Article(id=1325194163267961728, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2026.02.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762704000000, receivedDateStr=2025-11-10, revisedDate=1772726400000, revisedDateStr=2026-03-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880509439, onlineDateStr=2026-11-05, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880509439, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880509439, creator=13701087609, updateTime=1793880509439, updator=13701087609, issue=Issue{id=1325194116245619276, tenantId=1146029695717560320, journalId=1319690209941618849, year='2026', volume='7', issue='2', pageStart='1', pageEnd='200', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793880498229, creator='13701087609', updateTime=1793943300582, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325457528724795795, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325457528724795796, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=169, endPage=181, ext={EN=ArticleExt(id=1325194163414762369, articleId=1325194163267961728, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on apple damage detection method based on RS_FUJII dynamic speckle algorithm, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Apples, as a high-yield fruit, often suffer significant losses during transportation and storage due to collisions and improper temperature control. Traditional manual inspection methods for apple surface damage are not only costly and inefficient but also heavily reliant on operator experience and subjective factors, leading to limited detection accuracy. To address these issues, this study proposed a cost-effective and efficient apple damage detection method based on laser speckle imaging. The proposed approach integrated the RS_FUJII speckle feature extraction algorithm with the RECONANOM-AE damage detection model to improve detection speed and accuracy. Shandong Fuji apples were used as the experimental samples. Speckle images were first preprocessed, followed by feature extraction. To overcome the limitations of the traditional FUJII algorithm in spatiotemporal feature representation and noise robustness, this study introduces a spatiotemporal weighting mechanism and robustness enhancement strategies to develop an improved RS_FUJII algorithm for high-precision damage feature extraction. In addition, an unsupervised neural network model, RECONANOM-AE, is designed to automatically identify damage regions via reconstruction error. Experimental results demonstrate that the proposed model exhibits favorable convergence and stability during training, with both training and validation errors gradually decreasing with increasing epochs. Comprehensive performance evaluation showed that the model achieved a true positive rate of 86.67% and a false positive rate of 6.67%. The overall recognition Accuracy reached 90%, with a Precision of 92.86%, Recall of 86.67%, Specificity of 93.33%, and an F1 Score of 89.68%. These results demonstrated that the proposed method achieved good training stability and strong generalization ability. The method enabled non-destructive quality assessment of apples and provided a reliable technical solution for intelligent fruit quality inspection.
, authors=Wenbo He, Junhao Zhang, Yue Liu, Xiaoxin Li, Sen Men, Kehong Li, authorsList=Wenbo He, Junhao Zhang, Yue Liu, Xiaoxin Li, Sen Men, Kehong Li, authorCompany=null, correspAuthors=Kehong Li, authorNote=
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随着苹果产量的持续增长，运输与储存过程中因碰撞、温控不当等因素造成的损耗问题日益突出。传统人工对苹果表面损伤的检测方法不仅效率低、成本高，而且受操作经验与主观因素影响，检测的准确性难以保障。针对上述问题，研究提出了一种基于激光散斑成像技术的低成本、高精度的苹果表面损伤智能检测方法。该方法结合RS_FUJII散斑特征提取算法与无监督学习的RECONANOM-AE损伤检测模型，实现了对苹果损伤的快速识别与精准评估。研究以山东富士苹果为研究对象，首先对采集的散斑图像进行预处理与特征提取。针对传统FUJII算法中“帧间差异比值累加”在时空特征表达与抗噪性能方面的不足，本研究引入时空加权机制与鲁棒性增强策略，构建了改进型RS_FUJII算法，用于高精度损伤特征提取。在此基础上设计了一种无监督自编码网络RECONANOM-AE，通过重构误差实现苹果损伤区域的自动识别与异常检测。试验结果表明，所提出模型在训练过程中表现出良好的收敛性与稳定性，训练与验证误差均随迭代次数增加而逐步降低。综合性能评估结果显示，模型在损伤检测任务中取得较高的分类与识别性能：正确预测率（true positive rate，TPR）为86.67%，错误预测率（false positive rate，FPR）为6.67%，Accuracy达到90%，Precision为92.86%，Recall为86.67%，Specificity为93.33%，F1 Score为89.68%。研究结果表明，该方法在保持高检测精度的同时，具有良好的泛化能力与鲁棒性，可实现苹果的无损质量智能评估。该研究为果品品质检测提供了可行且高效的技术路径，为智能农业与食品质量控制领域提供了新的研究思路与技术支撑。
, authors=何文博, 张君豪, 刘玥, 李潇鑫, 门森, 李克鸿
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何文博，男，2004年生，北京丰台人；研究方向为数字图像处理。E-mail：2023250385020@buu.edu.cn
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何文博，男，2004年生，北京丰台人；研究方向为数字图像处理。E-mail：2023250385020@buu.edu.cn
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