@inproceedings{77acf74b8fe44c57861a8480212d77c0,
title = "Wheat Growth Assessment for Satellite Remote Sensing Enabled Precision Agriculture",
abstract = "In this paper, a backpropagation (BP) neural network algorithm and a multiple factor regression (MR) algorithm are presented to improve the performance of the prediction of wheat growth. By applying the BP neural network algorithm and the MR algorithm, the corresponding Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD) values can be regressed from the Thematic Mapping (TM) data. The experimental result demonstrates that the designed framework has a better performance, which can effectively predict desired parameters and provide a promising solution for the crop growth monitoring. For finding a better solution for the crop growth monitoring, the performance of the BP neural network and the MR algorithms have been investigated.",
keywords = "Artificial neural network, Multiple factor linear regression, Precision agriculture, Wheat growth assessment",
author = "Yuxi Fang and He Sun and Yijun Yan and Jinchang Ren and Daming Dong and Zhongxin Chen and Hong Yue and Tariq Durrani",
note = "Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 ; Conference date: 20-07-2019 Through 22-07-2019",
year = "2020",
month = apr,
day = "5",
doi = "10.1007/978-981-13-9409-6_275",
language = "English",
isbn = "9789811394089 (hbk)",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Singapore",
pages = "2270--2277",
editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Min Jia and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems",
address = "Singapore",
edition = "1",
}