Crop Yield Prediction by Integrating Meteorological and Pesticides Use Data with Machine Learning Methods: An Application for Major Crops in Turkey

Abstract

Agriculture, as one of the most important and vital human activity, is highly vulnerable to global, local and environmental issues. This fragility also surfaced in the initial stages of the COVID-19 pandemic. Accordingly, such matters are considered to have dramatic impacts on demand and pricing dynamics of agricultural products. Nonetheless, improving crop yield and its estimation is the fundamental goal of agricultural activities. To cope with the rapidly changing circumstances, Turkey needs to keep developing data-based agricultural information systems which is also stated as one of the main objectives of the 11th development plan. Therefore, accurate crop yield prediction appears to be a critical task. In this context, using meteorological parameters, pesticides use and crop yield values during 1990-2019, evaluation of machine learning regression methods in the yield prediction of nine major crops in Turkey can be stated as the main aim of this research. After the training, all models are used to predict crop yields and acquired values were compared with actual figures. The results showed that successful predictions were obtained by using the Decision Tree Regression (DTR) and Random Forest Regression (RFR) especially for wheat, barley and maize yields; however, Support Vector Regression (SVR) showed inconsistent predictions.

Keywords

• crop yield prediction
• machine learning
• decision tree regression
• random forest regression
• tarımsal üretim tahmini
• makine öğrenmesi
• karar ağacı regresyon
• rastgele orman regresyon