Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques
For global food security, accurate large-scale wheat yield estimates are critical. The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study...
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Association of agrometeorologists
2025-03-01
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| Series: | Journal of Agrometeorology |
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| Online Access: | https://journal.agrimetassociation.org/index.php/jam/article/view/2807 |
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| author | KAVITA JHAJHARIA |
| author_facet | KAVITA JHAJHARIA |
| author_sort | KAVITA JHAJHARIA |
| collection | DOAJ |
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For global food security, accurate large-scale wheat yield estimates are critical. The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. The results indicated Support Vector Regression outperformed Long Short-Term Machine in wheat yield prediction. In comparison to coarse-resolution SIF products, the high-resolution SIF product offers superior prediction. The results emphasize that with high-quality SIF the crop predictions can be improved.
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| format | Article |
| id | doaj-art-7ebb1a19f6ef46568948666cf04e3440 |
| institution | DOAJ |
| issn | 0972-1665 2583-2980 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Association of agrometeorologists |
| record_format | Article |
| series | Journal of Agrometeorology |
| spelling | doaj-art-7ebb1a19f6ef46568948666cf04e34402025-08-20T03:07:45ZengAssociation of agrometeorologistsJournal of Agrometeorology0972-16652583-29802025-03-0127110.54386/jam.v27i1.2807Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniquesKAVITA JHAJHARIA0Department of Information Technology, Manipal University Jaipur, India For global food security, accurate large-scale wheat yield estimates are critical. The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. The results indicated Support Vector Regression outperformed Long Short-Term Machine in wheat yield prediction. In comparison to coarse-resolution SIF products, the high-resolution SIF product offers superior prediction. The results emphasize that with high-quality SIF the crop predictions can be improved. https://journal.agrimetassociation.org/index.php/jam/article/view/2807Remote sensingCrop yield predictionMachine learningDeep learningGlobal Ozone Monitoring Experiment-2 (GOME-2) |
| spellingShingle | KAVITA JHAJHARIA Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques Journal of Agrometeorology Remote sensing Crop yield prediction Machine learning Deep learning Global Ozone Monitoring Experiment-2 (GOME-2) |
| title | Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques |
| title_full | Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques |
| title_fullStr | Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques |
| title_full_unstemmed | Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques |
| title_short | Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques |
| title_sort | wheat yield prediction of rajasthan using climatic and satellite data and machine learning techniques |
| topic | Remote sensing Crop yield prediction Machine learning Deep learning Global Ozone Monitoring Experiment-2 (GOME-2) |
| url | https://journal.agrimetassociation.org/index.php/jam/article/view/2807 |
| work_keys_str_mv | AT kavitajhajharia wheatyieldpredictionofrajasthanusingclimaticandsatellitedataandmachinelearningtechniques |