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Indian Harvest Yield Forecast utilizing Relapse Models

Pushpa S

Abstract


Horticulture is the foundation of our country. Tragically, there are exceptionally draconian circumstances for Indian ranchers to make due. We anticipate the yield to be developed as per the locales where ranchers develop. We for the most part utilize clear boundaries like State,district, season, region and consequently, the client can anticipate the yield of the harvest in the year ofhis or her decision. By utilizing progressed relapse strategies to foresee performanceand utilizes the idea of stacking relapse toimprove the calculations to give better prescient outcomes.


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References


“data.gov.in.” [Online]. Available: https://data.gov.in/

Ananthara, M. G., Arunkumar, T., & Hemavathy, R. (2013, February). CRY—an improved crop yield prediction model using bee hive clustering approach for agricultural data sets. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (pp. 473-478). IEEE.

Awan, A. M., & Sap, M. N. M. (2006,

April). An intelligent system based on kernel methods for crop yield prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 841-846). Springer,

Berlin, Heidelberg.

Bang, S., Bishnoi, R., Chauhan, A. S., Dixit, A. K., & Chawla, I. (2019, August). Fuzzy Logic based Crop Yield Prediction using Temperature and Rainfall parameters predicted through ARMA, SARIMA, and ARMAX models. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (pp. 1-6). IEEE.

Bhosale, S. V., Thombare, R. A., Dhemey, P. G., & Chaudhari, A. N. (2018, August). Crop yield prediction using data analytics and hybrid approach. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1- 5). IEEE.

Gandge, Y. (2017, December). A study on various data mining techniques for crop yield prediction. In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT) (pp. 420-423). IEEE.

Gandhi, N., Petkar, O., & Armstrong,

L. J. (2016, July). Rice crop yield

prediction using artificial neural networks. In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 105-110). IEEE.

Gandhi, N., Armstrong, L. J., Petkar, O., & Tripathy, A. K. (2016, July). Rice crop yield prediction in India using support vector machines. In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1- 5). IEEE.

Gandhi, N., Armstrong, L. J., & Petkar, O. (2016, July). Proposed decision support system (DSS) for Indian rice crop yield prediction. In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 13-18). IEEE.

Islam, T., Chisty, T. A., & Chakrabarty, A. (2018, December). A deep neural network approach for crop selection and yield prediction in Bangladesh. In 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-6). IEEE.


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