Grain yield prediction model based on the analysis of climate and irrigated area conditions in the wheat grain-filling period

The food problem is a major issue of common concern around the world, and the forecasting of wheat output can help to promote the solution of this problem. This paper analyses the relationship between wheat yield and weather, precipitation and irrigated area; proposes a Bayesian optimized machine model of the XGBoost regression algorithm; and applies it to forecast wheat yield in Uttar Pradesh, India. Second, the stability and robustness of the XGBoost model using the top five parameter combinations of the optimization score were evaluated by cross-validation, and the best model was used to predict wheat yield in Uttar Pradesh, India. Finally, the model is compared with other prediction models, and the tenfold cross-validation experimental results show the effectiveness of the model. Ultimately, by learning from the available data and using the 2022 wheat irrigation data to forecast wheat yield per acre, we obtain a wheat production estimate of 17 930 817t in Uttar Pradesh in 2022, which is a decreasing trend from previous years' wheat output. The impact of extreme heat on total production is somewhat contained by the year-on-year increase in the irrigated area in Uttar Pradesh. Resume Le probleme de l'alimentation est une preoccupation majeure commune du monde, et la prevision de la production de ble peut contribuer a promouvoir la solution de ce probleme. Cet article analyse la relation entre le rendement du ble et les conditions meteorologiques, les precipitations et la zone irriguee, propose un modele bayesien de l'algorithme de regression XGBoost, et l'applique pour prevoir le rendement du ble dans l'Etat d'Uttar Pradesh, en Inde. Ensuite, la stabilite et la robustesse du modele XGBoost utilisant les cinq meilleures combinaisons de parametres du score d'optimisation ont ete evaluees par validation croisee, et le meilleur modele a ete utilise pour prevoir le rendement du ble dans l'Etat d'Uttar Pradesh, en Inde. Enfin, le modele est compare a d'autres modeles de prediction, et les resultats experimentaux de validation croisee par dix montrent l'efficacite du modele. En fin de compte, en apprenant a partir des donnees disponibles et en utilisant les donnees d'irrigation du ble de 2022 pour prevoir le rendement du ble par acre, nous obtenons une estimation de la production de ble de 17 930 817t en Uttar Pradesh en 2022, ce qui represente une tendance a la baisse par rapport a la production de ble des annees precedentes. L'impact de la chaleur extreme sur la production totale est quelque peu contenu par l'augmentation annuelle de la superficie irriguee en Uttar Pradesh.