GERMPLASM SELECTION BASED ON THE DEPTH LEARNING NETWORK MOBILENET
W. Yin, C. Zhao, and Y. Chen. Agricultural Science: An International journal (AGRIJ), 2 (1):
18(September 2019)
Abstract
With the agricultural Internet of Things technology, real-time images of tomato plants can be obtained
and processed through the remote video surveillance system. The image processing technology and the
conventional neural network (CNN) based on visual algorithms will be used to process the collected
images so as to accomplish the acquisition, processing and analysis of physiological indexes of tomato
plants, which can identify the growth status of plants by breeading good traits and obtaining
high-quality germplasm resources, improve agricultural production effciency and mitigate the loss
caused by pest, insufficient of nutrition of soil and so forth.
%0 Journal Article
%1 noauthororeditor
%A Yin, Wu
%A Zhao, Chen
%A Chen, Yutian
%D 2019
%J Agricultural Science: An International journal (AGRIJ)
%K agriculture food fruit
%N 1
%P 18
%T GERMPLASM SELECTION BASED ON THE DEPTH LEARNING NETWORK MOBILENET
%U https://airccse.com/agrij/index.html
%V 2
%X With the agricultural Internet of Things technology, real-time images of tomato plants can be obtained
and processed through the remote video surveillance system. The image processing technology and the
conventional neural network (CNN) based on visual algorithms will be used to process the collected
images so as to accomplish the acquisition, processing and analysis of physiological indexes of tomato
plants, which can identify the growth status of plants by breeading good traits and obtaining
high-quality germplasm resources, improve agricultural production effciency and mitigate the loss
caused by pest, insufficient of nutrition of soil and so forth.
@article{noauthororeditor,
abstract = {With the agricultural Internet of Things technology, real-time images of tomato plants can be obtained
and processed through the remote video surveillance system. The image processing technology and the
conventional neural network (CNN) based on visual algorithms will be used to process the collected
images so as to accomplish the acquisition, processing and analysis of physiological indexes of tomato
plants, which can identify the growth status of plants by breeading good traits and obtaining
high-quality germplasm resources, improve agricultural production effciency and mitigate the loss
caused by pest, insufficient of nutrition of soil and so forth.
},
added-at = {2022-09-22T08:08:36.000+0200},
author = {Yin, Wu and Zhao, Chen and Chen, Yutian},
biburl = {https://www.bibsonomy.org/bibtex/21eba936329e925ea0e7b388d2eb20acf/agrijjournal},
interhash = {a8adeb9d95ba2bf435d642595fe5fced},
intrahash = {1eba936329e925ea0e7b388d2eb20acf},
journal = {Agricultural Science: An International journal (AGRIJ)},
keywords = {agriculture food fruit},
language = {English},
month = {September},
number = 1,
pages = 18,
timestamp = {2023-02-08T08:18:07.000+0100},
title = {GERMPLASM SELECTION BASED ON THE DEPTH LEARNING NETWORK MOBILENET},
url = {https://airccse.com/agrij/index.html},
volume = 2,
year = 2019
}