Trained neural network to predict paddy yield for various input parameters in Tamil Nadu, India
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Abstract
The major objective of the present study was to explore if Artificial Neural Network (ANN) models with back propagation could efficiently predict the rice yield under various climatic conditions; ground-specific rainfall, ground-specific weather variables and historic yield data. The back propagation algorithm will calculate each expected weight using the error rate as the activity level of a unit was altered. The errors in the model during the training phase were solved during the back-propagation. The paddy yield prediction took various parameters like rainfall, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, and the long-time paddy yield recorded using Artificial Neural Networks. The R2 value on the test set was found to be 93% and it showed that the model was able to predict the paddy yield better for the given data set. The ANN model was tested with learning rates of 0.25 and 0.5. The number of hidden layers in the first layer was 50 and in the second hidden layer was 30. From this, the testing value of R square was 0.97. The observations with the ANN Model showed that i) the best result for the test set was R2 value of 0.98, ii) the two hidden layers kept with 50 neurons in the first layer and 30 neurons in the second one, iii) the learning rate was of 0.25. With all these configurations, maximum yield is possible from the paddy crop.
Article Details
Article Details
Artificial neural networks, Multilayer perceptron, Root mean square error
Chawla, V., Naik, H.S. & Akintayo, A. (2016). A bayesian network approach to county-level corn yield prediction using historical data and expert knowledge. Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water, San Francisco, CA, USA, 2016.
Dahikar, S. & Rode, S. (2014). Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronic Instrumentation and Control Engineering, 2(1), 683-686.
Dakshayini, P. (2017). Rice crop yield prediction using Data mining techniques: An overview. International Journal of Advanced Research in Computer Science and Software Engineering, 7, 427-431.
Davey, T. (2011). Educational testing service. In: A guide to computer adaptive testing systems, Technical issues in large-scale assessment (TILSA), State Collaborative on Assessment and Student Standards (SCASS), 2011.
Dermo, J. (2009). E-Assessment and the student learning experience: A survey of student perceptions of e-assessment. British Journal of Educational Technology, 40(2), 203-214.
Deshpande, M. & Karypis, G. (2004). Selective Markov models for predicting web page accesses. ACM Transactions on Internet Technology, 4, 163-184 ,2004.
Dubey, M. (2011). Effective e-learning Design, Development and Delivery’, University Press, 7.
Gandhi, N., J. Leisa, Armstrong & O. Petkar (2016). Predicting rice crop yield using bayesian networks. Conference on Advances in Computing, Communications and Informatics, Jaipur, India, 2016
Hossain, M.A., Uddin, M.N., Hossain, M.A. & Jang, Y.M. (2017). Predicting rice yield for Bangladesh by exploiting weather conditions, Jeju, South Korea, pp. 1–6
Kalpana, R., Shanti, N. & Arumugam, S. (2014). A survey on data mining techniques in agriculture. International Journal of Advances in Computer Science and Technology, 3(8), 426-431.
Li, Z. & Tian, J. (2003). Testing the suitability of Markov chains as web usage models. Proceedings of 27th annual International Computer Software and Applications Conference, Dallas, TX, USA, 356-361.
Lilley, M. (2007). The development and application of computer adaptive testing in a higher education environment, Ph.D. Thesis, University of Hertfordshire, UK ,2007.
Mariappan, A.K., Austin Ben & Das, J. (2017). A paradigm for rice yield prediction In Tamil Nadu. In: International Conference on Technological Innovations in ICT for Agriculture and Rural Development, Chennai, India, pp. 1–4.
M. Shashi, Y. (2019). Atmospheric temperature prediction using support vector machines. International Journal of Computer Theory and Engineering, 1 (1), 55-58.
Raorane, A.A. & R.V. Kulkarni. (2012). Data mining- An effective tool for yield estimation in the agricultural sector. International Journal of Engineering Trends and Technology, 1(2), 75-79.
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