NDLI: Estimating crop yields with deep learning and remotely sensed data

Content Provider	IEEE Xplore Digital Library
Author	Kuwata, K. Shibasaki, R.
Copyright Year	2015
Description	Author affiliation: Univ. of Tokyo IIS, Tokyo, Japan (Kuwata, K.; Shibasaki, R.)
Abstract	This paper describes Illinois corn yield estimation using deep learning and another machine learning, SVR. Deep learning is a technique that has been attracting attention in recent years of machine learning, it is possible to implement using the Caffe. High accuracy estimation of crop yield is very important from the viewpoint of food security. However, since every country prepare data inhomogeneously, the implementation of the crop model in all regions is difficult. Deep learning is possible to extract important features for estimating the object from the input data, so it can be expected to reduce dependency of input data. The network model of two InnerProductLayer was the best algorithm in this study, achieving RMSE of 6.298 (standard value). This study highlights the advantages of deep learning for agricultural yield estimating.
Starting Page	858
Ending Page	861
File Size	4270415
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7325900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Agriculture Machine learning Remote sensing Feature extraction Meteorology Indexes Data models EVI Deep Learning Caffe MODIS crop yield
Content Type	Text
Resource Type	Article

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