IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 4 2024
DOI: 10.56201/ijcsmt.v10.no4.2024.pg53.69

---

Knowledge Discovery Model for Predicting Optimal Climatic Conditions for Maize Crop Productivity

Johnson Wito Malgwa, Dr. Asabe Sandra Ahmadu, Ahmed Zainab Tijjani

---

Abstract

This research presents an integrated approach to forecasting the impact of climate on maize output in Nigeria, emphasizing the critical role of predictive modeling and data mining techniques. The study acknowledges the profound influence of climate conditions, particularly temperature, rainfall, soil moisture and humidity, on maize development and yield in agriculture. By examining historical climatic data and its correlation with agricultural outcomes, the research aims to develop a predictive model, the Maize Yield Predictive Model, tailored to Nigeria's agricultural landscape. Leveraging data mining techniques, such as Linear Regression, Decision Trees, Support Vector Machine and K-Nearest Neighbor, the study analyzes meteorological data collected over a Five-year period to predict future climatic conditions and their effects on Maize output. Through meticulous data preprocessing and experimentation with classification algorithms, the optimal predictive model is identified, facilitating strategic planning and decision- making for farmers. The significance of the research lies in its potential to enhance agricultural productivity, profitability, and resilience in the face of climate variability, thereby contributing to the socioeconomic development of Nigeria. From the result of the study: Based on the given metrics, KNN (K-Nearest Neighbors) is the best algorithm for this task due to its lowest RMSPE and high CC, indicating good prediction accuracy despite its longer training time withTime: 0.19 (highest), CC: 0.8604 (second highest), RMSPE: 0.0687 (lowest). Additionally, the study adds to the body of literature on the application of data mining techniques in predicting climate effects on agriculture and on maize yield, paving the way for further empirical research in this domain. Despite its scope limitations, focusing on selected cereal crop and sub-variables, the study provides valuable insights into the intersection of c

keywords:

Agriculture, Data mining, Soil, and Prediction

---

References:

Ahamed, A. T., Mahmoood, N. T., and Hossain, N. (2015). Applying Data mining Techniques to
Predict Annual yield of crops in Different Disctricts in Bangladesh. 2915 IEEE/ACIS 16th
International Conference on Software Engineering, Artificial Intelligence, Networking and
Parallel//Dostributed
Computing
(SNPD)
Japan,
(pp.
1-6).
doi:10.1109/SNPD.2015.7176185
Chekole, A. (2019). Appplication of Data mining tools for Identifying determinant Factors for
crop productivity. International Journal of Computer Applications, 181(42), 16-21.
Folorunsho, O., and Adeyemo, A. B. (2012). Application of Data Mining Techniques in Weather
Prediction and Climate Change Studies. Information Engineering and Electronic Business,
1, 51-59. doi:10.5815/ijieeb.2012.01.07
Harsányi, E., Bashir, B., Arshad, S., Ocwa, A., Vad, A., and Alsalman, A. (2023). Data Mining
and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central
Europe. Agronomy, 13(1297), 3-23. doi:https:// doi.org/10.3390/agronomy13051297
Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques. Morgan
Kaufmann.
Jogannagari, M. R., and Manchala, M. (2020, July 7). Data Mining: Techniques, Tools and its
Kareem, K. (2022). Retrieved September 25, 2023, from dataphyte.com: http:www.dataphyte.com
Khan, F., and Singh, D. (2014). ”Knowledge Discovery on Agricultural Dataset Using Association
Rule Mining. International Journal of Emerging Technology and Advanced Engineering,
5(5), 925-930.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model
selection. IJCAI, 14(2), 1137-1145.
Kuradusenge, M., Hitimana, E., Hanyurwimfura, D.Rukundo, P., Mtonga, K., A., and Uwamahoro
M.,. (2023). Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato
and Maize. Agriculture, 13(225), 2-9.
Lata, K., and Chaudhari, B. (2019). Crop Yield Prediction Using Data Mining Techniques And
Machine Learning Models For Decision Support System. Journal of Emerging
Technologies and Innovative Research, 6(4), 391-396.
Mupangwa, W., Chipindu, L., Nyagumbo, I., Mkuhlani, S., and Sisito, G. (2020, April 24).
Evaluating machine learning algorithms for predicting maize yield under conservation
agriculture in Eastern and Southern Africa. Research Article SN Applied Sciences (2020).
doi: doi.org/10.1007/s42452-020-2711-6
Nigerian Meteorological Society. (2023). Annual Climatic Data Reports. Nigerian
Meteorological Society.
Nanjesh, G. M., Chetan, and Ramya, S. (2019). Rice yield prediction using data mining
technique. International Research Journal of Engineering and Technology (IRJET), 6(4),
4231-4233
Ogunleye,
J.
O.
(2021).
The
Concept
of
Data
Mining.
In
IntechOpen.
doi:http://dx.doi.org/10.5772/intechopen.99417
Paul, M., Vishwakarma, S. K., and Verma, A. (2015). Analysis of Soil Behaviour and Prediction
of Crop yield using Data mining approach. Mathematics 205 International Conference on
Computational
Intelligence
and
Communication
Networks
(CICN).
doi:10.1109/CICN.2015.156
Petre, E. G. (2009). A Decision Tree for Weather Prediction.
Priya, P., Muthaiah, U., and Balamurugan, M. (2019). Predicting yield of the crop using machine
learning algorithm. International journal of engineering sciences and research, 7(4), 1-8.
doi:10.5281/zenodo.1212821
Ramesh, D., and Vardhan, V. (2015). Analysis of Crop Yield Prediction Using Data Mining
Techniques. International Journal of Research in Engineering and Technology, 4(1),
2321-7308. doi:10.15623/ijret.2015.0401071
Sawaitul, S., Wagh, K. P., and Chatur, D. (2012). Classification and Prediction of Future Weather
by using Back Propagation Algorithm- An Approach. International Journal of
EmergingTechnology and Advanced Engineering, 2(1), 110-113.
Siddharth S. Bhatkande, R. G. (2016). Weather Prediction Based on Decision Tree Algorithm
Using Data Mining Techniques. International Journal of Advanced Research in Computer
and Communication Engineering, v(5).
Surya, P., and Aroquiaraj, L. I. (2018). Crop yield prediction in Agriculture using Data Mining
Predictive Analytic Techniques. 5(4), 2348-1269.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine
Learning Tools and Techniques. Morgan Kaufmann.
Zupan, B., and Demsar, J. (2008). Open-Source Tools for Data Mining. Clinics in Laboratory
Medicinee, 28(1), 37-54. doi:10.1016/j.cll.2007.10.002

DOWNLOAD PDF

---

Back