Soybean growth vigour predication method based on satellite remote sensing data

Abstract

The invention discloses a soybean growth vigour predication method based on satellite remote sensing data. The method comprises the following steps: (1) collecting remote sensing data of soybean by aremote sensing satellite in a designated soybean monitoring area; (2) preprocessing the collected soybean remote sensing data; (3) extracting growth vigour information from the processed soybean growth vigour remote sensing data and analyzing the growth vigour condition. According to the invention, the growth vigour information of the soybean is acquired in the designated soybean monitoring area by the remote sensing satellite, and data processing is performed, and by extracting the growth vigour information of leaf area and leaf color of the soybean and analyzing, the growth vigour conditionof the soybean can be accurately acquired, so as to provide guidance for production.

Description

technical field [0001] The invention relates to the technical field of satellite remote sensing, in particular to a soybean growing condition prediction method based on satellite remote sensing data. Background technique [0002] Soybean is one of the main grain and oil crops in my country. At present, my country's soybean planting area, output and export volume have been in the leading position in the world. At the same time, my country has researched and promoted suitable high-yield supporting technologies and field management measures in different ecological regions. my country is the hometown of soybeans and a traditional exporter of soybeans. Due to the continuous improvement of people's living standards in my country, the consumption demand for soybeans continues to be strong. By monitoring the growth of soybeans, we can timely understand the growth status of soybeans, diseases and insect pests or crop nutritional status, so as to guide people to take corresponding ...

AI Technical Summary

Problems solved by technology

[0005] 1. It takes a lot of manpower, and the efficiency is extremely low

[0006] 2. Observers are required to have rich experience and farming knowledge, and usually only qualitative conclusions can be given. Therefore, the observation results are highly subjective and cannot reflect the actual growth of crops more objectively.