Abstract
Crop yield is directly dependent on climatic and weather conditions. A lot of research has been done studying the dependency of weather on crop yield. Crop prediction models have proven to be successful in increasing the crop yield. Soil parameters and atmospheric parameters are used by the models to predict the suitable crop. Parameters such as type of soil, pH, phosphate, potassium, organic carbon, sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity have shown to influence the yield of crop. In this paper, we review the research conducted by several researchers in this direction with a logical conclusion.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dahikar, S.S., Rode, S.V.: Agricultural crop yield production using artificial neural networks. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 2(1), 683–686 (2014)
Shahane, S.K., Tawale, P.V.: Prediction on crop cultivation. In: Int. J. Adv. Res. Comput. Sci. Electron. Eng. (IJARCSEE) 5(10) (2016)
Honawad, S.K., Chinchali, S.S., Pawar, K., Deshpande, P.: Soil classification and suitable crop prediction. In: National Conference on Advances in Computational Biology, Communication, and Data Analytics, pp. 25–29 (2017)
Hiremath, P.S., Shivashankar, S.: Wavelet based features for texture classification. GVIP J. 6(3) (2006)
Ramana Reddy, B.V., Suresh, A., Radhika Mani, M., Vijaya Kumar, V.: Classification of textures based on features extracted from preprocessing images on random windows. Int. J. Adv. Sci. Technol. 9 (2009)
Kanjana Devi, P., Shenbagavadivu, S.: Enhanced crop yield prediction and soil data analysis using data mining. Int. J. Modern Comput. Sci. 4(6) (2016)
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T.: The DSSAT cropping system model. Eur. J. Agron. 18(3), 235–265 (2003)
Fernando, M.T.N., Zubair, L., Peiris, T.S.G., Ranasinghe, C.S., Ratnasiri, J.: Economic value of climate variability impacts on coconut production in Sri Lanka. In: AIACC Working Papers, Working Paper No. 45 (2007)
Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B.: Wheat yield forecasting for Punjab province from vegetation index time series and historic crop statistics. Remote Sens. 6 (2014)
Ji, B., Sun, Y., Yang, S., Wan, J.: Artificial neural networks for rice yield prediction in mountainous regions. J. Agric. Sci. 145, 249–261 (2007)
Gholap, J., Ingole, A., Gohil, J., Gargade, S., Attar, V.: Soil data analysis using classification techniques and soil attribute prediction. Int. J. Comput. Sci. Issues 9(3) (2012)
Kushwaha, A.K., Bhattachrya, S.: Crop yield prediction using agro algorithm in Hadoop. Int. J. Comput. Inf. Technol. Secur. 5(2) (2015)
Acknowledgements
The authors are grateful to Dr. Anitha C, Assistant Professor, Department of Computer Science & Engineering, The National Institute of Engineering, Mysuru for her guidance and helpful comments. The authors also would like to thank the HOD, Department of CSE and the Principal for their continuous support and encouragement.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Avadhani, S.S., Arun, A.B., Govinda, V., Inamdar, J.S.I. (2019). Crop Prediction Models—A Review. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-1951-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1950-1
Online ISBN: 978-981-13-1951-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)