Machine Learning as a Tool for Crop Yield Prediction

Abstract—The possibilities of using machine learning for estimating the effect of the complex of weather and agrotechnical factors on the yield of agricultural crops and for yield forecasting were investigated. Numerical simulations were carried out using materials of long-term field experiments in the forest-steppe zone of Kemerovo oblast. Continuous observation data for 2013–2018 for the main crops, wheat and barley, were used to train the model. The Random Forest Classifier machine-learning algorithm was used for calculations. The accuracy was defined as the ratio of the number of correct predictions for the test sample to the total number of test cases. When the model was trained using information on current agricultural practices and weather conditions of the previous year (average monthly temperatures and precipitation) as input data, the accuracy for wheat was 0.81, 0.87 for barley, and 0.84 on average for the crops. To estimate the information content in the data of weather fluctuations of the previous year and the effect of the agronomic factors of the current year on the accuracy of the yield forecast, the model was trained in two alternative ways using modified input data. In one case, we considered only the weather image of the previous year based on the monthly average temperature and precipitation. The second case accounted for the agricultural practices used, while the weather data were reduced to just one value, the average annual temperature. The accuracy for the crop (without distinction between barley or wheat) in the case of considering only the weather factors of the previous year was 0.7. In the case of accounting mainly for agricultural practices, with minimal consideration of weather factors, the accuracy was 0.73. These results suggest that each of the groups of factors considered (weather for the previous period and planned agricultural practices) made a comparable contribution to the expected accuracy of the yield forecast.