Article(id=1277254069722944355, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277253949203809194, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-2561.2026.01.023, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755014400000, receivedDateStr=2025-08-13, revisedDate=null, revisedDateStr=null, acceptedDate=1761926400000, acceptedDateStr=2025-11-01, onlineDate=1782450700454, onlineDateStr=2026-06-26, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1782450700454, onlineIssueDateStr=2026-06-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1782450700454, creator=13701087609, updateTime=1782450700454, updator=13701087609, issue=Issue{id=1277253949203809194, tenantId=1146029695717560320, journalId=1235980609244409860, year='2026', volume='47', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1782450671721, creator='13701087609', updateTime=1782451647086, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1277258041263256431, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277253949203809194, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1277258041263256432, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1277253949203809194, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=246, endPage=258, ext={EN=ArticleExt(id=1277254071505523558, articleId=1277254069722944355, tenantId=1146029695717560320, journalId=1235980609244409860, language=EN, title=Monitoring Model for Rubber Tree Mite Infestation Based on Multi- source Remote Sensing, columnId=1236318328365577171, journalTitle=Chinese Journal of Tropical Crops, columnName=Agricultural Ecology & Environmental Protection, runingTitle=null, highlight=null, articleAbstract=

Rubber is a globally significant strategic raw material, playing an irreplaceable role in national defense, transportation, aerospace and medical equipment industries. However, pests such as the rubber tree leaf mite frequently occur in tropical rubber plantations, especially under conditions of high temperature and drought, where they are prone to large-scale outbreaks. Infestations result in symptoms such as leaf chlorosis, yellowing and premature defoliation, which severely compromise tree vitality and substantially reduce latex yield. Traditional pest and disease monitoring relies primarily on manual field surveys, which are time-consuming, labor-intensive, and limited in spatial scope. The constraints make it increasingly difficult to meet the needs of modern rubber plantations for rapid early warning and precise pest management. Remote sensing technology has demonstrated significant advantages in the monitoring of agricultural pests and diseases. It enables large-scale, rapid, non-contact and dynamic monitoring across multiple temporal scales. To address the limitations of conventional monitoring methods, such as low efficiency and inadequate accuracy, this study proposes a multi-source remote sensing approach that integrates ground-based ASD (Analytical Spectral Devices) hyperspectral data, proximal multispectral imagery, and field survey data to achieve rapid and accurate detection of rubber tree mite infestations. The study was conducted based on spectral reflectance data of rubber leaves collected in 2024 from the experimental station of Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences in Danzhou, Hainan province, along with UAV-based multispectral imagery. The raw spectral data were preprocessed using Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) smoothing and first-order derivative transformation. Four types of spectral feature variables were derived, including vegetation indices (VIs), hyperspectral features, continuum removal (CR) parameters and wavelet coefficients. Sensitive features were selected through a combination of the Least Absolute Shrinkage and Selection Operator (LASSO) and Pearson correlation coefficient analysis. Based on the selected features, three regression models, Multiple Linear Regression (MLR), Random Forest (RF) and Back Propagation Neural Network (BPNN), were developed to model rubber tree mite infestations levels. Results indicated that the ASD hyperspectral data yielded the best performance across all models, with the RF and BPNN models performing particularly well. Among them, the RF model achieved the highest coefficient of determination (R²) of 0.8361 and the lowest root mean square error (RMSE) of 0.6353 on the validation dataset, demonstrating a strong capacity for accurate infestation severity prediction. In conclusion, the integration of ASD hyperspectral data with the RF model enables the construction of a high-accuracy, strongly interpretable monitoring model for rubber tree mite infestations. This approach effectively supports early detection and quantitative classification of infestation levels in rubber tree mite infestations, and would provide essential technical guidance and theoretical support for precision plantation management and integrated pest control.

, authors=null, authorsList=Fengzheng CAI, Jingjing WANG, Jinlong ZHU, Yanan YOU, Donghua WANG, Zhiqiang TAO, Bingsun WU, Huichun YE, authorCompany=null, correspAuthors=Jingjing WANG, 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=1277254073141302120, articleId=1277254069722944355, tenantId=1146029695717560320, journalId=1235980609244409860, language=CN, title=基于多源遥感的橡胶树螨害发生监测模型研究, columnId=1236292523270918153, journalTitle=热带作物学报, columnName=农业生态与环境保护, runingTitle=null, highlight=null, articleAbstract=

橡胶是全球重要的战略性基础原料，在国防安全、交通运输、航空航天和医疗器械等领域具有不可替代的作用。然而，橡胶树叶螨等害虫在热带种植区频繁发生，尤其在高温干旱季节易大面积暴发，导致叶片褪绿、枯黄甚至脱落，严重影响树体健康和胶乳产量。传统病虫害监测主要依赖人工调查，存在耗时长、效率低、覆盖面有限等问题，已难以满足现代化橡胶园对快速预警与精准管理的需求，遥感技术在农业病虫害监测中展现出显著优势，能够实现大范围、快速、非接触式的动态监测。本研究针对传统橡胶树螨害监测方法效率低、精度不足等问题，提出融合ASD光谱、近地多光谱和地面调查数据的多源遥感监测方法，以实现橡胶树螨害的快速、精准识别。本研究基于中国热带农业科学院橡胶研究所儋州试验区2024年采集的橡胶叶片ASD光谱数据与无人机遥感影像，采用MSC校正、SG平滑与一阶导数等方法对原始光谱进行预处理，构建植被指数、高光谱特征、连续统去除参数及小波系数四类光谱特征变量，通过LASSO结合Pearson相关系数筛选敏感特征变量，并分别采用多元线性回归（MLR）、随机森林（RF）和反向传播神经网络（BPNN）构建橡胶树螨害监测模型。结果表明：ASD光谱数据在各模型中表现最佳，尤其是在RF和BPNN模型下性能突出，验证集决定系数（R²）最高达0.8361，最低RMSE为0.6353，具备较强的螨害分级预测能力。总体而言，ASD光谱与RF模型的结合可构建高精度、强解释力的橡胶树螨害监测模型，有效支撑橡胶树螨害发生的监测与分级评估，为橡胶林的精准管理和病虫害防控提供重要的技术参考与理论依据。

, authors=, authorsList=蔡丰政, 王晶晶, 朱锦龙, 尤亚楠, 王栋华, 陶志强, 吴炳孙, 叶回春, authorCompany=null, correspAuthors=王晶晶, authorNote=null, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=W4JaRAADFgw/V7t9QhA6vA==, magXml=8bzYvJ/PvcxwqFk1CTiVrg==, pdfUrl=null, pdf=Eqzp95PmCgqJjn/t+irwcg==, pdfFileSize=4878574, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=1V6MG1Dz9wpAPTbx+4tv0g==, mapNumber=null, fund=null)}, authors=[Author(id=1277254076865844095, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1277254069722944355, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1277254077268497283, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1277254069722944355, authorId=1277254076865844095, language=EN, stringName=Fengzheng CAI, firstName=Fengzheng, middleName=null, lastName=CAI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.School of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan 570228, China
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^2.中国热带农业科学院橡胶研究所，海南海口　571101, bio={"content":"

蔡丰政（2001—），男，硕士研究生，研究方向：热带雨林生物多样性保护及恢复。

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蔡丰政（2001—），男，硕士研究生，研究方向：热带雨林生物多样性保护及恢复。

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