Development of an Artificial Intelligence based Agriculture Distance Education Model for Prediction of Crop Price and Yield Levels in India
Main Article Content
Abstract
Agriculture is the source of food and is indisputably the prime reason for the survival of the beings in the earth planet. With the surging population of people, there is a need for guaranteeing precision-centric, informed and optimized agriculture. It is all about generating quality, fresh and edible food in large quantities in order to feed the growing number of human beings. The role of information technology (IT) is therefore bound to escalate in the days to come towards intelligent agriculture. Newer trends and technologies are emerging and evolving fast. These bring forth a variety of transitions. With Smartphone’s, wearable’s, and IoT devices, the learning can happen everywhere all the time. Agriculturists and horticulturists ought to be knowledge-driven in their pursuits and passions in order to be hugely successful. Ubiquitous learning is, therefore, defined as an all-day learning environment supported by a bevy of edge technologies and tools. It is enriched with the Internet, which is emerging as the world's largest information superhighway. In this paper, we formulate a method for enabling ubiquitous learning for farmers to get the best price for their crops, to predict the crop yields, and to get other relevant information to attain the phenomenal success in their occupations. We have built and hosted a cloud application through which our farmers can easily get to know all the right and relevant details in order to plan and execute the farming in a clear and confident fashion.
Article Details
How to Cite
MADHUMATHI , R. (2017). Development of an Artificial Intelligence based Agriculture Distance Education Model for Prediction of Crop Price and Yield Levels in India . Asian Journal of Distance Education, 12(1), 69-78. Retrieved from https://asianjde.com/ojs/index.php/AsianJDE/article/view/254
Section
Articles
The work published in AjDE is licensed under a Creative Commons Attribution ShareAlike 4.0 International Licence (CC-BY).
References
Agarwal, H., & Kumar, A. (2013). E-Learning for Agriculture Education in India. IJRET: International Journal of Research in Engineering and Technology, 2, 101104. Retrieved March 10, 2014, from https://pdfs.semanticscholar.org/0a79/31 d3d93fb3ac754f0e6e7ae62c9d2c3ea71b. pdf
Ankalaki, S., Chandra, N., & Majumdar, J. (2016). Applying Data Mining Approach and Regression Model to Forecast Annual Yield of Major Crops in Different District of Karnataka. International Journal of Advanced Research in Computer and Communication Engineering, 5, 25-29. Retrieved November 11, 2016,
75
MADHUMATHI
from https://ijarcce.com/wpcontent/uploads/2016/11/IJARCCEICRITCSA-7.pdf
Bagchi, A. (2000). Artificial Intelligence in Agriculture. To eradicate extreme poverty and hunger - Millennium Development Goal, UN Summit, 1-5, Retrieved May 29, 2015, from https://www.mindtree.com/sites/default/f iles/201804/Artificial%20Intelligence%20in%20A griculture.pdf
Bendre, M.R.R.C., & Thool, V.R. (2015). Big Data in Precision Agriculture: Weather Forecasting for Future Farming. 1st International Conference on Next Generation Computing Technologies, 744-750. Retrieved October 10, 2016, from https://www.semanticscholar.org/paper/B ig-data-in-precision-agriculture%3AWeather-for-BendreThool/774fe2c519dcc5ea9a3e4e8609ffb2 37fb68aa48
Fathima, G., & Geetha, R. (2014). Agriculture Crop Pattern Using Data Mining Techniques. International Journal of Advanced Research in Computer Science and Engineering, 4, 781-786. Retrieved June 15, 2015, from http://ijarcsse.com/Before_August_2017/ docs/papers/Volume_4/5_May2014/V4I5 -0284.pdf
Gayatri, M.K., Jayasakthi, J., & Mala, A.G.S. (2015). Providing Smart Agricultural Solutions to Farmers for better yielding using IoT. IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 40-43. Retrieved December 25, 2016, from https://ieeexplore.ieee.org/document/735 8528/
Grajales, D.F.P., Mosquera, G.J.A., Mejia, F., Piedrahita, L.C., &Basurto, C. (2015). Crop-Planning, Making Smarter Agriculture with Climate Data. 4th International Conference on Agro-GeoInformatics, 240-244. Retrieved July 1, 2016, from https://ieeexplore.ieee.org/document/724 8124/
Hansra, B.S., & Jain, P.K. (2012). Open and Distance Learning System in Extension Education. Indian Research Journal of Extension Education, 1, 20-24. Retrieved July 25, 2015, from https://www.seea.org.in/special_issue/vol 1/03.pdf
Hemageetha, N. (2016). A survey on application of data mining techniques to analyze the soil for agricultural purpose. 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 3112-3117. Retrieved April 10, 2017, from https://ieeexplore.ieee.org/document/772 4836/
Irani, T., Scherler, C., Harrington, M., &Telg, R. (2013). Overcoming Barriers to Learning in Distance Education: The Effects of Personality Type and Course Perceptions on Student Performance. 27th Annual National Agricultural Education Research Conference, 434448. Retrieved August 30, 2015, from https://pdfs.semanticscholar.org/f70d/132 7078c2bff51c8ea8df9160ad4272957ff.pd f
Kalyani, M.R. (2012). Data Mining Techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 2, 439-449. Retrieved July 21, 2013, from http://citeseerx.ist.psu.edu/viewdoc/down load?doi=10.1.1.898.6657&rep=rep1&ty pe=pdf
Kaur, M., Gulati, H., & Kundra, H. (2014). Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications. International Journal of Computer Applications, 99, 1-3. Retrieved November 10, 2016, from https://www.ijcaonline.org/archives/volu me99/number12/17422-8273
Kundu, S. (2014). Open and Distance Learning Education its Scope and Constraints in Indian Scenario. IOSR Journal of Humanities and Social Science (IOSRJHSS), 19, Retrieved May 30, 2016, from http://iosrjournals.org/iosrjhss/papers/Vol19-issue4/Version4/A016440105.pdf
76
ASIAN JOURNAL of DISTANCE EDUCATION
Kushwaha, A.K., & Chrya, S.B. (2015). Crop yield prediction using Agro Algorithm in Hadoop. International Journal of Computer Science and Information Technology & Security (IJCSITS), 5, 271-274. Retrieved June 5, 2016, from https://ijcsits.org/papers/vol5no22015/7v ol5no2.pdf
Mathur, A., & Jha, A.K. (2015). Open and Distance Learning in India: A Practitioner’s Perspective. Book title on Optimizing Open and Distance Learning in Higher Education institutions, Ch. 17, 359-382. Retrieved December 24, 2016, from https://www.igi- global.com/book/optimizing-open- distance-learning-higher/177909.
McLean, S., Gasperini, L., &Rudgard, S. (2002). Distance Learning for Food Security and Rural Development: A Perspective from the United Nations Food and Agriculture Organization. 3, Retrieved April 18, 2014, from http://www.fao.org/docs/eims/upload/25 4244/157.pdf
Mundi, N.E., & Tenebe, A.V. (2013). The Role of Distance Education and E- learning in Agricultural Extension Services. Common Wealth of Learning, Retrieved March 13, 2016, from http://oasis.col.org/bitstream/handle/1159 9/1854/2013_Mundi%26Tenebe_Agricul tralExtensionServices.pdf?sequence=1&i sAllowed=y
Rub, G. (2009). Data Mining of Agricultural Yield Data: A Comparison of Regression Models. 9th Industrial Conference, 2437. Retrieved October 22, 2013, from https://link.springer.com/chapter/10.1007 %2F978-3-642-03067-3_3
Santhy, P., Jothi, G., & Valluvaparidasan, V. (2010). Empowerment of Agricultural Skills through Distance Education at Tamil Nadu Agricultural University. Common Wealth of Learning, Retrieved April 23, 2016, from http://wikieducator.org/images/4/48/P._S anthy.pdf
Sellam,V., & Poovammal, E. (2016). Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology, 9, 1-5. Retrieved March 15, 2017, from http://www.indjst.org/index.php/indjst/art icle/viewFile/91714/73940
Sharma, R. C. (2010). Emerging Trends of Student Support Services in Indian Distance Education. In D. Gearhart (Ed.), Cases on Distance Delivery and Learning Outcomes: Emerging Trends and Programs (pp. 245-258). Hershey, PA: IGI Global. doi:10.4018/978-160566-870-3.ch015 https://www.igiglobal.com/chapter/emerging-trendsstudent-support-services/38005
Sharma, R. C., & Mishra, S. (2010). Applications of E-Tutoring at Indira Gandhi National Open University (pp. 185-200). doi:10.4018/978-1-60566876-5.ch01 https://www.igiglobal.com/teaching-case/applicationstutoring-indira-gandhi-national/38035
Sharma, R. (2001). Online Delivery of Programmes: A case study of IGNOU. The International Review of Research In Open And Distributed Learning, 1(2). doi: http://dx.doi.org/10.19173/irrodl.v1i2.18
Subrahmanyam, C.V., & Ravichandran, K. (2013). Technology & Online Distance Mode of Learning. International Journal of Humanities and Social Science Invention, 2, 5-13, Retrieved December 10, 2015, from http://www.ijhssi.org/papers/v2(1)/Versi on-2/B210513.pdf
Verma, K., Pabbi, D., & Jat, A.S. (2016). Agriculture Advancement using Artificial Intelligence. 2nd International conference on Recent Trends Innovations in Science, Technology, Management and Environment, 74-77, Retrieved December 5, 2016, from https://pdfs.semanticscholar.org/65e3/ed b24c2e00c4d958a249d37a91b8576e993a .pdf
77
MADHUMATHI Tothova, D., & Hennyeyova, K. (2002). Preparation of E learning methodology in distance learning. AGRIC ECON, 48, 364-366. Retrieved June 30, 2014, from https://www.agriculturejournals.cz/public Files/59315.pdf
Ankalaki, S., Chandra, N., & Majumdar, J. (2016). Applying Data Mining Approach and Regression Model to Forecast Annual Yield of Major Crops in Different District of Karnataka. International Journal of Advanced Research in Computer and Communication Engineering, 5, 25-29. Retrieved November 11, 2016,
75
MADHUMATHI
from https://ijarcce.com/wpcontent/uploads/2016/11/IJARCCEICRITCSA-7.pdf
Bagchi, A. (2000). Artificial Intelligence in Agriculture. To eradicate extreme poverty and hunger - Millennium Development Goal, UN Summit, 1-5, Retrieved May 29, 2015, from https://www.mindtree.com/sites/default/f iles/201804/Artificial%20Intelligence%20in%20A griculture.pdf
Bendre, M.R.R.C., & Thool, V.R. (2015). Big Data in Precision Agriculture: Weather Forecasting for Future Farming. 1st International Conference on Next Generation Computing Technologies, 744-750. Retrieved October 10, 2016, from https://www.semanticscholar.org/paper/B ig-data-in-precision-agriculture%3AWeather-for-BendreThool/774fe2c519dcc5ea9a3e4e8609ffb2 37fb68aa48
Fathima, G., & Geetha, R. (2014). Agriculture Crop Pattern Using Data Mining Techniques. International Journal of Advanced Research in Computer Science and Engineering, 4, 781-786. Retrieved June 15, 2015, from http://ijarcsse.com/Before_August_2017/ docs/papers/Volume_4/5_May2014/V4I5 -0284.pdf
Gayatri, M.K., Jayasakthi, J., & Mala, A.G.S. (2015). Providing Smart Agricultural Solutions to Farmers for better yielding using IoT. IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 40-43. Retrieved December 25, 2016, from https://ieeexplore.ieee.org/document/735 8528/
Grajales, D.F.P., Mosquera, G.J.A., Mejia, F., Piedrahita, L.C., &Basurto, C. (2015). Crop-Planning, Making Smarter Agriculture with Climate Data. 4th International Conference on Agro-GeoInformatics, 240-244. Retrieved July 1, 2016, from https://ieeexplore.ieee.org/document/724 8124/
Hansra, B.S., & Jain, P.K. (2012). Open and Distance Learning System in Extension Education. Indian Research Journal of Extension Education, 1, 20-24. Retrieved July 25, 2015, from https://www.seea.org.in/special_issue/vol 1/03.pdf
Hemageetha, N. (2016). A survey on application of data mining techniques to analyze the soil for agricultural purpose. 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 3112-3117. Retrieved April 10, 2017, from https://ieeexplore.ieee.org/document/772 4836/
Irani, T., Scherler, C., Harrington, M., &Telg, R. (2013). Overcoming Barriers to Learning in Distance Education: The Effects of Personality Type and Course Perceptions on Student Performance. 27th Annual National Agricultural Education Research Conference, 434448. Retrieved August 30, 2015, from https://pdfs.semanticscholar.org/f70d/132 7078c2bff51c8ea8df9160ad4272957ff.pd f
Kalyani, M.R. (2012). Data Mining Techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 2, 439-449. Retrieved July 21, 2013, from http://citeseerx.ist.psu.edu/viewdoc/down load?doi=10.1.1.898.6657&rep=rep1&ty pe=pdf
Kaur, M., Gulati, H., & Kundra, H. (2014). Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications. International Journal of Computer Applications, 99, 1-3. Retrieved November 10, 2016, from https://www.ijcaonline.org/archives/volu me99/number12/17422-8273
Kundu, S. (2014). Open and Distance Learning Education its Scope and Constraints in Indian Scenario. IOSR Journal of Humanities and Social Science (IOSRJHSS), 19, Retrieved May 30, 2016, from http://iosrjournals.org/iosrjhss/papers/Vol19-issue4/Version4/A016440105.pdf
76
ASIAN JOURNAL of DISTANCE EDUCATION
Kushwaha, A.K., & Chrya, S.B. (2015). Crop yield prediction using Agro Algorithm in Hadoop. International Journal of Computer Science and Information Technology & Security (IJCSITS), 5, 271-274. Retrieved June 5, 2016, from https://ijcsits.org/papers/vol5no22015/7v ol5no2.pdf
Mathur, A., & Jha, A.K. (2015). Open and Distance Learning in India: A Practitioner’s Perspective. Book title on Optimizing Open and Distance Learning in Higher Education institutions, Ch. 17, 359-382. Retrieved December 24, 2016, from https://www.igi- global.com/book/optimizing-open- distance-learning-higher/177909.
McLean, S., Gasperini, L., &Rudgard, S. (2002). Distance Learning for Food Security and Rural Development: A Perspective from the United Nations Food and Agriculture Organization. 3, Retrieved April 18, 2014, from http://www.fao.org/docs/eims/upload/25 4244/157.pdf
Mundi, N.E., & Tenebe, A.V. (2013). The Role of Distance Education and E- learning in Agricultural Extension Services. Common Wealth of Learning, Retrieved March 13, 2016, from http://oasis.col.org/bitstream/handle/1159 9/1854/2013_Mundi%26Tenebe_Agricul tralExtensionServices.pdf?sequence=1&i sAllowed=y
Rub, G. (2009). Data Mining of Agricultural Yield Data: A Comparison of Regression Models. 9th Industrial Conference, 2437. Retrieved October 22, 2013, from https://link.springer.com/chapter/10.1007 %2F978-3-642-03067-3_3
Santhy, P., Jothi, G., & Valluvaparidasan, V. (2010). Empowerment of Agricultural Skills through Distance Education at Tamil Nadu Agricultural University. Common Wealth of Learning, Retrieved April 23, 2016, from http://wikieducator.org/images/4/48/P._S anthy.pdf
Sellam,V., & Poovammal, E. (2016). Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology, 9, 1-5. Retrieved March 15, 2017, from http://www.indjst.org/index.php/indjst/art icle/viewFile/91714/73940
Sharma, R. C. (2010). Emerging Trends of Student Support Services in Indian Distance Education. In D. Gearhart (Ed.), Cases on Distance Delivery and Learning Outcomes: Emerging Trends and Programs (pp. 245-258). Hershey, PA: IGI Global. doi:10.4018/978-160566-870-3.ch015 https://www.igiglobal.com/chapter/emerging-trendsstudent-support-services/38005
Sharma, R. C., & Mishra, S. (2010). Applications of E-Tutoring at Indira Gandhi National Open University (pp. 185-200). doi:10.4018/978-1-60566876-5.ch01 https://www.igiglobal.com/teaching-case/applicationstutoring-indira-gandhi-national/38035
Sharma, R. (2001). Online Delivery of Programmes: A case study of IGNOU. The International Review of Research In Open And Distributed Learning, 1(2). doi: http://dx.doi.org/10.19173/irrodl.v1i2.18
Subrahmanyam, C.V., & Ravichandran, K. (2013). Technology & Online Distance Mode of Learning. International Journal of Humanities and Social Science Invention, 2, 5-13, Retrieved December 10, 2015, from http://www.ijhssi.org/papers/v2(1)/Versi on-2/B210513.pdf
Verma, K., Pabbi, D., & Jat, A.S. (2016). Agriculture Advancement using Artificial Intelligence. 2nd International conference on Recent Trends Innovations in Science, Technology, Management and Environment, 74-77, Retrieved December 5, 2016, from https://pdfs.semanticscholar.org/65e3/ed b24c2e00c4d958a249d37a91b8576e993a .pdf
77
MADHUMATHI Tothova, D., & Hennyeyova, K. (2002). Preparation of E learning methodology in distance learning. AGRIC ECON, 48, 364-366. Retrieved June 30, 2014, from https://www.agriculturejournals.cz/public Files/59315.pdf