive relationships with technical efficiency and allocative efficiency and slope significantly affects technical efficiency negatively. The result also indicated that cultivated land was among significant variables in determining technical efficiency and economic efficiency of farmers in the study area. Education was found to significantly determine allocative and economic efficiencies of farmers positively. The result indicated that there is a room to increase the efficiency of sorghum producers in the study area. Therefore, emphasis should be given to improve the efficiency level of those less efficient farmers by adopting and using the best practices of relatively efficient farmers.
Perception of Cattle Herdsmen on the use of Information and Communication Tec...AI Publications
The study was carried out to investigate the perception of cattle herdsmen on the use of information and communication technology in management practice in Akinyele local government of Oyo state. Multistage sampling procedure was used for the study. A total of 164 questionnaire were administered and 135questionnaires were retrieved. Data collected were subjected to statistical analysis using the frequency table, simple percentage, chi square and PPMC (Pearson product moment of correlation).97.8% and 2.2% respectively represent the percentage of male and female respondent. The ages of the respondents were 56years and above (3.7%), 46-55years (5.2%), 36-45years (43.7%), 26-35years (32.6%), 15-25years (14.8%). Majority of the respondents (45.2%) and (72.6%) get information from television and radio set respectively. The result obtained showed that majority of the respondents (63.0%) strongly agreed that the use of ICT promotes productivity while 33.3% of the respondent agreed. Also, 47.4% of the respondent agreed that perception of cattle rearing to the use of ICT for management practices promote farming, 40.7% of the respondent strongly agreed.From the research, respondents see poor power supply, economic barrier, level of education as major constraint.PPMC shows that there was significant relationship between constraints to the use of various ICT tools and perception of the herdsmen (r= 0.944, p=0.000), the level of perception of the herdsmen and the entire hypothesis tested were all significant. Effort should be made by government to improve ways by which the recommended ICT tools can get to the herdsmen so as to enhance their production.
This study aims to analyze the technical efficiency of sorghum production by smallholder farmers in Konso district, Southern Ethiopia using cross sectional data collected from a sample of 124 sorghum producing households. Individual levels of technical efficiency scores were estimated using the Cobb-Douglas functional form, which was specified to estimate the stochastic production frontier. The estimated stochastic production frontier model indicated that input variables such as land size, fertilizer (Urea and DAP), human labour, oxen power and chemicals (herbicides or pesticides) found to be important factors in increasing the level of sorghum output in the study area. The result further revealed significant differences in technical efficiency among sorghum producers in the study area. The discrepancy ratio, which measures the relative deviation of output from the frontier level due to inefficiency, was about 90%. The estimated mean levels of technical efficiency of the sample households was about 69%, which shows existence of a possibility to increase the level of sorghum output by about 31% by efficient use of the existing resources. Among the household specific socio-economic and institutional factors hypothesized to affect the level of technical inefficiency, age, education level, family size, off/non-farm activities, extension contact, livestock holding, plots distance and soil fertility status were found to be significant in determining the level of technical inefficiency of sorghum production in the study area. Hence, emphasis should be given to improve the efficiency level of those less efficient households by adopting the practices of relatively efficient households in the study area. Beside this, policies and strategies of the government should be directed towards the above mentioned determinants.
Abstract
Cotton is the important cash crop of Pakistan and a major source of foreign earnings. However cotton crop is
facing many problems, such as disease and pest attacks. One way to reduce losses caused by disease and pest
attack is the use integrated pest management (IPM) practices. Keeping in view the importance of this technique,
the present study analyzed the adoption of IPM along with estimation of risk involved in the adoption process.
To estimate the cotton yield, two types of production functions (one for adopter and other for non-adopters) were
estimated using the regression analysis. Then estimate of regression models was used further in risk analysis.
The results of non-adopters of IPM showed that cost of urea bags, cost of nitro-phosphate bags, cost of herbicide
and rainfall were -0.038, 0.00475, 0.301 and 0.164 respectively and all of these significant at 10 percent level.
For non-adopters of IPM the coefficient values of seed expenditure, temperature, humidity and spray cost were
0.0035, 0.026,-.0.00093 and 0.00027 respectively. The results of IPM adopters showed that coefficient of
temperature, seed expenditure, spray cost, urea cost and rainfall equal to 0.0305,0.100,0.0029,-.000213 and
0.894 respectively and significant at ten percent level. Coefficient values of cost of nitro-phosphate bags,
herbicide cost, humidity were 0.00035, 0.100.-0.000671 and -0.000445 respectively.
Keywords: Cotton, IPM, herbicide, evaluation, risk, Coefficient, Hyderabad.
Technical Efficiency in Teff (Eragrostis teff) Production: The Case of Smallh...Premier Publishers
The aim of this study was to determine the level of technical efficiency of smallholder teff producers and identify factors affecting technical efficiency of smallholder farmers in teff production of Jamma district, South Wollo Zone, Ethiopia. A three-stage sampling technique was employed to select 149 sample farmers. A Cobb-Douglas stochastic frontier production analysis approach with the inefficiency effect model was used to estimate technical efficiency and identify the determinants of efficiency of teff producing farmers. The maximum likelihood parameter estimates showed that teff output was positively and significantly influenced by area, fertilizer, labor and number of oxen. The mean levels of technical efficiency of the sample farmers were about 78%. This shows that there exists a possibility to increase the level of teff output by 22% through efficiently utilizing the existing resources. The estimated stochastic production frontier model together with the inefficiency parameters showed that, age, education, improved seed, training and credit were found to have negative and significant effect on technical inefficiency while farm size was found to have positive and significant effect on technical inefficiency of teff production. Hence, local government should provide necessary supports such as formal as well as informal education, training, credit, improved seed and timely supply of fertilizer.
Merauke as a food barn has been carried out since the days of the Dutch
administration, namely by making Merauke an Asia Pacific food warehouse. To
realize this target, the Dutch government conducted a program to move the
population known as colonization, namely by bringing residents from Java to be
moved to Merauke. After the independence period the Indonesian colonization
program was still held by the name of transmigration. These transmigrant farmers
then live side by side with local residents and transmit the ability to cultivate rice to
local residents in Merauke Regency who previously lived from gathering, shifting
fields, sago farming, fishing, hunting and farming with the method of lagging, finally
getting interested in learning to grow crops rice. The purpose of this study is to
analyze (1) the factors that influence rice farming production on transmigrant
Sources of Risk and Management Strategies among Farmers in Rice Post Harvest ...Agriculture Journal IJOEAR
— The study examined sources of risk and management strategies among farmers in rice post harvest management in Niger State. The research was undertaken in five Local Government Areas of Niger State, namely Katcha, Lavun, Paikoro, Shiroro and Wushishi. Data obtained for the research was achieved through questionnaires administered to 200 farmers selected using multi-stage sampling techniques. Descriptive statistics was used for data analysis. The study showed that rice post harvest management is carried out by subsistence farmer with average farm size of 2.7ha and are of active productive age of 31-50 years, who have 24 years farming experience in the rice post harvest management. The study revealed that farmers in the study area are affected by production risk, financial risk, human or personal risk, market or price risk and technological risk sources. The farmers have adopted prevention, mitigation and coping with risk as management strategies. Based on the findings the study recommended provision of credit facilities, rice post harvest machineries at subsidized rate, rural infrastructures, cooperative formation, use of extension officer and proper storage facilities.
This research work examines the impact of technology adoption on agricultural productivity. It considers the south-west region of Bangladesh as the study area. Since most of the farmers in the study area are engaged in rice production, this study surveys randomly selected 60 rice farmers in the IRRI season of year 2015 through using a questionnaire. This study finds sufficient variation in frequency and degree of technology adoption in agricultural practices among the surveyed farmers. The analysis results indicate that farmers are adopting high level of technology in seed variety and irrigation phases, medium level of technology in land preparation, pest management and fertilizer application phases and low level of technology in weeding and harvesting phases. There is a statistically significant difference in productivity between high and low degree technology adopters. Education and land holdings are the statistically significant variables in determining technology adoption level. According to the study findings, a one percent increase in technology adoption leads to increase in productivity by 0.22 percent, holding other factors constant, and this influence is statistically significant at one percent level. Therefore, this study concludes that there is a scope for further increase in productivity through planned manipulation of technology adoption level in different phases of agricultural production.
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
Data mining is a process of extracting knowledge from a vast database using tools and techniques. Data
mining plays an important role in decision making on issues related to many real-time problems such as
business, education, agriculture etc. Data mining in agriculture helps the farmers to decide on crop yield ratio,
water resource management, pesticides management and fertilizer management. Nowadays, climatic change is
one of the challenging problems in agriculture which has a greater impact on productivity. Many
researchers have contributed in the field of agriculture data mining i) To predict crop productivity, ii) water
management, iii) air pollution using the naïve bias and decision tree algorithms. The Proposed work is to
predict the diseases due to Climatic changes and recommended pesticide for the disease. Decision tree
algorithm is used to develop a recommendation system which helps to the farmer in the usage of pesticide for
the incidence of crop diseases.
Technical Efficiency of Smallholder Sorghum Producers in West Hararghe Zone, ...Premier Publishers
This study was aimed at analyzing the technical efficiency of sorghum producing smallholder farmers in Chiro district. It was based on cross-sectional data of 130 sample sorghum producing households randomly selected. The estimated results of the Cobb-Douglas frontier model with inefficiency variables shows that the mean technical efficiency of the farmers in the production of sorghum is 78 percent. This implies that sorghum producers can reduce current level of input application by 22 percent given the existing technological level. The discrepancy ratio γ, which measures the relative deviation of output from the frontier level due to inefficiency, was about 84.6% and while the remaining 15.4% variation in output, was due to the effect of random noise. The estimated stochastic production frontier (SPF) model also indicates that Organic fertilizer, DAP fertilizer, Area, Labor and seed are significant determinants of sorghum production level. The estimated SPF model together with the inefficiency parameters shows that age, Frequency of extension contact, Household size, Slope, Fertility of soil and Livestock holding significantly determine the efficiency level of the farmers in sorghum production in the study area. Hence, emphasis should be given to improve the efficiency level of those less efficient farmers by adopting and using practices of relatively efficient farmers in the area so that they can be able to operate at the frontier. Beside this, a strategy of the government needs to be directed towards the above-mentioned determinants.
IOSR Journal of Applied Physics (IOSR-JAP) is an open access international journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Economic Efficiency Analysis of Smallholder Sorghum Producers in West Harargh...Premier Publishers
The study was aimed at analyzing the economic efficiency of sorghum producing smallholders in West Hareghe zone. It was based on cross-sectional data of 200 sample sorghum producing households randomly selected. The estimation of stochastic frontier production function indicated that labor, DAP fertilizer, area, seed and oxen power affects sorghum yield positively. The estimated results showed that the mean technical, allocative and economic efficiencies were 78.9%, 38.6% and 33.6% respectively which indicates the presence of inefficiency in sorghum production in the study area. Among factors hypothesized to determine the level of efficiencies, frequency of extension contact had positive relationship with technical efficiency and it was negatively related to both allocative and economic efficiencies, while soil fertility was also found to significantly influence technical efficiencies positively and experience has positive relationships with technical efficiency and allocative efficiency and slope significantly affects technical efficiency negatively. The result also indicated that cultivated land was among significant variables in determining technical efficiency and economic efficiency of farmers in the study area. Education was found to significantly determine allocative and economic efficiencies of farmers positively. The result indicated that there is a room to increase the efficiency of sorghum producers in the study area. Therefore, emphasis should be given to improve the efficiency level of those less efficient farmers by adopting and using the best practices of relatively efficient farmers.
Perception of Cattle Herdsmen on the use of Information and Communication Tec...AI Publications
The study was carried out to investigate the perception of cattle herdsmen on the use of information and communication technology in management practice in Akinyele local government of Oyo state. Multistage sampling procedure was used for the study. A total of 164 questionnaire were administered and 135questionnaires were retrieved. Data collected were subjected to statistical analysis using the frequency table, simple percentage, chi square and PPMC (Pearson product moment of correlation).97.8% and 2.2% respectively represent the percentage of male and female respondent. The ages of the respondents were 56years and above (3.7%), 46-55years (5.2%), 36-45years (43.7%), 26-35years (32.6%), 15-25years (14.8%). Majority of the respondents (45.2%) and (72.6%) get information from television and radio set respectively. The result obtained showed that majority of the respondents (63.0%) strongly agreed that the use of ICT promotes productivity while 33.3% of the respondent agreed. Also, 47.4% of the respondent agreed that perception of cattle rearing to the use of ICT for management practices promote farming, 40.7% of the respondent strongly agreed.From the research, respondents see poor power supply, economic barrier, level of education as major constraint.PPMC shows that there was significant relationship between constraints to the use of various ICT tools and perception of the herdsmen (r= 0.944, p=0.000), the level of perception of the herdsmen and the entire hypothesis tested were all significant. Effort should be made by government to improve ways by which the recommended ICT tools can get to the herdsmen so as to enhance their production.
This study aims to analyze the technical efficiency of sorghum production by smallholder farmers in Konso district, Southern Ethiopia using cross sectional data collected from a sample of 124 sorghum producing households. Individual levels of technical efficiency scores were estimated using the Cobb-Douglas functional form, which was specified to estimate the stochastic production frontier. The estimated stochastic production frontier model indicated that input variables such as land size, fertilizer (Urea and DAP), human labour, oxen power and chemicals (herbicides or pesticides) found to be important factors in increasing the level of sorghum output in the study area. The result further revealed significant differences in technical efficiency among sorghum producers in the study area. The discrepancy ratio, which measures the relative deviation of output from the frontier level due to inefficiency, was about 90%. The estimated mean levels of technical efficiency of the sample households was about 69%, which shows existence of a possibility to increase the level of sorghum output by about 31% by efficient use of the existing resources. Among the household specific socio-economic and institutional factors hypothesized to affect the level of technical inefficiency, age, education level, family size, off/non-farm activities, extension contact, livestock holding, plots distance and soil fertility status were found to be significant in determining the level of technical inefficiency of sorghum production in the study area. Hence, emphasis should be given to improve the efficiency level of those less efficient households by adopting the practices of relatively efficient households in the study area. Beside this, policies and strategies of the government should be directed towards the above mentioned determinants.
Abstract
Cotton is the important cash crop of Pakistan and a major source of foreign earnings. However cotton crop is
facing many problems, such as disease and pest attacks. One way to reduce losses caused by disease and pest
attack is the use integrated pest management (IPM) practices. Keeping in view the importance of this technique,
the present study analyzed the adoption of IPM along with estimation of risk involved in the adoption process.
To estimate the cotton yield, two types of production functions (one for adopter and other for non-adopters) were
estimated using the regression analysis. Then estimate of regression models was used further in risk analysis.
The results of non-adopters of IPM showed that cost of urea bags, cost of nitro-phosphate bags, cost of herbicide
and rainfall were -0.038, 0.00475, 0.301 and 0.164 respectively and all of these significant at 10 percent level.
For non-adopters of IPM the coefficient values of seed expenditure, temperature, humidity and spray cost were
0.0035, 0.026,-.0.00093 and 0.00027 respectively. The results of IPM adopters showed that coefficient of
temperature, seed expenditure, spray cost, urea cost and rainfall equal to 0.0305,0.100,0.0029,-.000213 and
0.894 respectively and significant at ten percent level. Coefficient values of cost of nitro-phosphate bags,
herbicide cost, humidity were 0.00035, 0.100.-0.000671 and -0.000445 respectively.
Keywords: Cotton, IPM, herbicide, evaluation, risk, Coefficient, Hyderabad.
Technical Efficiency in Teff (Eragrostis teff) Production: The Case of Smallh...Premier Publishers
The aim of this study was to determine the level of technical efficiency of smallholder teff producers and identify factors affecting technical efficiency of smallholder farmers in teff production of Jamma district, South Wollo Zone, Ethiopia. A three-stage sampling technique was employed to select 149 sample farmers. A Cobb-Douglas stochastic frontier production analysis approach with the inefficiency effect model was used to estimate technical efficiency and identify the determinants of efficiency of teff producing farmers. The maximum likelihood parameter estimates showed that teff output was positively and significantly influenced by area, fertilizer, labor and number of oxen. The mean levels of technical efficiency of the sample farmers were about 78%. This shows that there exists a possibility to increase the level of teff output by 22% through efficiently utilizing the existing resources. The estimated stochastic production frontier model together with the inefficiency parameters showed that, age, education, improved seed, training and credit were found to have negative and significant effect on technical inefficiency while farm size was found to have positive and significant effect on technical inefficiency of teff production. Hence, local government should provide necessary supports such as formal as well as informal education, training, credit, improved seed and timely supply of fertilizer.
Merauke as a food barn has been carried out since the days of the Dutch
administration, namely by making Merauke an Asia Pacific food warehouse. To
realize this target, the Dutch government conducted a program to move the
population known as colonization, namely by bringing residents from Java to be
moved to Merauke. After the independence period the Indonesian colonization
program was still held by the name of transmigration. These transmigrant farmers
then live side by side with local residents and transmit the ability to cultivate rice to
local residents in Merauke Regency who previously lived from gathering, shifting
fields, sago farming, fishing, hunting and farming with the method of lagging, finally
getting interested in learning to grow crops rice. The purpose of this study is to
analyze (1) the factors that influence rice farming production on transmigrant
Sources of Risk and Management Strategies among Farmers in Rice Post Harvest ...Agriculture Journal IJOEAR
— The study examined sources of risk and management strategies among farmers in rice post harvest management in Niger State. The research was undertaken in five Local Government Areas of Niger State, namely Katcha, Lavun, Paikoro, Shiroro and Wushishi. Data obtained for the research was achieved through questionnaires administered to 200 farmers selected using multi-stage sampling techniques. Descriptive statistics was used for data analysis. The study showed that rice post harvest management is carried out by subsistence farmer with average farm size of 2.7ha and are of active productive age of 31-50 years, who have 24 years farming experience in the rice post harvest management. The study revealed that farmers in the study area are affected by production risk, financial risk, human or personal risk, market or price risk and technological risk sources. The farmers have adopted prevention, mitigation and coping with risk as management strategies. Based on the findings the study recommended provision of credit facilities, rice post harvest machineries at subsidized rate, rural infrastructures, cooperative formation, use of extension officer and proper storage facilities.
This research work examines the impact of technology adoption on agricultural productivity. It considers the south-west region of Bangladesh as the study area. Since most of the farmers in the study area are engaged in rice production, this study surveys randomly selected 60 rice farmers in the IRRI season of year 2015 through using a questionnaire. This study finds sufficient variation in frequency and degree of technology adoption in agricultural practices among the surveyed farmers. The analysis results indicate that farmers are adopting high level of technology in seed variety and irrigation phases, medium level of technology in land preparation, pest management and fertilizer application phases and low level of technology in weeding and harvesting phases. There is a statistically significant difference in productivity between high and low degree technology adopters. Education and land holdings are the statistically significant variables in determining technology adoption level. According to the study findings, a one percent increase in technology adoption leads to increase in productivity by 0.22 percent, holding other factors constant, and this influence is statistically significant at one percent level. Therefore, this study concludes that there is a scope for further increase in productivity through planned manipulation of technology adoption level in different phases of agricultural production.
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many
sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in
reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and
conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior
weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly
and sustainably. In agriculture, it is the use of current information and
communication technologies.
PROBLEM:
Smart farming is a new concept in the field of agriculture with its complex mechanisms, fresh-coined terms, usage statistics and analytics, and its implementations differ from country to country. There is a shortage of structured information on this, especially, analytical research on comparison the countries’ past and current performance and future-expected gains on the field.
OBJECTIVES:
This paper’s mission is to familiarize the students with the mechanisms, terms, statistics, analytical research data and to do the comparison of the different scenarios of Smart Farming’s implementation in Germany and Uzbekistan.
APPROACHES:
Introducing interconnected technology fields that smart farming strongly related to:
- Farm Management Information Systems
- Precision Agriculture
- Agricultural automation and robotics
Comparing the current and future expected state of the SMART FARMING technology in Uzbekistan and Germany.
Agriculture in Indian Economy and Contribution of Science and Technology IIJSRJournal
One of the oldest occupations in history, agriculture has benefited much from innovation throughout the years. Since then, science has played a significant role in agricultural innovation and quality assurance. We have listed a few of the factors that were mentioned in the introduction section if you'd want to understand more about the significance of science and technology in agriculture. Encouraging the use of science and technology is the cornerstone for improving agriculture's productivity, quality, efficiency, and competitiveness, which also contributes to the modernization of agriculture and rural areas, ensures food security, social security, and increases the income of agricultural producers and traders.
Modern farming has gone through a massive upgrade due to the evolution of the latest technologies. The tech innovations have given a complete overhaul to the agriculture industry.
This post highlights the most recent innovation in Agriculture that are likely to dominate in the future.
Read here for more details!
Agricultural Informatics is a valuable domain in the field of interdisciplinary sciences. This is responsible for the applications of Information Technology, Computing and similar technologies into the agricultural activities. This is the combination of Agricultural Science and Information Sciences. The field due to technological nature is much closed with the Agricultural Engineering or Agricultural Technology. There are many allied and similar nomenclature of the fields but all of these are primarily responsible for the same purpose. The field is rapidly increasing in recent past and most practiced in the developed nation. However, in developing countries as well Agricultural Informatics becomes an emerging field of practice and growing rapidly. Agricultural Informatics is growing both in pre and post agricultural activity. This branch is considered as branch of Information Sciences & Technology due to its technological applications in the field of agriculture and allied areas. Information Sciences are the broadest field within the allied branches and growing rapidly. Agricultural Informatics educational programs have started in recent past in different level and stream of education viz. science and technology. However within the broad periphery of Information Sciences it could be offered in other streams and under the wide variety of Information Sciences. This paper is broad and interdisciplinary in nature and deals with the aspects of the Information Sciences and Technology including features, nature, scope and also the potentialities in respect of Agricultural Informatics.
Identification of recorded information is the most important requirement for developing an effective traceability system. This paper aimed to develop an information technology (IT)-based traceability system combined with an intelligent system in potato chips agro-industry. In this paper, we present a business process in potato chips agro-industry which consists of three activities, i.e. raw materials receiving, processing, and warehousing of final products. First of all, a traceability system architecture was developed. To develop computational models, quality, food safety and environment criteria using fuzzy inference system (FIS) and adaptive neuro fuzzy inference system (ANFIS), and intelligent decision support system (IDSS) traceability model using android front end. An internal information capture point was identified for each step and corresponding traceability information to be recorded was determined. Furthermore, a prototype IDSS was developed to represent method of information modeling on products, processes, quality, and transformation at each node in potato chips
agro-industry. In this study, intelligent systems have been developed for decision making in quality control good agricultural practices.
ISSN 2321 – 9602
It seems like you're describing the publication process of a journal or publication called . This information provides insight into the journal's commitment to a fast publication schedule while maintaining rigorous peer review of the journalism research paper.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
Information and Communication Technology in dissemination of Agricultural Tec...Lokesh Waran
Information and Communication Technology in dissemination of Agricultural Technologies
Dr.J.Meenambigai
Associate Professor
Department of agricultural Extension
Faculty of Agriculture
Annamalai University
Chidambaram
23 9150 survey of ict knowledge based agriculture dev edit septianIAESIJEECS
E-agriculture gives to applying new things to use ICTs in the country, with the main heart on agricultural. ICT in Agriculture provides a wide range of solutions to some farming ideas. The rising field focuses on the development of agricultural and rural advance through improved information and communication. This time, ICT is used as around all information and communication developments including Android mobiles, IOT devices, communication networking devices, web services; this variety from original Internet-era technologies and sensors to other pre-accessible aids such as TV, satellites, and radios. This technique continues to evolve in scope as new ICT applications continue to be harnessed in the agriculture industries. It involves the concept, development, design, application, and evaluation of novel ways to use ICTs in the rural domain, with the main focus on cultivation. This includes principles, norms, methods, and apparatus as well as the growth of personality and institutional capacity, and policy hold is all key mechanism of e-agriculture.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Knowledge Engineering in Agriculture: A Case Study of Soft Computing Model for Wheat Production Management
1. Knowledge Engineering in Agriculture:
A Case Study of Soft Computing Model for Wheat
Production Management
S. M. Aqil Burney1
Jawed Naseem2
College of Computer Science SSITU, SARC
& Information System Pakistan Agriculture Research Council
IoBM University, Karachi, Pakistan Karachi, Pakistan
aqil.burney@iobm.edu.pk .I
Abstract: Computer based dissemination of agricultural
information, expert Systems and decision support systems
(DSS) play a pivotal role in sustainable agricultural
development. The adoption of these technologies requires
knowledge engineering in agriculture. Diversification in
application, spatio-temporal variation, and uncertainty in
environmental data pose a challenge for knowledge
engineering in agriculture. Wheat production management
decision in Pakistan requires acquisition of spatio temporal
information, capturing inherent uncertainty of climatic data
and processing information for possible solution to problems.
In this paper a frame work for engineering of knowledge base
and soft computing model for production management of
wheat crop is presented The frame work include an ontology
based knowledge representation scheme along with structured
rule based system for query processing. A soft computing
model for acquisition and processing of wheat production
information for decision support is presented along with
knowledge delivery through semantic web.
Key Words: Ontology, Knowledge Engineering,
Agriculture, Semantic Web, Rule Based System
I. INTRODUCTION
Knowledge Engineering [Darai, 2010] is the
aspect of system engineering which addresses solution
of problems in uncertain process by emphasizing the
acquisition of knowledge and representing it in a
Knowledge-based System. KE is defined by Edward
Feigenbaum and Pamela McCorduck (1983) as an
engineering discipline that involves integrating
knowledge into computer systems in order to solve
complex problems normally requiring a high level of
human expertise. Knowledge engineering is a field
within artificial intelligence that develops knowledge-
based systems. Such systems are computer programs
that contain large amounts of knowledge, rules and
reasoning mechanisms to provide solutions to real
world problems.
Artificial Intelligence (AI) is the area of
computer science which focuses on developing
machines and computer systems requiring intelligence
like humans being. Using AI techniques and methods
researchers are creating systems which can mimic
human expertise in any field of science. Application of
AI ranges from creating robots to soft computing
models (softbot) that can reason like human expert and
suggest solutions to real life problems. AI can be used
for reasoning on the basis of incomplete and uncertain
information and delivering predictive knowledge.
Sustainable agricultural development requires
adaptation and incorporation of newly developed
technology to enhance agricultural production [Khan,
2010]. The technology may involve development of
new varieties, agricultural production management,
water management or crop protection. Adaptation of
these technologies involves continuous updating of
knowledge regarding a particular technology and
processing of information by expert to deliver
appropriate solution to problem in agriculture
[Hoogenboom 1999]. High level human expertise in
agriculture, like other disciplines of science, are not
only scares but also costly. Beside this expert
knowledge in agriculture require mass dissemination of
information to large audience of end-users including
policy makers, researcher, extension people and
ultimately to farmer. Conventional means of
communication of agricultural information have limited
scope of knowledge acquisition, processing and instant
delivery to end-user [Khan, 2010]. Computer based
systems and soft computing models can provide an
effective and efficient knowledge management system.
[Kolhe 2011]. Knowledge engineering in domain of
agriculture comprises three basic component,
Knowledge acquisition & representation, information
processing and delivering of possible solution.
Ontology is an effective way of knowledge
representation in domain of application [Burney and
Nadeem 2012]
In this paper a case study for knowledge
engineering of wheat production management decision
support system is discussed. Section II discuss
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
56 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
2. challenges and issues in wheat production management,
Section III describes mechanism for knowledge
representation of wheat production management
information. Section VI discuss mapping
production technology information into a knowledge
base, Section V present soft computing model and
section VI discuss future work.
II DECISION MAKING
PRODUCTION MANAGEMENT
Wheat production management requires decision
making on several factors from pre
harvesting based on different parameters
Wheat production technology
temporal variation. In Pakistan wheat varieties are
developed which are suitable
ecological zones and have varied
parameters. The main parameters are
appropriate variety, agronomic practices (
seed rate etc), and irrigation management during
cultivation. The yield of crop is not only affected by
these factors but management of disease
subject to environmental conditions contribute to
growth of plant. Capturing spatio-temporal variation in
wheat production technology is a challenge in
development of wheat production knowledge base.
technological information varies depending upon
various agricultural zones (spatial) along with time of
adoption of the technology (temporal)
disease monitoring is another essential component in
wheat plant growth. Diagnosis of disease along with
intensity of attack depends upon
including environment. Incomplete information
dynamic changes in data contributes
Therefore capturing inherent uncertainty is another
challenge in design issue of KE in wheat production
knowledge base. However, AI method and techniques
can address these issues and probabilistic reasoning is
one of the options. Wheat production technology in
Pakistan, based on factor indicated in (Table
been developed and available through many sources.
The agricultural technology is constantly changing as
result of continuous research. Sharing of newly
generated knowledge and updating is also an essential
component in agricultural information management.
Decision making in wheat production
management starts from pre-cultivation till the
harvesting of crop (Fig-1). In pre cultivation selecting
appropriate wheat variety suitable for particular
agricultural zone is required along with the prevailing
cropping system.
Cultivation management is more dynamic as
compare to pre cultivation and post harvest
management. The critical decision involved appropriate
planting date, seed rate at time of sowing
in wheat production management,
describes mechanism for knowledge
representation of wheat production management
discuss mapping of wheat
formation into a knowledge
base, Section V present soft computing model and
IN WHEAT
MANAGEMENT
Wheat production management requires decision
from pre-cultivation to
parameters (Table-1)
technology constitutes spatio-
temporal variation. In Pakistan wheat varieties are
for different agro
have varied set of decision
parameters are selection of
practices (planting date,
management during
cultivation. The yield of crop is not only affected by
but management of diseases and pests
environmental conditions contribute to
temporal variation in
wheat production technology is a challenge in
development of wheat production knowledge base. The
technological information varies depending upon
patial) along with time of
(temporal). The pest and
essential component in
wheat plant growth. Diagnosis of disease along with
depends upon certain factors
ncomplete information and
contributes to uncertainty.
Therefore capturing inherent uncertainty is another
challenge in design issue of KE in wheat production
knowledge base. However, AI method and techniques
and probabilistic reasoning is
Wheat production technology in
Pakistan, based on factor indicated in (Table-1), has
been developed and available through many sources.
The agricultural technology is constantly changing as
result of continuous research. Sharing of newly
ed knowledge and updating is also an essential
component in agricultural information management.
in wheat production
cultivation till the
In pre cultivation selecting
appropriate wheat variety suitable for particular
required along with the prevailing
Cultivation management is more dynamic as
compare to pre cultivation and post harvest
ritical decision involved appropriate
planting date, seed rate at time of sowing, fertilizer
application and irrigation management.
cultivation management continuous updating of
environmental parameters
affect incidence pest & disease
factor in crop yield.
Consideration
S
#
Decision
Domain
Environmental
1 Crop &
Cultivar
selection
Temperature
growing season,
soil conditions
2 Land
Preparation
Crop timing
and Methods
Soil Temperature
and moisture, Soil
temperature,
humidity
3 Irrigation
Management
Rainfall amount
and distribution,
soil moisture,
4 Fertility
Management
Soil chemical
condition, soil
moisture and
aeration, soil
temperature
5 Pest
management
Temperature,
humidity, rainfall
6 Harvest
Timing and
Methods
Temperature,
rainfall, light
intensity, humidity
Table-1 Parameter affecting Crop Production Decisions
Similarly timely application of water and fertilizer
along with monitoring of the growth stages of wheat
crop is required to achieve optimum
.
Fig-1 Decision Requirement in Wheat production
and irrigation management. During
cultivation management continuous updating of
(temperature, humidity)
& disease which is make or break
Consideration
Environmental Biological
Temperature
growing season,
soil conditions
Crop Adaptation,
Pest resistance
Soil Temperature
and moisture, Soil
Soil Biology
Germination,
Emergence,
growth rate
Rainfall amount
and distribution,
soil moisture,
Water required
/available by
crop, water use
efficiency,
Soil chemical
condition, soil
moisture and
aeration, soil
Soil/plant
Nutrient , uptake,
Growth rate, crop
residue
contribution to
subsequent crop
Temperature,
humidity, rainfall
Weed, Insect and
disease
population,
Population of pest
predators or
parasite
Temperature,
rainfall, light
intensity, humidity
risk of loss due to
over maturity, or
pest damage
Parameter affecting Crop Production Decisions
Similarly timely application of water and fertilizer
along with monitoring of the growth stages of wheat
optimum yield of wheat.
Requirement in Wheat production
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
57 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
3. III KNOWLEDGE REPRESENTATION IN WHEAT
PRODUCTION MANAGEMENT
Reasoning on the basis of available fact and getting
required solution requires that facts are represented in
appropriate form. Knowledge representation is the field
of artificial intelligence (AI) devoted to representing
information about the domain of interest, like
agriculture, in a form that a computer system can utilize
to solve complex tasks such as diagnosing plant disease
or structuring rules to classify information. One of the
techniques of knowledge representation is using
ontology [Natalya F] [Burney and Nadeem 2012].
Ontology defines the terms and concepts commonly
used in a particular domain. Therefore, ontology
development is a process of representing terms,
concepts and relationship in domain of interest. The
main advantage of ontology representation is, it
provides an explicit conceptual standard that can be
shared and commonly used to describe the information
in a domain of interest.
The use of ontology can be undertaken by different
points of view:[ Sofia]
1. Building a new ontology from scratch
2. Building ontology by assembling, extending,
specializing and adapting, other ontology which are
parts of the resulting ontology.
3. Reusing ontology, by merging different ontology
on the same or similar subject into a single one that
unifies all of them.
In this study second option is used by utilizing
terms and concept of AGROVOC along with rice
production ontology [Thunkijjanukij 2009].
In Agriculture ontology are developed through
many sources. AGROVOC is a controlled vocabulary
in agricultural covering all areas of interest including
food, nutrition, agriculture, fisheries, forestry,
environment etc. AGROVOC is a collaborative effort
and kept up to date by the AGROVOC team in FAO, by
a number of involved institutions serving as focal points
for specific languages, and by individual domain
experts. To date, AGROVOC contains over 32,000
concepts organized in a hierarchy; each concept may
have labels in up to 22 languages. Thunkijjanukij1
et al
has proposed rice production ontology for production
management in Thailand. The ontology contain,
concepts, terms and relationship related to rice
production ontology
AGROVOC arrange terms in agriculture in
hierarchy of application which helps to develop
conceptual model of entities and entity
relationship[Sachit 2012]. The traditional AGROVOC
[Sachit 2012] Thesaurus is made up of terms (Table-2),
connected by hierarchical and non-hierarchical
relations. The relations used are the classical relations
(Table-3) used in thesauri as: BT (broader term), NT
(narrower term), RT (related term), UF (non-
descriptor). Scope notes and definitions are used in
AGROVOC to clarify the meaning and the context of
terms. AGROVOC in addition to "terms" also uses the
notion of "concept", and a larger set of relations
between concepts. A concept is represented by all the
terms, preferred and non-preferred, in languages, to
which it is associated. Both concepts and terms
participate in relationships with other concepts and
terms:
Fertilizer application Descriptor
Fertilizer combinations Descriptor
Fertilizer formulations Descriptor
fertilizers Descriptor
Planting date Descriptor
Planting density Non-Descriptor
Planting depth Descriptor
Planting distance Non-Descriptor
Planting methods Descriptor
Disease prevalence Non-Descriptor
Disease recognition Descriptor
Disease reporting Non-Descriptor
Disease resistance Descriptor
Disease surveillance Descriptor
Disease symptoms Non-Descriptor
Disease transmission Descriptor
Disease treatment Non-Descriptor
Diseases Descriptor
Table-2 Common Agricultural terms(snapshot)
The processes in wheat production are defined
by the specifying relationship between concepts and
terms (Table-3). The concepts may have equivalence or
associative relationship have forward or inverse
direction. For instance the object property
hasIrrigationMethod define relationship between
agricultural Zone and irrigation process e.g Zone-IV
hasIrrigationMethod of Rain-Fed. Similarly the inverse
relationship specifies Rain-Fed isIrrigationMethodof
Zone-IV. In wheat production ontology AGROVOC
terms and concepts are utilized for representing
knowledge. (Table-3)
Relationship Inverse Relationship Relationship
Type
hasCommonName isCommonNameof Equivalence
hasCultivationProcess isCultivationProcessof Associative
hasCultivationMethod isCultivationMethodof Associative
hasIrrigationProcess isIrrigationProcessof Associative
hasIrrigationMethod isIrrigationMethodof Associative
hasPest IsPestof Associative
Produce IsProducedFrom Associative
isResistantTo IsHarmlessFor Associative
isSucceptibleTo IsHarmfuFor Associative
Table-3 Relationship in wheat production ontology
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
58 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
4. Utilizing basic concepts and
production technology is represented in machine
readable form.
IV KNOWLEDGE BASE DEVELOPMENT
A. Knowledge Base (KB) Wheat Production
In the next step Knowledge Base
using wheat ontology. KB comprises
facts, mechanism for logical reasoning
methods. Logical reasoning is done by defining
and imposing constraints. We utilized
effective open source tool to develop KB
rules. Protégé employ graph
information, SWRL[Semantic Web Rule Language]
rule development, SPARQL[Protocol and RDF query
language] for query development and RDF
description Framework) to deliver
through semantic web. Graph data
basic components of semantic web [
The graph database is an effective tool of
semantic web. Its a kind of database
structures to represent and store data through
edges and properties[Silvescu]. The graph database is
quite different from relational or hierarchical
In graph database resources are
resources[Fig 2], with no single resource having any
particular intrinsic importance over another.
are connected through properties. Graph databases, by
design, allow simple and fast retrieval of complex
hierarchical structures that are difficult to
relational database systems.
Fig 2 Wheat graph database
Different methods can be used as
storage mechanism in graph database
tables, document or RDF graph. In this research RDF is
utilized for storage.
The Resource Description Framework (RDF)
is a family of World Wide Web Consortium
specifications originally designed as a
model. The graph data model is the model the semantic
tilizing basic concepts and relationship wheat
is represented in machine
DEVELOPMENT
Knowledge Base (KB) Wheat Production
Knowledge Base is developed
comprises representation of
logical reasoning and querying
methods. Logical reasoning is done by defining rules
We utilized Protégé, an
develop KB and defining
graph database to store
Semantic Web Rule Language] for
Protocol and RDF query
for query development and RDF(Resource
deliver wheat knowledge
abase and RDF are
[Canda]
The graph database is an effective tool of
database that uses graph
represent and store data through nodes,
The graph database is
quite different from relational or hierarchical database.
are related to other
, with no single resource having any
particular intrinsic importance over another. Resources
Graph databases, by
design, allow simple and fast retrieval of complex
hierarchical structures that are difficult to represent in
Different methods can be used as underlying
in graph database which includes
In this research RDF is
The Resource Description Framework (RDF)
World Wide Web Consortium (W3C)
originally designed as a metadata data
. The graph data model is the model the semantic
web to store data and RDF is the format in which it is
written. RDF is an XML-based language for describing
information contained in a Web resource.
B. Querying Wheat KB
Retrieval of information from Wheat KB can
be done in two ways querying the RDF graph
SPARQL [Zheng] query la
based system using SWRL[Connor
approaches are used
C. Rules based system
Rules based expert system and rules are
structured way of reasoning and classifying
information. Production rules in the form of if & then
clause are used to define or apply constrain in
declarative manner. Domain rules are structured in
informal, semi informal and formal ways. However, in
knowledge base rules are expressed in formal system.
Informal statements in natural language are transformed
into formal language or rule execution language.
In AI several methods can be used for formal
expression or rules like SQL
Language), ECA, predicate logic and propositional
logic. In wheat production expert system ontology
based predicate logic and axioms are used to define
rules for extraction and updating of information from
wheat knowledge base. Developing ontology base rule
[Kalibatiene 2010] three steps proces
Express rule in informal natural language
Express rule using ontology concepts and
relationship
Express rule in formal predicate logic
In wheat production management expert
system conditions and actions are proposed by
agriculture expert in natural language like
Informal Rule: If soil fertility is average and
rainfall is moderate then 2 begs of urea is
applied.
Using this scheme lets take one
In-formal: if soil is loamy and soil condition is
weak then apply 3 bag of NPK or 2
bag of DAP at
Ontology based Representation:
<Soil> < has_type> <loamy>
<Soil> <has_condition> <Weak>
then <Fertilizer> <has_name>
<hasQuantity> = ”2 bags”, Or
web to store data and RDF is the format in which it is
based language for describing
information contained in a Web resource.
of information from Wheat KB can
querying the RDF graph using
query language or utilizing rule
Connor]. In this study both
Rules based expert system and rules are
structured way of reasoning and classifying
information. Production rules in the form of if & then
clause are used to define or apply constrain in
declarative manner. Domain rules are structured in
formal and formal ways. However, in
knowledge base rules are expressed in formal system.
Informal statements in natural language are transformed
into formal language or rule execution language.
methods can be used for formal
ules like SQL (Structured Query
Language), ECA, predicate logic and propositional
logic. In wheat production expert system ontology
based predicate logic and axioms are used to define
rules for extraction and updating of information from
. Developing ontology base rule
hree steps process is carried out to
Express rule in informal natural language
Express rule using ontology concepts and
Express rule in formal predicate logic
In wheat production management expert
system conditions and actions are proposed by
agriculture expert in natural language like
Informal Rule: If soil fertility is average and
rainfall is moderate then 2 begs of urea is
take one example
if soil is loamy and soil condition is
weak then apply 3 bag of NPK or 2
of DAP at the time of sowing
Ontology based Representation:
<Soil> < has_type> <loamy> and
<Soil> <has_condition> <Weak>
has_name> = ”NPK”
Or
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
59 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
5. Fertilizer> <has_name> = ”DAP” <hasQuantity> = ”3
bags”,
and <Fertilizer> <hastimeof application> = ”at sowing”
The ontological expression is transformed into rule
using SWRL syntax. So formal expression in
logic will be
SoilType(?x) ^ SoilCondition(?y) -----
Fertilizer(?z),
Fertilizer(?f) ^ hastimeof application(?t)
has_quantity(?q)
The terms in bracket represent variable which are
replaced by named individual
In summary knowledge base of wheat
production technology basically comprises a set of
terms concepts and relationships and set of rules which
can perform reasoning process on wheat production
technology information in Pakistan and finally propose
solution to queried problem.
Fig 3 Semantic Web Wheat Production Technology
V SOFT COMPUTING MODEL
PRODUCTION MANAGEMENT
Finally, we propose a soft computing model
(Fig 4) for knowledge engineering
model employ acquisition of agricultural
through conventional methods, developing a knowledge
base through OWL ontology and implementing a
reasoning mechanism on top of it using rule base
system and soft computing techniques for classification
and probabilistic reasoning. The
implementation of the system is undertaken through
semantic web (Fig 3). Web based user interface enable
user submitting problems which are
Fertilizer> <has_name> = ”DAP” <hasQuantity> = ”3
and <Fertilizer> <hastimeof application> = ”at sowing”
transformed into rule
rmal expression in predicate
-----
Fertilizer(?f) ^ hastimeof application(?t) ---
The terms in bracket represent variable which are
knowledge base of wheat
production technology basically comprises a set of
terms concepts and relationships and set of rules which
can perform reasoning process on wheat production
technology information in Pakistan and finally propose
Wheat Production Technology
ODEL WHEAT
ANAGEMENT
we propose a soft computing model
engineering in agriculture. The
model employ acquisition of agricultural information
through conventional methods, developing a knowledge
base through OWL ontology and implementing a
reasoning mechanism on top of it using rule base
system and soft computing techniques for classification
. The technical
implementation of the system is undertaken through
). Web based user interface enable
which are transformed into
query and submitted to KB. Knowledge is processed
using embedded rules and solution is delivered
RDF
Fig. 4 Soft Computing Model for DSS in Wheat
Production Management
II RESULT AND DISCUSSION
Knowledge engineering in agriculture is
essential for developing knowledgebase, expert systems
and decision support system
work for knowledge engineering of Wheat production
technology in Pakistan is discussed. The frame work is
a four tire process. In the first tire wheat production
technology is acquired in informal
(English) structure. In the second step agricultural
ontology is used for more formal representation using
specific technical terms whi
as relationship. In the third
used for logical reasoning and
and finally acquired knowledge is delivered using
semantic web through RDF.
of the frame work is the updating of knowledge by the
user who has limited knowledge of information
technology. Ontology provides
with underlying logical structure which facilitate
mapping highly technical agricultural information into
machine readable form. The proposed system is
scalable as well as dynamic.
The developed system is dynamic
that it continuously update domain knowledge
expert opinion as well as new
research and scalable in the sense that
modified for other crops management
With regard to scope of application o
knowledge representation of wheat production
management have several advantages as it facilitate
generic application development
different users. Further, use of predicate logic in rule
based system facilitates adoption
query and submitted to KB. Knowledge is processed
and solution is delivered using
Soft Computing Model for DSS in Wheat
RESULT AND DISCUSSION
Knowledge engineering in agriculture is
essential for developing knowledgebase, expert systems
and decision support systems. In this paper a frame
work for knowledge engineering of Wheat production
technology in Pakistan is discussed. The frame work is
In the first tire wheat production
in informal simple language
n the second step agricultural
formal representation using
ich define concepts as well
In the third step rule based system is
and knowledge processing
acquired knowledge is delivered using
semantic web through RDF. The essential component
of the frame work is the updating of knowledge by the
limited knowledge of information
provides basic building blocks
ing logical structure which facilitate
mapping highly technical agricultural information into
The proposed system is flexible
tem is dynamic in the sense
update domain knowledge using
expert opinion as well as new technology based on
in the sense that system can be
other crops management technologies.
With regard to scope of application ontology based
e representation of wheat production
management have several advantages as it facilitate
application development of knowledge base for
use of predicate logic in rule
adoption of semantic web
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
60 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
6. technology enabling implementation on diversified
plat form including embedded mobile phone based
technology. One limiting factor of the proposed
system is need for incorporation of some local
language terms specific to particular region.
However, this can be achieved by updating or
redefining of some ontological concepts or terms
VI FUTURE WORK
Rule based system for reasoning the
knowledge base is more efficient if it is capable of
handling incomplete and uncertain information.
Authors has plan to uses Bayesian network and fuzzy
logic system for wheat disease diagnosis [Burney
2015] and predicting impact of pest and disease
attack on crop yield to capture the inherent
uncertainty of these factor as it has profound effect
on overall production.
REFRENCES
1. Burney, Aqil, Nadeem Mehmood, 2012
“Generic Temporal and Fuzzy Ontological
Framework, (GTFOF) for Developing Temporal-
Fuzzy Database Model for Managing Patient’s
Data, Journal of Universal Computer Science,
vol. 18, no. 2 (2012), 177-193
2. Burney, Aqil, Zain Abbas, 2015, “Applications
of Rough Sets in Health Sciences and Disease
Diagnosis”, Recent Researches in Applied
Computer Science, ISBN: 978-1-61804-307-8
3. Canda, K.Selcuk, “Resource Description Frame
work: Metadata and Its Applications”, Arizona
State University,
candan,hliu,reshma.suvarna@asu.edu
http://citeseerx.ist.psu.edu
4. Connor, Martin O et al , “Querying the Semantic
Web with SWRL”, Stanford Medical
Informatics, Stanford University School of
Medicine, Stanford, CA 94305
martin.oconnor@stanford.edu.
5. Darai, D.S S Singh, S Biswas, 2010,
“Knowledge Engineering an overview”,
International Journal of Computer Science and
Information Technologies, Vol. 1 (4) , 230-234.
6. Hoogenboom, G P.W. Wilkens, P.K. Thornton,
et al., 1999. “Decision support system for agro
technology transfer” v3.5. In: DSSAT version 3,
vol. 4 University of Hawaii, Honolulu, HI, pp.
1-36.
7. Kalibatiene, Diana et al , 2010, “Ontology-Based
Application for Domain Rules Development”
Scientific Paper University of Latvia, Vol
756, Computer Science and Information
Technologies pp 9-32
8. Khan, Ghanzafar, Ali, 2010, “Present and
prospective role of electronic media in the
dissemination of agricultural technologies among
farmers of the Punjab, Pakistan, Ph.d theses,
university of Agriculture Faisalabad, Pakistan
9. Kolhe Savita, Raj Kamal, Harvinder S. Saini,
G.K. Gupta, 2011, “A web-based intelligent
disease-diagnosis system using a new fuzzy-
logic based approach for drawing the inferences
in crops”, Computers and Electronics in
Agriculture Volume 76, Issue 1, Pages 16-27
10. Magarey, R.D.; Travis, J.W.; Russo, J.M.; Seem,
R.C. & Magarey, P.A. 2002. Decision Support
Systems: Quenching the Thirst. Plant Disease,
Vol. 86, No. 1, pp. 4-14,
11. Natalya F. Noy and Deborah L. McGuinness,
“Ontology Development 101: A Guide to
Creating Your First Ontology” Stanford
University
12. Sachit, Rajbhandari and Johannes Keizer(2012,)
“The AGROVOC Concept Scheme-A
Walkthrough”. Journal of Integrative
Agriculture, vol. 11, n. 5. [Journal article]
13. Silvescu, Adrian et al “Graph Databases”,
Artificial Intelligence Research Laboratory,
Department of Computer Science, Iowa State
University,
http://people.cs.ksu.edu/~dcaragea/papers/report.
pdf
14. Sofia H. Pinto and J.P. Martins “Reusing
Ontologies” Instituto Superior Te´cnico
Departamento de Eng. Informatica Grupo de
Inteligencia Artificial Av. Rovisco Pais, 1049-
001 Lisboa, Portugal
15. Thunkijjanukij, Aree., 2009, “Ontology
Development for Agricultural Research
Knowledge Management: A Case Study of Thai
Rice. Tropical Agriculture interdisciplinary
Graduate program, Ph.D Theses
16. Verborgh, Ruben, Querying Datasets on the Web
with High Availability,
http://linkeddatafragments.org/publications/iswc
2014.pdf
17. Zheng, Weiguo et al, “Semantic SPARQL
Similarity Search Over RDF Knowledge
Graphs”, http://www.vldb.org/pvldb/vol9/p840-
zheng.pdf, pp 840-851
18. Zhi Ping Ding, 2011, “The Development of
Ontology Information System Based on
Bayesian Network and Learning,” Advances in
Intelligent and Soft Computing”, Volume 129, ,
Pages 401-406
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
61 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
7. AUTHOR’S PROFILE
Dr. Aqil Burney is Professor at
College of Computer Science and
Information Systems (CCSIS) at
Institute of Business Management
(IoBM) Karachi, one of the leading
Business School Pakistan. Dr. Aqil
Burney was a Meritorious
Professor (R.E.) and approved supervisor in
Computer Science and Statistics by the HEC, Govt.
of Pakistan. He was also the founder Project Director
(UBIT) & Chairman of the Department of Computer
Science, University of Karachi. He is also member of
various higher academic boards of different
universities of Pakistan. His research interest includes
artificial intelligence, soft computing, neural
networks, fuzzy logic, data science, statistics,
simulation and stochastic modeling of mobile
communication system and networks and network
security, currently heading the detp. of actuarial
science and risk management at CSCIS - IoBM.
Teaching mostly MS(CS) Ph.D(CS))courses such as
Data Warehousing, data mining & ML and
information retrieval systems, fuzzy systems
,advanced theory of statistics, Markov chains and
Financial Time Series.
Jawed Naseem is Principal Scientific
Officer (RE) in Pakistan Agricultural
Research Council. He has MCS &
M.Sc (Statistics) and currently a
Ph D. scholar department of
Computer Science, University of
Karachi, Pakistan. His research
interest includes data modeling, machine learning,
probabilistic reasoning, Information Management &
Security and Decision Support System particularly in
health care and agricultural research. He has
experience in research & education at national
regional (SAARC) and international level.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 2, February 2018
62 https://sites.google.com/site/ijcsis/
ISSN 1947-5500