Abstract
• A feature selection process was conducted to identify the most significant factors influencing wheat yield prediction. This process involved correlation analysis and principal component analysis (PCA). • Numerous experiments were carried out, evaluating various combinations of variables in the training set, using support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) models. • While climatic variables such as precipitation rate, rainfall, and cooling and heating degree-days can influence wheat yield, their impact is less pronounced in irrigated systems. Nevertheless, monitoring these indicators is recommended to better understand crop conditions. • Normalized difference vegetation index (NDVI), biomass (BM), and harvest index (HI) are proposed as the most suitable indices for improving the accuracy of wheat yield prediction. Plant breeding centers, in their relentless pursuit of more productive and resilient wheat varieties, have generated vast data repositories that are fundamental to ensuring global food security. This study uses these data to develop a wheat grain yield (GY) prediction model, using machine learning techniques such as Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield. One of the major challenges of this research was to find the most relevant variables for predicting wheat yield. Using clustering, feature selection, and variable combination techniques, particularly agronomic variables such as harvest index (HI) and biomass (BM), provided complementary information to the Normalized Difference Vegetation Index (NDVI). This combination, analyzed through the XGBoost model, resulted in an exceptional performance, with an RMSE of 28.5082 (grams/square meter) and an R² of 0.9156, showing the constructive collaboration between these indicators. After a thorough analysis, it was discovered that daily clustering and filtering of climatic variables, especially precipitation rate, were favorable in these types of models.
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Claims 7
Claim · model-written summary
The XGBoost model using NDVI, biomass and harvest index gave an RMSE of 28.5082 grams per square meter for wheat yield prediction.
- Taxon
- wheat
Triticum aestivume0 - Outcome
- RMSEe1
- Value
- 28.5082 (grams/square meter)e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
e1“This combination, analyzed through the XGBoost model, resulted in an exceptional performance, with an RMSE of 28.5082 (grams/square meter)”
Claim · model-written summary
The XGBoost model using NDVI, biomass and harvest index gave an R² of 0.9156 for wheat yield prediction.
- Taxon
- wheatTriticum aestivume0
- Outcome
- R²e2
- Value
- 0.9156e2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
e2“and an R² of 0.9156, showing the constructive collaboration between these indicators”
Claim · model-written summary
NDVI, biomass and harvest index are the most suitable indices for improving the accuracy of wheat yield prediction.
- Taxon
- wheatTriticum aestivume0
- Outcome
- accuracy of wheat yield predictione3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
e3“Normalized difference vegetation index (NDVI), biomass (BM), and harvest index (HI) are proposed as the most suitable indices for improving the accuracy of wheat yield prediction”
Claim · model-written summary
RF and XGBoost-based models accurately predict wheat yield.
- Taxon
- wheatTriticum aestivume0
- Outcome
- predict wheat yielde0
- Qualifier
- RF and XGBoost-based modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
Claim · model-written summary
Daily clustering and filtering of climatic variables, especially precipitation rate, were favourable for these models.
- Taxon
- wheatTriticum aestivume0
- Treatment
- daily clustering and filtering of climatic variablese4
- Outcome
- these types of modelse4
- Direction
- favorablee4
- Qualifier
- especially precipitation ratee4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
e4“daily clustering and filtering of climatic variables, especially precipitation rate, were favorable in these types of models”
Claim · model-written summary
The impact of climatic variables on wheat yield is less pronounced in irrigated systems.
- Taxon
- wheatTriticum aestivume5
- Treatment
- climatic variables such as precipitation rate, rainfall, and cooling and heating degree-dayse5
- Outcome
- wheat yielde5
- Direction
- less pronouncede5
- Setting
- irrigated systemse5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e5“While climatic variables such as precipitation rate, rainfall, and cooling and heating degree-days can influence wheat yield, their impact is less pronounced in irrigated systems”
Claim · model-written summary
Monitoring climatic and crop indicators is recommended to understand wheat crop conditions.
- Taxon
- wheatTriticum aestivume5
- Outcome
- monitoring these indicatorse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e5“While climatic variables such as precipitation rate, rainfall, and cooling and heating degree-days can influence wheat yield”
e6“monitoring these indicators is recommended to better understand crop conditions”