Research on the Maturity Detection Method of Korla Pears Based on Hyperspectral Technology

被引:3
|
作者
Liu, Jiale [1 ,2 ]
Meng, Hongbing [1 ,2 ]
机构
[1] Tarim Univ, Coll Informat Engn, Alar 843300, Peoples R China
[2] Tarim Univ, Minist Educ, Key Lab Tarim Oasis Agr, Alar 843300, Peoples R China
来源
AGRICULTURE-BASEL | 2024年 / 14卷 / 08期
关键词
hyperspectral; Korla pear; maturity detection; nondestructive testing; CNN-S model; IMAGING TECHNOLOGY; IDENTIFICATION; STRAWBERRY;
D O I
10.3390/agriculture14081257
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
In this study, hyperspectral imaging technology with a wavelength range of 450 to 1000 nanometers was used to collect spectral data from 160 Korla pear samples at various maturity stages (immature, semimature, mature, and overripe). To ensure high-quality data, multiple preprocessing techniques such as multiplicative scatter correction (MSC), standard normal variate (SNV), and normalization were employed. Based on these preprocessed data, a custom convolutional neural network model (CNN-S) was constructed and trained to achieve precise classification and identification of the maturity stages of Korla pears. Additionally, a BP neural network model was used to determine the characteristic wavelengths for maturity assessment based on the sugar content feature wavelengths. The results demonstrated that the BP model, based on sugar content feature wavelengths, effectively discriminated the maturity stages of the pears. Specifically, the comprehensive recognition rates for the training, testing, and validation sets were 98.5%, 93.5%, and 90.5%, respectively. Furthermore, the combination of hyperspectral imaging technology and the custom CNN-S model significantly enhanced the detection performance of pear maturity. Compared to traditional CNN models, the CNN-S model improved the accuracy of the test set by nearly 10%. Moreover, the CNN-S model outperformed existing techniques based on partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) in capturing hyperspectral data features, showing superior generalization capability and detection efficiency. The superior performance of this method in practical applications further supports its potential in smart agriculture technology, providing a more efficient and accurate solution for agricultural product quality detection. Additionally, it plays a crucial role in the development of smart agricultural technology.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears
    Fang, Yiming
    Yang, Fan
    Zhou, Zhu
    Lin, Lujun
    Li, Xiaoqin
    JOURNAL OF SPECTROSCOPY, 2019, 2019
  • [2] Visual Detection Study on Early Bruises of Korla Pear Based on Hyperspectral Imaging Technology
    Chen Xin-xin
    Guo Chen-tong
    Zhang Chu
    Liu Zi-yi
    Jiang Hao
    Lou Bing-gan
    He Yong
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37 (01) : 150 - 155
  • [3] Classification of Korla Fragrant Pears Using NIR Hyperspectral Imaging Analysis
    Rao, Xiuqin
    Yang, Chun-Chieh
    Ying, Yibin
    Kim, Moon S.
    Chao, Kuanglin
    SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY IV, 2012, 8369
  • [4] Detection of early collision and compression bruises for pears based on hyperspectral imaging technology
    Wang, Guanglai
    Wang, Congcong
    Liu, Dayang
    JOURNAL OF AGRICULTURAL ENGINEERING, 2024, 55 (04)
  • [5] Study on quick analysis method of korla pears quality based on the electronic nose
    Hui, G., 1600, Chinese Institute of Food Science and Technology (14):
  • [6] Research on detection method of pea seed vigor based on hyperspectral imaging technology
    Qinjuan L.
    Lianming W.
    Xiaoqing Z.
    Hua Q.
    Lei Y.
    International Journal of Circuits, Systems and Signal Processing, 2021, 15 : 1072 - 1083
  • [7] Progress on quality detection of pears based on vis/NIR spectroscopy and hyperspectral imaging technology
    Yin, Xun
    Yuan, Yitong
    Zhang, Dongyan
    Xu, Lu
    Weng, Shizhuang
    Hong, Qi
    International Agricultural Engineering Journal, 2019, 28 (01): : 360 - 370
  • [8] Exploring the limit of detection on early implicit bruised 'Korla' fragrant pears using hyperspectral imaging features and spectral variables
    Li, Yiting
    You, Sicong
    Wu, Shasha
    Wang, Mengyao
    Song, Jin
    Lan, Weijie
    Tu, Kang
    Pan, Leiqing
    POSTHARVEST BIOLOGY AND TECHNOLOGY, 2024, 208
  • [9] Soluble Solids Content prediction for Korla fragrant pears using hyperspectral imaging and GsMIA
    Wang, Tingting
    Li, Guanghui
    Dai, Chenglong
    INFRARED PHYSICS & TECHNOLOGY, 2022, 123
  • [10] Prediction method of shelf life of damaged Korla fragrant pears
    Yu, Shihui
    Lan, Haipeng
    Li, Xiaolong
    Zhang, Hong
    Zeng, Yong
    Niu, Hao
    Niu, Xiyue
    Xiao, Ailing
    Liu, Yang
    JOURNAL OF FOOD PROCESS ENGINEERING, 2021, 44 (12)