Development of Strategic Wheat Crop Prediction Toolkit Using Artificial Intelligence Algorithms to Reduce Food Security Risks (Case Study: Alborz Province)

Document Type : Research Paper

Authors

1 Irrigation and Development Engineering Department, College of Agriculture and Natural Resources, University of Tehran, Karaj.

2 Phd Candidate of Water Resources Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran.

10.22059/ijswr.2022.342638.669260

Abstract

Wheat as the main food of the people in the country is of particular importance. Wheat is not only an important economic agricultural commodity in the world, but also known as a powerful lever in political and global relations. Therefore, the analysis and forecast of the production status of this product in the country has always been the focus of attention. The purpose of this study is to predict the amount of wheat yield (X) using artificial intelligence in the annual time scale in Alborz province. For this purpose, using annual cultivation and production data, wheat yield was investigated in six cities of Nazarabad, Savojbalagh, Karaj, Eshtehard, Fardis and Taleghan with a period of 40 years (1981-2020). After calculating the performance (ton per hectare) and forming an annual time series, using four artificial intelligence methods including the best neighbor algorithm (KNN), backup vector (SVM), gene expression planning (GEP) and Bayesin Network (BN) Wheat yields were predicted the following year. Results indicate increased predictions in the years with more production; According to the results of the BN, GEP, SVM and KNN model, the correlation coefficient between the observed and anticipated wheat yield values was 0.84, 0.89, 0.89 and 0.92, respectively. Explaining that Karaj and Taleghan cities have the highest and lowest wheat production respectively. The results showed that the KNN method was best accuracy over other methods, and its assessment criteria of R, RMSE and MAE varyed from 0.84 to 0.92, 0.21 to 0/24 and 0.11 to 0.18. Overall, by comparing the methods used, the KNN method, the best and the BN method, had the least accuracy to predict the amount of wheat yield in Alborz province. The results of this study can be very useful in providing and managing food security in areas under study.

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