[1]马亚星,张文斌,鲁权,等.近红外光谱结合深度学习的苹果糖心区域面积占比预测[J].江苏农业学报,2025,(04):715-723.[doi:doi:10.3969/j.issn.1000-4440.2025.04.010]
　MA Yaxing,ZHANG Wenbin,LU Quan,et al.Near-infrared spectroscopy combined with deep learning for prediction of proportion of apple watercore area[J].,2025,(04):715-723.[doi:doi:10.3969/j.issn.1000-4440.2025.04.010]
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近红外光谱结合深度学习的苹果糖心区域面积占比预测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年04期
页码:
715-723
栏目:
农业信息工程
出版日期:
2025-04-30

文章信息/Info

Title:
Near-infrared spectroscopy combined with deep learning for prediction of proportion of apple watercore area
作者:
马亚星1; 张文斌2; 鲁权3; 尹治棚3; 赵春林3; 张隆鑫1; 徐晗1; 吴海剑1
(1.昆明理工大学机电工程学院,云南昆明650500；2.昆明学院机电工程学院,云南昆明650214；3.宁蒗恒泰农业投资开发有限公司,云南宁蒗674300)
Author(s):
MA Yaxing1; ZHANG Wenbin2; LU Quan3; YIN Zhipeng3; ZHAO Chunlin3; ZHANG Longxin1; XU Han1; WU Haijian1
(1.College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China；2.College of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China；3.Ninglang Hengtai Agricultural Investment Development Limited Company, Ninglang 674300, China)
关键词:
苹果; 糖心区域面积占比; 近红外光谱; 深度学习; 预测
Keywords:
apple; watercore area proportion; near-infrared spectrum; deep learning; prediction
分类号:
O657.36；TP181
DOI:
doi:10.3969/j.issn.1000-4440.2025.04.010
文献标志码:
A
摘要:
苹果中糖心区域面积的大小对其口感、售价均有一定的影响,但是经长时间存储的苹果内部糖心会逐渐消失,甚至影响苹果的品质以及售价。为探究近红外光谱数据与苹果糖心区域面积之间的相关性,建立糖心区域面积占比预测模型,以成熟时期的糖心苹果为研究对象,采集其光谱数据,通过对苹果进行切片处理,利用深度学习算法提取各个截面中的糖心区域面积,测定各个截面所提取的糖心区域面积占整个截面面积的比值作为该截面的糖心区域面积占比,并将最大糖心区域面积占比作为其整体糖心区域面积占比测量值。将原始光谱数据进行多元散射校正（MSC）、标准正态变换（SNV）、标准化等多种预处理,并建立偏最小二乘回归（PLSR）、支持向量机（SVM）和随机森林（RF）机器学习预测模型以及卷积神经网络（CNN）、双向长短时记忆网络（BiLSTM）深度学习预测模型,其中使用经标准化预处理之后的光谱数据建立的CNN模型预测效果最优。为进一步提高建模效果,采用无信息变量消除法（UVE）、连续投影算法（SPA）、竞争性自适应重加权算法（CARS）等特征提取方法,对预处理之后的数据进行优化,比较不同特征提取方法之间的建模效果。结果表明,CNN卷积神经网络构建的苹果糖心区域面积占比预测模型要优于传统的机器学习建模。其中经标准化预处理,UVE+CARS组合特征提取算法构建的CNN模型预测效果最优,验证集的决定系数(R2)、均方根误差(RMSE)分别为0.921和1.882。研究结果表明,近红外光谱结合CNN卷积神经网络建立的苹果糖心预测模型可以较好地对苹果糖心区域面积占比进行预测,为糖心苹果无损检测提供了理论支撑。
Abstract:
The amount of watercore region in apples has a certain impact on their taste and selling price, but the internal watercore of apples stored for a long time will gradually disappear, even affecting the quality of apples as well as their selling price. In order to investigate the correlation between near-infrared spectral data and the area of the watercore region of apples, and to establish a prediction model for the proportion of watercore area, watercore apples in the mature stage were taken as the research objects, and their spectral data were collected. By slicing the apples, deep learning algorithm was used to extract the region of the watercore in each section, and determined the ratio of the area of the extracted watercore area of each cross-section to the area of the whole cross-section as the proportion of watercore area of that cross-section, and the maximum value of the proportion of watercore area was taken as the measurement value of the overall proportion of watercore area. The original spectral data were pretreated with multiple scattering correction (MSC), standard normal variate (SNV), standardization, etc., and partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) machine learning prediction models, as well as convolutional neutral networks (CNN) and bidirectional long short-term memory (BiLSTM) deep learning prediction models were established, among which the CNN model established using spectral data after standardized pretreatment had the best effect. In order to further improve the modeling effect, feature extraction methods such as uninformative variables elimination (UVE), successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to optimize the data after preprocessing and to compare the modeling effect between different feature extraction methods. The results showed that the prediction model of apple watercore area proportion constructed by CNN convolutional neural network was better than the traditional machine learning modeling. The CNN model constructed by UVE+CARS combined feature extraction algorithm after standardized preprocessing had the best prediction effect, and the determination coefficient (R2) and root mean squared error (RMSE) of the validation set were 0.921 and 1.882, respectively. This study proves that the prediction model of apple watercore area proportion established by near-infrared spectroscopy combined with CNN convolutional neural network can better predict the proportion of watercore area of apples, and it provides the theoretical support for nondestructive testing of watercore apples.

参考文献/References:

[1]童盼盼,张亚若,汤蕾,等. 光照和温度条件对富士苹果糖心形成的影响[J]. 西北农业学报,2020,29(4):579-586.
[2]冯建文,韩秀梅,宋莎,等. 贵州高海拔区糖心苹果果实品质特征及成因分析[J]. 南方农业学报,2021,52(5):1273-1281.
[3]刘小勇. 苹果水心病发病机理及防治研究[D]. 兰州:甘肃农业大学,2005.
[4]周文静,李文胜,王安丽,等. 两种贮藏条件下冰糖心红富士苹果品质变化[J]. 新疆农业科学,2020,57(8):1431-1442.
[5]张思旭,徐焕良,王江波,等. 基于光学特性参数反演的苹果水心病检测[J]. 南京农业大学学报,2023,46(5):986-994.
[6]张弘炀,蔡骋. 基于计算机视觉的苹果水心病无损在线分级系统[J]. 农机化研究,2018,40(10):208-210.
[7]ZHANG Y F, YANG X H, CAI Z L, et al. Online detection of watercore apples by vis/NIR full-transmittance spectroscopy coupled with ANOVA method[J]. Foods,2021,10(12):2983.
[8]邱嘉聆,付庄,金惠良,等. 体表血管近红外三维成像系统与识别算法研究[J]. 光学学报,2023,43(9):0917001.
[9]郑越,李霞,刘娜,等. 基于NIR定量分析模型快速预测酶解蒲公英中多糖的含量[J]. 中国农业大学学报,2024,29(4):205-214.
[10]吴虹璋,蔡红星,任玉,等. 基于可见/近红外光谱对葡萄可溶性固形物无损检测研究[J]. 光散射学报,2024,36(1):44-51.
[11]RONG D, WANG H Y, YING Y B, et al. Peach variety detection using VIS-NIR spectroscopy and deep learning[J]. Computers and Electronics in Agriculture,2020,175:105553.
[12]CHANG H, WU Q F, TIAN H, et al. Non-destructive identification of internal watercore in apples based on online vis/NIR spectroscopy[J]. Transactions of the ASABE,2020,63(6):1711-1721.
[13]GUO Z M, WANG M M, AGYEKUM A A, et al. Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy[J]. Journal of Food Engineering,2020,279:109955.
[14]MA T, LI X Z, INAGAKI T, et al. Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging[J]. Journal of Food Engineering,2018,224:53-61.
[15]ALGUL B E, AL SHOFFE Y, PARK D, et al. Preharvest 1-methylcyclopropene treatment enhances ‘stress-associated watercore’ dissipation in ‘Jonagold’ apples[J]. Postharvest Biology and Technology,2021,181:111689.
[16]LATIF G, ABDELHAMID S E, MALLOUHY R E, et al. Deep learning utilization in agriculture:detection of rice plant diseases using an improved CNN model[J]. Plants,2022,11(17):2230.
[17]CHEN H Z, CHEN A, XU L L, et al. A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources[J]. Agricultural Water Management,2020,240:106303.
[18]江沛原. 基于深度语义分割的无人机遥感影像水体提取研究[J]. 现代计算机,2022,28(15):50-56.
[19]YUAN Y H, CHEN X L, WANG J D. Object-contextual representations for semantic segmentation[M]. Cham:Springer International Publishing,2020.
[20]CENTNER V, MASSART D L, DE NOORD O E, et al. Elimination of uninformative variables for multivariate calibration[J]. Analytical Chemistry,1996,68(21):3851-3858.
[21]ARAUJO M C U, SALDANHA T C B, GALVO R K H, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and Intelligent Laboratory Systems,2001,57(2):65-73.
[22]LI H D, LIANG Y Z, XU Q S, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta,2009,648(1):77-84.
[23]LI Q Q, HUANG Y, TIAN K D. Optimal modeling pattern of variables selection on analog complex using UVE-PLS regression[J]. IOP SciNotes,2020,1(1):014201.
[24]CANOVA L D S, VALLESE F D, PISTONESI M F, et al. An improved successive projections algorithm version to variable selection in multiple linear regression[J]. Analytica Chimica Acta,2023,1274:341560.
[25]SHI X Y, SONG J H, WANG H J, et al. Improving soil organic matter estimation accuracy by combining optimal spectral preprocessing and feature selection methods based on pXRF and vis-NIR data fusion[J]. Geoderma,2023,430:116301.

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备注/Memo

备注/Memo:
收稿日期：2024-09-06基金项目：兴滇英才支持计划项目(YNWR-QNBJ-2018-349)；云南省科技厅创新引导与科技型企业培育计划项目(202204BP090005、202304BU090015)作者简介：马亚星(2001-),男,河南平顶山人,硕士研究生,主要从事农产品无损检测方面的研究。(E-mail)1912832237@qq.com通讯作者：张文斌, (E-mail)190322507@qq.com
更新日期/Last Update: 2025-05-26