Volume 6, Issue 3, June 2020, Page: 72-82
Using Prescriptive Analytics for the Determination of Optimal Crop Yield
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
Malik Adeiza Rufai, Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Oluoha Onyekwere, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Alao Abiodun Abdulrahman, Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Idris Abdulkadri, Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Received: May 28, 2020;       Accepted: Jun. 8, 2020;       Published: Jul. 6, 2020
DOI: 10.11648/j.ijdsa.20200603.11      View  209      Downloads  86
The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.
Prescriptive Analytics, Optimal, Crop Yield, Machine Learning, Support Vector Machine, XGBoost
To cite this article
Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, Idris Abdulkadri, Using Prescriptive Analytics for the Determination of Optimal Crop Yield, International Journal of Data Science and Analysis. Vol. 6, No. 3, 2020, pp. 72-82. doi: 10.11648/j.ijdsa.20200603.11
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Plecher (2020). Distribution of gross domestic product (GDP) across economic sectors Nigeria 2018. Available at URL: https://www.statista.com/statistics/382311/nigeria-gdp-distribution-across-economic-sectors/.
Federal Ministry of Agriculture and Rural Development (2008). Available at URL: https://fmard.gov.ng/.
Ibirogba, F. (2019). Consumption of locally produced food items on the rise as borders remain shut. Available at URL: https://guardian.ng/saturday-magazine/consumption-of-locally-produced-food-items-on-the-rise-as-borders-remain-shut/.
Veenadhari, S., Bharat, & M., Singh, D. (2018). Machine learning approach for forecasting crop yield based on climatic parameters. International Research Journal of Engineering and Technology (IRJET), 5 (3), 129.
Priya, P., Muthaiah, U. & Balamuruga, M. (2018). Predicting Yield of the Crop Using Machine Learning Algorithm. Ijesrt International Journal of Engineering Sciences & Research Technology, 7 (4), 1-3.
Bondre, D. A. & Mahagaonkar, S. (2019). Prediction of Crop Yield and Fertilizer Recommendation Using Machine Learning Algorithms. International Journal of Engineering Applied Sciences and Technology, 4 (5): 371-376.
Alexandros, B. (2018) Prescriptive Analytics: A Survey of Approaches and Methods. Athens, Greece: National Technical University of Athens (NTUA).
Kimetu, J., Lehmann, J., Ngoze, S., Mugendi, D., Kinyangi, J., Riha, S. V., Louis R., John & Pell, A. (2008). Reversibility of Soil Productivity Decline with Organic Matter of Differing Quality Along a Degradation Gradient. Ecosystems. 11. 10.1007/s10021-008-9154-z.
Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, N.. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development. Vol 36 (2). 10.1007/s13593-016-0364-z.
You J., Li X., Low M., Lobell D. & Ermon S. (2017). Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. 31th AAAI Conference on Artificial Intelligence (AAAI 2017) Available at URL: https://cs.stanford.edu/~jiaxuan/files/Jiaxuan_AAAI17.pdf.
Monali P., Santosh V. & Ashok V. (2015). Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach. International Conference on Computational Intelligence and Communication Networks. 766-771. 10.1109/CICN.2015.156.
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 (INDIA-Com), 3112-3117.
Pandey, A. & Mishra, A. (2017). Application of artificial neural networks in yield prediction of potato crop. Russian Agricultural Sciences. 43. 266-272. 10.3103/S1068367417030028.
Kuanr, M., Rath, B. K. & Mohanty, S. N. (2018), “Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model”, International Journal of Engineering & Technology, 7 (4.15) 277-280.
Reddy, K. A., & Kumar, K. A. (2018). Recommendation System a Collaborative Model for Agriculture. Available at URL: https://www.semanticscholar.org/paper/Recommendation-System-A-Collaborative-Model-for-Reddy-Kumar/173c7688bc6c86f6bdf122807a6e18f4ff9e0ae3.
Raja S. K. S., Rishi R., Sundaresan E. & Srijit V. (2017). "Demand based crop recommender system for farmers," 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, 2017, pp. 194-199.
Pudumalar S., Ramanujam E., Rajashree R. H., Kavya C., Kiruthika & Nisha J. (2016). Crop recommendation system for precision agriculture. 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, 2017, pp. 32-36.
El-Bendary N., Elhariri E., Hazman M., Saleh S. M. & Hassanien A. E. (2016). Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt Procedia Computer Science Volume 96, Pages 110-119.
Sangeeta & Shruthi G (2019) Survey on Crop Yield Recommender System in Agriculture. International Journal of Scientific Research and Review Volume 07, Issue 05.
Viviliya B. & Vaidhehi V (2019). The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: Volume-9 Issue-2, 2278-3075.
Lakshmi N., Priya M., Sahana S. & Manjunath C. R. (2018). CropRecommendation System for Precision Agriculture. International Journal for Research in Applied Science & Engineering. 6 (5). http://doi.org/10.22214/ijraset.2018.5183.
Madhusree, K., Bikram, K. R. & Sachi, N. M. (2018). Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model. International Journal of Engineering & Technology, 7 (4.15) 277-280.
Mohmmad S. & Ali, A. M. (2018) A Survey on Agriculture Crop Recommender System. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 7 (2).
Veenadhari, S., Misra, B. & Singh, C. D. (2014). Machine learning approach for forecasting crop yield based on climatic parameters. International Conference on Computer Communication and Informatics, 1-5.
Maeda, Y., Goyodani, T., Nishiuchi, S. & Kita, E. (2018). Yield Prediction of Paddy Rice with Machine Learning. In Proceeding of Int'l Conf. Par. and Dist. Proc. Tech. and Appl. (PDPTA'18), 361-365.
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.
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