Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network.

Li Ma, Lei Zhao, Liying Cao, Dongming Li, Guifen Chen, Ye Han

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Intelligent Monitoring and Trend Analysis of Surface Soil Organic Carbon in the Black Soil Region Using Multi-Satellite and Field Sampling: A Case Study from Northeast China.Sensors (Basel, Switzerland) · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Li MaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Lei ZhaoCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Liying CaoCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.ORCID 0000-0001-7413-8385

Dongming LiCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Guifen ChenInstitute of Technology, Changchun Humanities and Sciences College, Changchun 130118, China.

Ye HanCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Funding

Jilin Province Ecological Environment Department Project 2021-07Jilin Province Science and Technology Department No. 20200301047RQJilin Provincial Department of Education Project No. JJKH20210336KJ,Jilin Provincial Natural Science Foundation No. 20200201288JCNational Natural Science Foundation of China u19a2061The science and technology research project of "13th Five-" of the Education Department of Jilin province No. JJKH20200330KJ

6 · The paper itself

Abstract

Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R

Indexed as

Neural Networks, ComputerSoilLinear ModelsRegression AnalysisSupport Vector MachineSoildeep neural networksintelligent agricultureremote sensingsoil organic matter

Identifiers

PMID36298127

DOI10.3390/s22207777

PMCPMC9610480

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.