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Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.)

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Title Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.)
Not Available
 
Creator Pankaj Das
Girish Kumar Jha
Achal Lama
Rajender Parsad
 
Subject Soft computing
MARS
SVM
ANN
Hybrid approach
 
Description Not Available
This paper introduces a novel hybrid approach, combining machine learning algorithms
with feature selection, for efficient modelling and forecasting of complex phenomenon governed by
multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits
of the soft computing algorithm multivariate adaptive regression spline (MARS) for feature selection
coupled with support vector regression (SVR) and artificial neural network (ANN) for efficiently
mapping the relationship between the predictors and predictand variables using the MARS-ANN
and MARS-SVR hybrid frameworks. The performances of the algorithms are com-pared on different
fit statistics such as RMSE, MAD, MAPE, etc., using numeric agronomic traits of 518 lentil genotypes
to predict grain yield. The proposed MARS-based hybrid models outperformed individual models
such as MARS, SVR and ANN. This is largely due to the enhanced feature ex-traction capability
of the MARS model coupled with the nonlinear adaptive learning ability of ANN and SVR. The
superiority of the proposed hybrid models MARS-ANN and MARS-SVM in terms of model building
and generalisation ability was demonstrated.
ICAR-IASRI
 
Date 2023-03-31T09:30:51Z
2023-03-31T09:30:51Z
2023-02-28
 
Type Article
 
Identifier Das, P.; Jha, G.K.; Lama, A.; Parsad, R. Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.). Agriculture 2023, 13, 596. https://doi.org/ 10.3390/agriculture13030596
2077-0472
http://krishi.icar.gov.in/jspui/handle/123456789/76644
 
Language English
 
Relation Not Available;
 
Publisher MDPI