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A Survey on Crop Yield Prediction Models


Affiliations
1 M.E. (Computer Science and Engineering), KPR Institute of Engineering and Technology, Arasur, Coimbatore, India
2 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, India
 

Objectives: To analysis various models to improve the prediction of crop yield production.

Methods: In this paper, there are different methods has been analyzed to predict the crop yield. The methods such as artificial neural network, Adaptive Neuro-Fuzzy inference System, Fuzzy Logic and Multi Linear Regression are analyzed to know the best methods for crop yield prediction. The prediction of crop yield varied by internal factors and external factors of crop an environment. The internal factors such as pesticides, water level, spacing and fertilizers and the external factors such as temperature, humidity, and rainfall. There are various models were developed to predict the crop yield prediction. This paper provides detailed information about the different models for crop yield prediction.

Findings: In this paper various models for crop yield prediction are compared through their parameters such as Root Mean Square Error (RMSE), R2,correlation coefficient and Mean Square Error (MSE) to prove Adaptive NeuroFuzzy Inference System (ANFIS)prediction model is better than other techniques.

Application/improvements: The findings of this work prove that the Adaptive Neuro Fuzzy Inference System (ANFIS) prediction model provides better result than other approaches.


Keywords

Crop Yield Prediction, Adaptive Neurofuzzy Inference System, Data Mining, Agriculture.
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Authors

K. Menaka
M.E. (Computer Science and Engineering), KPR Institute of Engineering and Technology, Arasur, Coimbatore, India
N. Yuvaraj
Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, India

Abstract


Objectives: To analysis various models to improve the prediction of crop yield production.

Methods: In this paper, there are different methods has been analyzed to predict the crop yield. The methods such as artificial neural network, Adaptive Neuro-Fuzzy inference System, Fuzzy Logic and Multi Linear Regression are analyzed to know the best methods for crop yield prediction. The prediction of crop yield varied by internal factors and external factors of crop an environment. The internal factors such as pesticides, water level, spacing and fertilizers and the external factors such as temperature, humidity, and rainfall. There are various models were developed to predict the crop yield prediction. This paper provides detailed information about the different models for crop yield prediction.

Findings: In this paper various models for crop yield prediction are compared through their parameters such as Root Mean Square Error (RMSE), R2,correlation coefficient and Mean Square Error (MSE) to prove Adaptive NeuroFuzzy Inference System (ANFIS)prediction model is better than other techniques.

Application/improvements: The findings of this work prove that the Adaptive Neuro Fuzzy Inference System (ANFIS) prediction model provides better result than other approaches.


Keywords


Crop Yield Prediction, Adaptive Neurofuzzy Inference System, Data Mining, Agriculture.

References