Digital mapping of soil available phosphorus supported by AI Technology for precision agriculture

被引：0

作者：

Dong, Wen ^{[1
]}

Wu, Tianjun ^{[2
]}

Sun, Yingwei ^{[1
]}

Luo, Jiancheng ^{[1
]}

机构：

[1] Chinese Acad Sci, IRSA, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[2] Changan Univ, Dept Math & Informat Sci, Xian, Shaanxi, Peoples R China

来源：

2018 7TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS) | 2018年

关键词：

geo-parcels; soil mapping; precision agriculture; deep learning; soil available phosphorus; ORGANIC-CARBON; CLASSIFICATION; MODEL;

D O I：

暂无

中图分类号：

S3 [农学（农艺学）];

学科分类号：

0901 ;

摘要：

Precision agriculture has been proposed to improve the sustainability of agriculture and solve the environmental pollution of soil. In precision agriculture process, the management of water and fertilizer is carried out on agricultural operation units. Therefore, acquisition of accurate soil nutrient distribution information is a key step for precision agriculture application and digital soil mapping is an effective technology. Significant progress has been made in digital soil mapping over the past 20 years. However, the current digital soil mapping framework was implemented based on grids, which was not consistent with the operation units of precision agriculture. This paper proposed a geo-parcel based digital soil mapping framework on the support of artificial intelligence technology for precision agriculture application. Two key technologies were studied for the implementation of this framework. Geo-parcels automatic extraction was the basis of this method, and a modified VGG16 network was used for geo-parcels' accurate boundary extraction from high resolution images. Different machine learning methods were attempted to construct the relationship between soil available phosphorus and environment on geo-parcels. We chose an agricultural region in Zhongning County, Ningxia Province as the study area, and the new digital soil mapping framework was applied for soil available phosphorus mapping. This research showed that geo-parcel based digital mapping method could reduce the number of prediction units more than 50% for fine soil mapping, and effectively improve the prediction and application efficiency. This study was an attempt to realize soil mapping based on agricultural operation units for precision agriculture application. The high resolution remote sensing images provide basic data for the realization of this idea and the development of AI technology provides technical support for it. In the future, we will carry out experiments in larger areas to further optimize this method and key technologies for the applications in more complex environments.

引用

页码：26 / 30

页数：5