County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model.

ABSTRACT: Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-sea

SUBMITTER: Sun J 

PROVIDER: S-EPMC6832950 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature