The week's pick
Random Articles
Reseach Article
Survey Paper on Agricultural Dataset for Improving Crop Yield Prediction using Machine Learning Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 46 |
| Year of Publication: 2023 |
| Authors: Atul Tripathi, Bhawani Singh Rathore, Divakar Singh |
10.5120/ijca2023922571
|
Atul Tripathi, Bhawani Singh Rathore, Divakar Singh . Survey Paper on Agricultural Dataset for Improving Crop Yield Prediction using Machine Learning Algorithms. International Journal of Computer Applications. 184, 46 ( Feb 2023), 28-34. DOI=10.5120/ijca2023922571
Abstract
By being able to anticipate crop yields more precisely than what has already been done in the field, we hope to improve precision agriculture. A system that examines a data set that includes crop, cost of cultivation, cost of production, and crop yield from the past, builds a statistical model through learning, and then tries to assist farmers in making accurate and precise decisions about which crops can be grown profitably in the near future can be created with the aid of machine learning techniques and the appropriate optimizations and fine-tuning of the classifying algorithm.As a consequence of this work, the system would have a set of guidelines (referred to as a "Knowledge base," which learns through additional training and data sets) that aid farmers in selecting the crops that are most likely to yield a profit in the current environment. The method used to categories agricultural datasets by crop, area (Quantel/hectar), and production. Here, we investigate our classification algorithms with the aid of the WEKA tool. There is currently a push to transform the vast amounts of agricultural data into various technologies and make them accessible to farmers so they can make better decisions. This survey study investigates the best machine learning algorithms, including Random Tree, J48, Bayes Net, and KStar. We research machine learning methods to uncover relevant data in the agricultural dataset so that we may more accurately forecast crop yields for important crops.
References
- Dr Shirin Bhanu Koduri, Loshma Gunisetti, Ch Raja Ramesh, K V Mutyalu and D. Ganesh,” Prediction of crop production using AdaBoost regression Method”, International conference on computer vision and machine learning, Conf. Series 1228 (2019) 012005.
- Kusum Lata,Sajidullah S Khan ”Proactive Crop Supervision with Machine Learning Algorithms for Yield Improvement”. April 2020 International Journal of Computer Trends and Technology 68(4):14-21
- Marcello Donatelli, Amit Kumar Srivastava, Gregory Duveiller, Stefan Niemeyer and Davide Fumagalli,” Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe”, Environmental Research Letters, vol. 10, no. 7, Jul 2015, Art. No. 075005.
- Rakesh Kumar, M.P. Singh, Prabhat Kumar, J.P. Singh, ”Crop Selection Method to maximize crop yield rate using machine learning technique”,2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM),27 August 2015
- Report on Economic Survey of Maharashtra 2012-2013, Directorate of Economics and Statistics, Planning Department, Government of Maharashtra, Mumbai (2013)
- D. Diepeveen and L. Armstrong, “Identifying key crop performance traits using data mining” World Conference on Agriculture, Information and IT, 2008.
- Alexander Murynin, Konstantin Gorokhovskiy and Vladimir Ignatie,“Efficiency of crop yield forecasting depending on the moment of prediction based on large remote sensing data set” retrievedfromhttp://worldcompproceedings.com/proc/p2013/DMI80 3 6.pdf.
- Hemageetha, N., “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), pp.3112-3117, 2016.
- Wu Fan, ChenChong, GuoXiaoling, Yu Hua, Wang Juyun. Prediction of crop yield using big data. 8th International Symposium on Computational Intelligence and Design (ISCID).2015; 1, 255- 260.
- Monali Paul, Santosh K. Vishwakarma, Ashok Verma. Analysis of soil behavior and prediction of crop yield using data mining approach. Computational Intelligence and Communication Networks (CICN). 2015; 766-771.
- Subhadra Mishra, Debahuti Mishra, GourHariSantra,” Applications of machine learning techniques in agricultural crop production: a review paper. Indian Journal of Science and Technology.2016, 9(38), 1-14
- AakunuriManjula, Dr.G .Narsimha (2015), ‘XCYPF: A Flexible and Extensible Framework for Agricultural Crop Yield Prediction’, Conference on Intelligent Systems and Control (ISCO)
- Monali Paul, Santhosh K. Vishwakarma, Ashok Verma, “Prediction of crop yield using Data Mining Approach” Computational Intelligence and Communication Networks (CICN), International Conference 12-14 Dec. 2015.
- Tng Zhang, "Solving large scale linear prediction problems using stochastic gradient descent algorithms", Proceedings of the twenty-first international conference on Machine Learning. Shweta Srivastava, Diwakar Yagysen,”Implementaion of Genetic Algorithm for Agriculture System”, International Journal of New Innovations in Engineering and Technology Volume 5 Issue 1 -May 2016.
- R.Kalpana, N.Shanti and S.Arumugam, “A survey on data mining techniques in Agriculture”, International Journal of advances in Computer Science and Technology, vol. 3, No. 8,426- 431, 2014.
- AakunuriManjula, G. Narsimha, "XCYPF: A Flexible and Extensible Framework for Agricultural Crop Yield Prediction", IEEE Sponsored 9th ISCO, 2015.
- Rossana MC, L. D. (2013). A Prediction Model Framework for Crop Yield Prediction. Asia Pacific Industrial Engineering and Management Society Conference Proceedings Cebu, Philippines, 185.
- Shruti Mishra, Priyanka Paygude,Snehal Chaudhary, Sonali Idate, "Use of data mining in crop yield prediction",2018 2nd International Conference on Inventive Systems and Control (ICISC).
Index Terms
Keywords