Article(id=1321893413591568510, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755792000000, receivedDateStr=2025-08-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093549391, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093549391, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093549391, creator=13701087609, updateTime=1793093549391, updator=13701087609, issue=Issue{id=1321893331622286028, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='7', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093529849, creator='13701087609', updateTime=1793094587578, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897768357048353, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897768357048354, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=283, endPage=292, ext={EN=ArticleExt(id=1321893413893558399, articleId=1321893413591568510, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Dynamic Diagnosis Method for Mine Reclamation Soil Quality Based on the Fusion of Swin-Unet and Grad-CAM, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the problems that existing diagnosis methods can hardly capture the long-term variation law of mine soil quality, the evaluation of reclamation effect lags behind, and the soil quality diagnosis results lack interpretability, an improved dynamic diagnosis model for mine reclamation soil quality based on Swin-Unet and Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization technology was proposed (IPPSUG model). Raw soil image features were extracted by the model via Swin-Unet, and noise was suppressed while key features were highlighted with the squeeze-and-excitation network module. Meanwhile, the model structure was optimized by the pruning algorithm to reduce computational complexity. Finally, the interpretability of diagnosis results was improved by Grad-CAM. The test results show that the accuracy and precision of soil quality diagnosis of the PSSU model reach 96.11% and 97.63%, respectively. The determination coefficient of IPSSUG model for identifying available potassium content is 0.983, and the recognition rate of soil-polluted areas is 98.40%. Test data are superior to those of the comparison models, which fully demonstrates the feasibility and superiority of this model. In addition, the degree of correlation between main influencing factors and soil quality indicators was quantified by IPSSUG model, the reliability of soil quality diagnosis results was improved, and a new method was provided for research on mine reclamation soil quality diagnosis.

, authors=Junhuan Lu, Haojie Qin, Shuzhi Zhang, Yongjin Song, authorsList=Junhuan Lu, Haojie Qin, Shuzhi Zhang, Yongjin Song, authorCompany=null, correspAuthors=Haojie Qin, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321893414254268544, articleId=1321893413591568510, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于Swin-Unet与Grad-CAM融合的矿山复垦土壤质量动态诊断方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

为解决现有诊断方法难以捕捉矿山土壤质量的长期变化规律、复垦效果评估滞后、土壤质量诊断结果缺乏可解释性等问题，提出了一个基于Swin-Unet与梯度加权类激活映射（Grad-CAM）可视化技术的改进矿山复垦土壤质量动态诊断模型（IPSSUG模型）。该模型通过Swin-Unet提取土壤图像原始特征，借助挤压和激励网络模块抑制噪声并突出关键特征；同时采用剪枝算法优化模型结构，降低计算复杂度；最后利用梯度加权类激活映射提高诊断结果的可解释性。试验结果表明，PSSU模型土壤质量诊断的准确率和精确率分别为96.11%、97.63%；IPSSUG模型识别速效钾含量的决定系数为0.983，土壤污染区域的识别率为98.40%，试验数据均优于对比模型，充分证明了该模型的可行性与优越性。此外，通过IPSSUG模型量化了主要影响因素与土壤质量指标的关联程度，提高了土壤质量诊断结果的可靠性，为矿山复垦土壤质量诊断研究提供了新方法。

, authors=卢俊寰, 覃豪杰, 张树志, 宋勇进, authorsList=卢俊寰, 覃豪杰, 张树志, 宋勇进, authorCompany=null, correspAuthors=覃豪杰, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=V9SALEXrfR+VFE2ZZHpVug==, magXml=PA4GMaAD2cjT25cQWkb/Xg==, pdfUrl=null, pdf=9r+91BsNHIhsoqzsihz0Jg==, pdfFileSize=2792931, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=OWLS8li/rORi6zNVoZ22Iw==, mapNumber=null, fund=null)}, authors=[Author(id=1321893414627561605, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893413591568510, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=13356961217@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893416284311687, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893413591568510, authorId=1321893414627561605, language=EN, stringName=Junhuan Lu, firstName=Junhuan, middleName=null, lastName=Lu, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Guangxi Natural Resources Vocational and Technical College, Chongzuo, Guangxi 532199, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893416372392072, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893413591568510, authorId=1321893414627561605, language=CN, stringName=卢俊寰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=广西自然资源职业技术学院，广西 崇左市　532199, bio={"content":"

卢俊寰（1985—），女，瑶族，广西桂林人，硕士，副教授，主要从事土壤性状及耕地保护政策研究。E-mail:13356961217@163.com

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卢俊寰（1985—），女，瑶族，广西桂林人，硕士，副教授，主要从事土壤性状及耕地保护政策研究。E-mail:13356961217@163.com

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