Crop yield prediction method based on an improved neural network

Abstract

Crop yield forecasting plays a decisive role in the development of national economy planned by the government. It is of great significance to rationally plan planting strategies and reduce the waste of water and fertilizer. There are many factors affecting crop yield, and accurate prediction of crop growth means a lot. Climate is an important factor affecting crop yield. Based on climatic factors, this paper proposes a time series prediction method based on the long short-term memory(LSTM) for crop yield prediction. The method combines historical yields with climatic factors to forecast the crop yield of the next period by years. Experimental results show that compared with LSTM and support vector machine methods, the proposed method achieves higher accuracy in crop yield prediction.

Keywords

• crop yield prediction
• long short-term memory
• deep learning
• recurrent neural network
• climate factor