Wheat is an agricultural product with strategic importance. Until now, fights against the sunn pest was an intensive work that requires experts to regularly visit the extensive wheat plantations every year and to conduct detailed reviews on these areas. In our project, models were developed by using the artificial intelligence, machine learning and data mining, which are among the most popular technological headings of today, to associate meteorological data with the life cycle of sunn pest. With these models, when the sunn pest were in the wintering season, when they moved towards the wheat fields, when they lay eggs on the wheat fields, when the "nimf"s (little sunn pest emerged from eggs) from the eggs reached the maturity period and intensity could be found using the meteorological data. Therefore, it is possible to determine the most suitable time for spraying the fields with very high accuracy rates using these data. Meteorological data are automatically collected daily from the meteorological stations located in the areas where sunn pests live and wheat plantations with the software developed by using the internet connection. Predictions are made based on these data, and according to the prediction results, the necessary precautions can be transmitted to farmers, experts and all other interested persons via email and / or SMS messages. Raw data, processed data, daily and past estimates, and log data saved during the operations of the software are kept in the database and all information can be accessed through the website developed within the scope of the project. A scheme summarizing the operation of the system is presented below.