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Rapid and accurate detection of total nitrogen in the different types for soil using laser-induced breakdown spectroscopy combined with transfer learning

文献类型： 外文期刊

作者： Lin, Peng 1 ; Song, Changbo 1 ; Yang, Chongshan 1 ; Zhang, Mengjie 1 ; Ma, Shixiang 1 ; Wen, Jiangtao 5 ; Dong, Daming 1 ; Han, Yuxing 3 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Sensors, Hefei, Peoples R China

3.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China

4.South China Agr Univ, Coll Artificial Intelligence, Coll Elect Engn, 486 Wushan Rd, Guangzhou 510642, Peoples R China

5.Tsinghua Univ, Res Inst, Shenzhen, Peoples R China

关键词： Laser-induced breakdown spectroscopy; Transfer learning; Soil total nitrogen sensing; Sample preprocess; Interpretation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 226 卷

页码：

收录情况： SCI

摘要： Precision fertilizing is crucial not only for enhancing fertilizer efficiency but also for protecting the environment. The rapid sensing of total soil nitrogen (TN) constitutes a key aspect of precision fertilization. Currently common methods, such as the Kjeldahl method, are not suitable for on-site applications. Laser-induced breakdown spectroscopy (LIBS), celebrated for its expeditious data acquisition and high precision, has seen widespread deployment in rapid soil sensing. However, the time-consuming sample preprocessing stage restricts the on-site application of LIBS. In this study, we employed a powder adhesion (PA) method to shorten the preprocessing cycle to 3 min. A transfer learning approach named TransLIBS is introduced to ensure the estimation performance of PA. Compared to the calibration model directly developed on the target domain, the transferred model by TransLIBS elevates R2 V by 0.134 and diminishes RMSEV by 0.312 g kg-1. The F-test method is leveraged to identify active variables, and feature map visualization is employed to interpret the transfer mechanism of the TransLIBS approach. The visualization results highlight the most influential variables situated in the 212-310 nm and 391-395 nm range. Transfer learning has advanced the application of LIBS in soil, providing more opportunities for on-site LIBS detection.

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