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Exploring the suitability of machine learning algorithms for crop yield forecasting using weather variables

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Title Exploring the suitability of machine learning algorithms for crop yield forecasting using weather variables
Not Available
 
Creator Manoj Varma
K. N. Singh
Achal Lama
 
Subject Machine learning
random forest
support vector regression
weather indices
 
Description Not Available
Crop yield forecast is valuable to many players in the agri-food chain, including agronomists, farmers, policymakers and
merchants of commodities. Machine learning may be used to estimate crop yields, as well as to decide what crops to sow and
what to do during the growing season. In present study Machine learning techniques such as Random Forest Regression and
Support Vector Regression has been applied on three different datasets. Statistical indicators like Root Mean Square Error
(RMSE), Mean Absolute Prediction Error (MAPE), and Mean Absolute Deviation (MAD) were used to compare the suggested
models’ forecasting performance. Also comparison has been done of both the machine learning techniques with the stepwise
regression method. Support Vector regression was observed as the best machine learning technique. However, performance of
the popular statistical approach (Stepwise regression) was found to be in between the two-machine learning algorithm.
Not Available
 
Date 2022-05-04T09:33:54Z
2022-05-04T09:33:54Z
2022-04-30
 
Type Research Paper
 
Identifier Not Available
2349-9400
http://krishi.icar.gov.in/jspui/handle/123456789/71896
 
Language English
 
Relation Not Available;
 
Publisher CROP AND WEED SCIENCE SOCIETY (CWSS)