American Journal of Data Mining and Knowledge Discovery
Volume 5, Issue 1, June 2020, Pages: 11-19
Received: Jun. 3, 2020;
Accepted: Jun. 17, 2020;
Published: Jul. 6, 2020
Views 337 Downloads 60
Terungwa Simon Yange, Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Charity Ojochogwu Egbunu, Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Oluoha Onyekwere, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Kater Amos Foga, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.
Terungwa Simon Yange,
Charity Ojochogwu Egbunu,
Kater Amos Foga,
Prediction of Agro Products Sales Using Regression Algorithm, American Journal of Data Mining and Knowledge Discovery.
Vol. 5, No. 1,
2020, pp. 11-19.
Essien J. A., Essien J. B. & Bello R. S. Product Quality Preservation and Agricultural Transformation in Nigeria. Science Journal of Business and Management. Vol 3, No. 5-1, 2015, pp. 35-40.
Agronigeria. ng (2017). NSPRI says Nigeria records N2.7trillion deficit annually from post-harvest losses. Available at URL: https://agronigeria.ng/2017/10/23/nspri-says-nigeria-records-n2-7trillion-deficit-annually-post-harvest-losses/.
Azeez, B. (2019). Nigeria loses over $50b annually to postharvest waste. Nigerian Stored Products Research Institute available at URL: https://tribuneonlineng.com/nigeria-loses-over-50b-annually-to-postharvest-waste/.
Alsem, K. J., Leeflang, P. S. & Reuyl, J. C. (1989). The Forecasting Accuracy of Market Share Models using Predicted Values of Competitive Marketing Behaviour. International Journal of Research in Marketing, 6: 183-98.
Ramasunbramanian V. (2013). Forecasting Techniques in Agriculture. Available at URL: http://cabgrid.res.in/cabin/publication/smfa/Module%20IV/5.%20Applications%20of%20Technology%20Forecasting%20methods%20in%20Agriculture_Rama.pdf.
Jha, G. K. & Sinha, K. (2013). Agricultural Price Forecasting Using Neural Network Model: An innovative information delivery system. Agricultural economics research. 26 (2). 229-239.
Ruekkasaem & Sasananan (2018) International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 7, July 2018, pp. 957–971, Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=7 ISSN Print: 0976-6340 and ISSN Online: 0976-6359.
Xu, G., Piao S. & Song, Z. (2015). Demand Forecasting of Agricultural Products Logistics in Community. American Journal of Industrial and Business Management, 5: 507-517.
Lawrencea, M. & Godwin, P. (2006) Judgemental Forecasting: A Review of Progress Over the Last 25 Years. International Journal of Forecasting, 22: 493-518.
Frisvold, G. & Murugesan, A. (2013). Use of Weather Information for Agricultural Decision Making. Weather, Climate, and Society. 5. 55-69. 10.1175/WCAS-D-12-00022.1.
Bahng, Y. & Kincade, D. (2012). The relationship between temperature and sales: Sales data analysis of a retailer of branded women's business wear. International Journal of Retail & Distribution Management. 40.
Kdhananjayamurthy B. V., Krishnamurthy K. N. & Murthy K. B (2018) Price forecasting of Chilli -by an Exponential Smoothing. Journal of Current Agricultural Sciences. Vol. 8, Issue, 10 (A), pp. 318-322.
Suttle, R. (2019). The Impact of Location on Business Success.
Available at URL: https://bizfluent.com/facts-5859788-impact-location-business-success.html.
Karani D. K. & Wanjohi J. (2017). Factors Influencing Marketing of Agricultural Produce Among Small-Scale Farmers: A Case of Sorhgum In Giaki Location, Meru County Kenya. International Journal of Economics, Commerce and ManagementUnited Kingdom, 5 (8).
Annadanapu, P. K. & Ravi, B. (2017). Time Series Data Analysis on Agriculture Food Production. 520-525. 10.14257/astl.2017.147.73.
Abid, S., N. Jamal, M. Z. Anwar & Zahid, S. (2018). Exponential growth model for forecasting of area and production of potato crop in Pakistan. Pakistan Journal of Agricultural Research, 31 (1): 24-28.
Nikkhah A., Rohani A., Rosentrater K. A., Assad M. E. & Ghnimi S. (2019). Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows. Environmental progress and sustainable Energy. 38 (4).
Sellam, V. & Poovammal, E., (2016). Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology. 9. 10.17485/ijst/2016/v9i38/91714.
Shastry, A., Sanjay, H. A., Bhanusree (2017). Prediction of Crop Yield Using Regression Techniques. International Journal of Soft Computing. Vol 12 (2): 96–102.
Zotteria, G. & Kalchschmidtb, M. (2007). A Model for Selecting the Appropriate Level of Aggregation in Forecasting. International Journal of Production Economics Processes, 108: 74–80.
Du, X., F., Leung, S. C. H., Zhang, J. L. & Lai, K. K. (2013). Demand Forecasting of Perishable Farm Products using Support Vector Machine. International Journal of Systems Science, 44 (3): 556-567.