Comparative Analysis of Data Mining Techniques for Predicting the Yield of Agricultural Crops
Автор: Utshab Das, Hasan Sanjary Islam, Kakon Paul Avi, Ajmayeen Adil, Dip Nandi
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 4 Vol. 15, 2023 года.
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Predicting crop yields is one of the more difficult tasks in the agriculture sector. A fascinating area of research to estimate agricultural productivity has emerged from recent advancements in information technology for agriculture. Crop yield prediction is a technique for estimating crop production based on a variety of factors, including weather conditions and parameters such as temperature, rainfall, fertilizer, and pesticide use. In the world of agriculture, Data mining techniques are extremely popular. In order to predict the crop production for the following year, data mining techniques are employed and evaluated in the agricultural sector. In this paper, we carried out the comparison between Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, and K-Means clustering algorithms to predict crop yield in order to determine which method is most effective at doing so. The results show which algorithm is better suitable for this particular purpose by comparing these data mining algorithms for agricultural crop production and determining which algorithm is more successful for this outcome.
Weka, Data Mining, Crop Yield, Visualization
Короткий адрес: https://sciup.org/15018941
IDR: 15018941 | DOI: 10.5815/ijitcs.2023.04.03
Список литературы Comparative Analysis of Data Mining Techniques for Predicting the Yield of Agricultural Crops
- Mamun Rashid, Bifta Sama Bari, Yusri Yusup, Mohammad Anuar Kamaruddin, “A Comprehensive Review of Crop Yield prediction Using Machine learning approaches with special emphasis on palm oil yield prediction” IEEE Volume 9, April 22, 2021
- N. Gandhi and L.J. Armstrong, "A review of the application of data mining techniques for decision making in agriculture", 2nd InternationalConference on Contemporary Computing and Informatics (ic3i), 2016
- J.W. Jones, J.W. Hasen, F.S. Royce, C.D. Messina, “Potential benefits of climate forecasting to agriculture”, Agriculture, Ecosystem and Environment 82 (2000) Page 169-184.
- J.P. Powell, S. Reinhard, “Measuring the effects of extreme weather events on yields” Weather and Climate Extremes, Volume 12, June 2016, Pages 69-79.
- D Ramesh, B Vishnu Vardhan, “Analysis of crop yield prediction using datamining techniques” International Journal of research in Engineering and Technology.
- Hassina Ait Issad, Rachida Aoudjit, Joel J.P.C Rodrigoues, “A comprehensive review of Data Mining techniques in smart agriculture” Engineering in Agriculture, Environment and Food Volume 12, Issue 4, October 2019, Pages 511-525.
- John A. Miranowski, “Impacts of productivity losses on crop production and management in a dynamic economic model” American Journal of Agriculture Economics Volume 66 Issue 1 page 61-67
- Ramesh A. Medar, Vijay S. Rajpurohit, Anand M. Ambekar, "Sugarcane Crop Yield Forecasting Model Using Supervised Machine Learning", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.8, pp.11-20, 2019. DOI: 10.5815/ijisa.2019.08.02
- B. Milovic and V. Radojevic. “Application of Data Mining in Agriculture”. Bulgarian Journal of Agricultural Science, 21 (No 1) 2015, 26-34.
- Abdul Rehman, Luan Jingdong, Rafia Khatoon, Imran Hussain, Muhammad Shahid Iqbal, “Modern Agricultural Technology Adoption its Importance, Role and Usage for the Improvement of Agriculture” Life Science Journal 2017;14(2)
- Sally Jo Cunningham and Geoffrey Holmes, "Developing innovative applications in agriculture using data mining" in Department of Computer Science University of Waikato Hamilton, New Zealand.
- Zhixi Tian, Jia-WeiWang, Jiayang Li, Bin Han, “Designing future crops: Challenges and strategies for sustainable agriculture, Wiley Online Library, The Plant Journal 2020.
- Jyotshna Solanki, Prof. (Dr.) Yusuf Mulge, “Different Techniques Used in Data Mining in Agriculture”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 5, May 2015 ISSN: 2277 128X.
- J. Wang, S. Kang, J. Sun, Z. Chen, and N. Song, “Spatial prediction of crop water requirement based on Bayesian maximum entropy and multisource data”, Transactions of the Chinese Society of Agricultural Engineering, Vol.33, No.9, pp.99-106, 2017.
- Ramesh, D. and Vishnu Vardhan, B. (2013). Data mining techniques and applications to agricultural yield data. Int. J. Adv. Res. Compu. Communi. Engg., 2(9): 3477-3480.
- B. Milovic and V. Radojevic. Application of Data Mining in Agriculture. Bulgarian Journal of Agricultural Science, 21 (No 1) 2015, 26-34.
- Jun Wu, Anstasiya Olesnikova, Chi-Hwa Song, Won Don Lee, “The development and application of Decision Tree for Agriculture Data” IEEE, 2022.
- Nevena Golubovic, Chandra Krintiz, Rich Wolski, Balaji Sethuramasamyraja, Bo Liu, “A Scalable system for executing and scoring K-Means clustering techniques and its impact on applications in agriculture” pp 163-175, 2019, InderScience online.
- Martin Kuradusenge, Eric Hitimana, Damien Hanyurwimfura, Placide Rukundo, Kambombo Mtonga, Angelique Mukasine, Claudette Uwitonze, Jakson Ngabonziza, Angelique Uwamahoro, “Crop Yield prediction using Machine Learning Models: Case of Iris Potato and Maize” Journals of MBPI, 16 January 2023.
- R. Sujatha, Dr.P.Isakki, Sivakasi, “ A study on crop yield forecasting using classification techniques“ Journals of IEEE, 2017
- Hetal Patel, Dharmendrea Patel, “A comparative study on various data mining algorithms with special reference to crop yield” Indian Journal of science and technology” Vol9(22).
- H. K. Karthikeya, K. Sudarshan, Disha S. Shetty “Prediction of agriculture crops using KNN algorithm” International Journal of Innovative Science and Research Technology, Volume 5, Issue 5, May 2020
- Fabrico Guevara-Viejo, Juan Diego Valenzuela-Cobos, Purificacion Vicenete Ganlido, Purificacion Galindo Villardon, “Application Of K-Means Clustering Algorithm to Commerical Parameters of pleurotus sp. Cultivated On representative agriculture wastefrom provience of guyas” Journals of Fungi, 2021.
- V. Geetha, A.Punthia, M.Abarna, M. Akshaya, S.Illakiya, A.P. Janani “ An effective crop yield prediction using random forest algorithm” Journals of IEEE, 2020.
