An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in "Luogang" Orange
文献类型： 外文期刊
作者： Xu, Sai 1 ; Lu, Huazhong 2 ; Ference, Christopher 4 ; Zhang, Qianqian 3 ;
作者机构： 1.Guangdong Acad Agr Sci, Publ Monitoring Ctr Agroprod, Guangzhou 510640, Peoples R China
2.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China
3.South China Agr Univ, Coll Engn, Guangzhou 510640, Peoples R China
4.Univ Florida, Dept Plant Pathol, 2550 Hull Rd, Gainesville, FL 32611 USA
关键词： quality detection; accuracy improvement; information fusion; deep learning; orange
期刊名称：ELECTRONICS （ 影响因子：2.397； 五年影响因子：2.408 ）
ISSN：
年卷期： 2021 年 10 卷 1 期
页码：
收录情况： SCI
摘要： The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of "Luogang" orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky-Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R-2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R-2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of "Luogang" orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.
- 相关文献
作者其他论文 更多>>
-
Nondestructive detection of Clonorchis sinensis infection of raw Pseudorasbora parva fish by near-infrared hyperspectral imaging
作者：Xu, Sai;Lu, Huazhong;Liang, Xin;He, Zhenhui;Lu, Huazhong;Xu, Sai;Lu, Huazhong
关键词：Pseudorasbora parva; Clonorchis sinensis; Hyperspectral imaging; Nondestructive detection; Modeling
-
The Impact of Light Intensities on the Phenotypic Parameters of Cucumber Seedlings at Three Developmental Stages
作者：Li, Bin;Wei, Xinyu;Zhou, Xingxing;Zhao, Junhong;Lu, Huazhong;Chen, Xi;Yang, Fengxi
关键词：plant factory; cucumber seedling; light requirement; developmental stage; seedling phenotype
-
Photocatalytic reforming of biomass for hydrogen production: A comprehensive overview
作者：Xu, Sai;Huang, Xi;Xu, Sai;Xu, Sai;Lu, Huazhong
关键词：Photocatalytic reforming; Biomass; Clean energy; Hydrogen; Environment
-
Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals
作者：Luo, Yizhi;Luo, Haowen;Luo, Yizhi;Lu, Huazhong;Luo, Haowen;Lv, Enli;Li, Bin;Meng, Fanming;Xia, Jinjin;Lv, Enli;Zeng, Zhixiong;Meng, Fanming;Yang, Aqing
关键词：nursery pigs; feeding behavior recognition; functional area proposals; behavioral quantification; transformer
-
Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review
作者：Xu, Sai;Liang, Xin;Guo, Yinghua;Liang, Xin;Lu, Huazhong
关键词：fruits and vegetables; intelligent rapid detection; low-content components
-
A Framework for Single-Panicle Litchi Flower Counting by Regression with Multitask Learning
作者：Lin, Jiaquan;Li, Jun;Ma, Zhe;Li, Can;Huang, Guangwen;Li, Jun;Li, Jun;Lu, Huazhong
关键词：
-
An Improved Rotating Box Detection Model for Litchi Detection in Natural Dense Orchards
作者：Li, Bin;Lu, Huazhong;Wei, Xinyu;Zhou, Xingxing;Luo, Yizhi;Guan, Shixuan;Zhang, Zhenyu
关键词：litchi detection; oriented bounding box; transformer module; eca attention mechanism; small target detection
