Design of Automatic Detection Scheme for Ecological Conditions of Soybean Growth in Facility

  • Chong Zhao
Keywords: Automatic Detection, Growth Ecological Conditions, Scheme Design, Facility Agriculture, Soybean Production

Abstract

As one of the domestic high-quality and high-yield crops, soybeans need to be joined by science and technology
in the construction and development of their facilities, so that facility soybeans can obtain a lot of information
on growing ecological conditions faster, more accurately, and more conveniently. In the process of automated
detection of the growth ecological environment of protected soybeans, it is necessary to design a detection
scheme suitable for the growth ecological conditions of soybeans, so as to promote the rapid and good
development of precision agriculture and also provide other crops with corresponding enlightenment. The
purpose of this paper is to research and design an automatic detection scheme for the ecological conditions of
soybean growth in facilities, which saves manpower and material resources, which is an important topic at
present. In this experiment, four experiments are being designed, including the automatic detection of the
growth ecological conditions of soybeans in the facility, and the growth ecological conditions of corn, wheat,
and triticale are also tested; the artificial test control group and the automatic test soybean are used as controls.
The accurate performance of the automatic detection, and whether the automatic detection all-weather data of
the same growth ecological condition are consistent with the manual detection. Through the experimental
investigation of the controlled variable method and the use of Excel software owned by the computer for data
statistics, the experimental data show that the automatic detection of the growth ecological conditions of
soybean data is consistent with the data obtained manually; the data obtained by professional manual detection
and automated detection Under basically the same circumstances, the labor cost has been reduced by an average
of 70%; the automated detection scheme can be used not only for soybeans, but also for various crops. The
experimental data show that the automatic detection of soybean growth ecological conditions and the
effectiveness of growth management have been realized, which has accurately reduced the production cost by
25%, improved the quality of soybeans by 15%, and added value of soybean products by 10%.

Published
2020-02-01