Phenological Prediction Algorithm Based on Deep Learning

The prediction of the future climate of the Earth is called phenological prediction, and accurate prediction of climate can be beneficial to avoid harm. The traditional phenological prediction method is not thorough enough to analyze the data, and it is time consuming and labor intensive. But the recent advancements in the field of artificial neural networks, especially in deep learning, have paved a new way for applying computer vision methods to phenological prediction. In this paper we proposes a deep learning algorithm combining FCN and Lstm to improve the accuracy and end-to-end generalization ability of the phenological prediction model. The algorithm use the dataset provided by DataCastle Big Data for training and verification. The data consists of vegetation density maps in a certain area in recent years. The algorithm aims to predict the vegetation density of the region in the future and then infer the future climate based on the future vegetation density. The experimental results show that the algorithm can well study the spatial information and time series information of the data, and accurately predict the vegetation density in different regions, so as to accurately predict the future climate.