Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Varma, Manoj
- Evaluating the Performance of Crop Yield Forecasting Models Coupled with Feature Selection in Regression Framework
Abstract Views :46 |
PDF Views:42
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 125, No 6 (2023), Pagination: 649-654Abstract
As crop yield is determined by numerous input parameters, it is important to identify the most important variables/parameters and eliminate those that may reduce the accuracy of the prediction models. The feature selection algorithms assist in selecting only those relevant features for the prediction algorithms. Instead of a complete set of features, feature subsets give better results for the same algorithm with less computational time. Feature selection has the potential to play an important role in the agriculture domain, with the crop yield depending on multiple factors such as land use, water management, fertilizer application, other management practices and weather parameters. In the present study, feature selection algorithms such as forward selection, backward selection, random forest (RF) and least absolute shrinkage and selection operator (LASSO) have been applied to three different datasets. Regression forecasting models have been developed with selected features for all the algorithms. The forecasting performance of the proposed models was compared using statistical measures such as root mean square error, mean absolute prediction error and mean absolute deviation. A comparison was made among all the feature selection algorithms. The regression models developed with LASSO, RF and backward selection algorithms were the best for different datasets.Keywords
Crop Yield, Feature Selection, Prediction Models, Regression Framework, Statistical Measures, Weather Indices.Full Text
References
- Singh, K. N., Singh, K. K., Kumar, S., Panwar, S. and Gurung, B., Forecasting crop yield through weather indices through LASSO. Indian J. Agric. Sci., 2019, 89, 540–544.
- Agrawal, R., Jain, R. C. and Jha, M. P., Joint effects of weather variables on wheat yields. Mausam, 1983, 34, 189–194.
- Agrawal, R., Has, C. and Aditya, K., Use of discriminant function analysis for forecasting crop yield. Mausam, 2012, 63(3), 455–458.
- Springenberg, J., Dosovitskiy, A., Brox, T. and Riedmiller, M., Striving for simplicity: the all convolutional net. In 2nd International Conference on Learning Representations, ICLR2014, Banff, AB, Canada, 14–16 April 2014, pp. 1–14; https://arxiv.org/abs/1412.6806.
- Oreski, D., Oreskib, S. and Klicek, B., Effects of dataset characteristics on the performance of feature selection techniques. Appl. Soft Comput., 2017, 52, 109–119.
- Gopal, P. M. and Bhargavi, R., Optimum feature subset for optimizing crop yield prediction using filter and wrapper approaches. Appl. Eng. Agric., 2019, 35, 9–14.
- Balogun, A. O., Basri, S., Abdulkadir, S. J. and Hashim, A. S., Performance analysis of feature selection methods in software defect prediction: a search method approach. Appl. Sci., 2019, 9(13), 2764.
- Suruliandi, A., Mariammal, G. and Raja, S. P., Crop prediction based on soil and environmental characteristics using feature selection techniques. Math. Comput. Modell. Dyn. Syst., 2021, 27(1), 117–140.
- Huang, J. Z., An Introduction to Statistical Learning: With Applications in R, Springer, New York, 2014, pp. 225–282.
- Tibshirani, R., Regression shrinkage and selection via the LASSO. J. R. Stat. Soc., Ser. B, 1996, 58(1), 267–288.
- Breiman, L., Random forests. Mach. Learn., 2001, 45, 5–32.
- Whitmire, C. D., Vance, J. M., Rasheed, H. K., Missaoui, A., Rasheed, K. M. and Maier, F. W., Using machine learning and feature selection for alfalfa yield prediction. AI, 2021, 2, 71–88.
- Bocca, F. F. and Rodrigues, L. H. A., The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Comput. Electron. Agric., 2016, 128, 67–76.
- Agrawal, R. and Mehta, S., Weather based forecasting of crop yields, pests and diseases – IASRI models. J. Indian Soc. Agric. Stat., 2007, 61, 255–263.