[1] 白胜, 朱润华, 阳圣莹, 等. 不同草莓品种营养成分比较与品种筛选[J]. 山西农业科学, 2020, 48(1): 64-67.BAI Sh, ZHU R H, YANG Sh Y, et al. Nutrients comparison and screening of different strawberry varieties[J]. Journal of Shanxi Agricultural Sciences, 2020, 48(1): 64-67(in Chinese).
[2] AFRIN S, GASPARRINI M, FORBES-HERNANDEZ T Y, et al. Promising health benefits of the strawberry: A focus on clinical studies[J]. Journal of Agricultural and Food Chemistry, 2016, 64(22): 4435-4449. doi: 10.1021/acs.jafc.6b00857
[3] 刘凯丽, 郑洪健, 黄敏, 等. 云南香格里拉3个草莓品种的感官品质及耐储藏性对比分析[J]. 昆明学院学报, 2022, 44(3): 87-92.LIU K L, ZHENG H J, HUANG M, et al. Comparative analysis of sensory quality and storage tolerance of three strawberry varieties in Yunnan Shangri-La[J]. Journal of Kunming University, 2022, 44(3): 87-92(in Chinese).
[4] 郝乾坤, 王智民, 童开林. 外源海藻糖对草莓保鲜效果的影响[J]. 陕西农业科学, 2022, 68(8): 84-87.HAO Q K, WANG Zh M, TONG K L. Effect of exogenous trehalose on preservation of strawberry[J]. Shaanxi Journal of Agricultural Sciences, 2022, 68(8): 84-87(in Chinese).
[5] 陈卓, 宋俏微, 张水洞, 等. 氧化淀粉的抑菌效果及草莓涂膜保鲜应用[J]. 食品科学, 2022, 43(21): 324-331.CHEN Zh, SONG Q W, ZHANG Sh D, et al. Antimicrobial effect of oxidized starch and its application as a coating for strawberry preservation[J]. Food Science, 2022, 43(21): 324-331(in Chinese).
[6] LIU Y, WU Q W, HUANG J L, et al. Comparison of apple firmness prediction models based on non-destructive acoustic signal[J]. International Journal of Food Science & Technology, 2021, 56(12): 6443-6450.
[7] 陈广大, 刘德君, 李天旭, 等. 便携式水果硬度测量仪的设计[J]. 中国农机化学报, 2016, 37(7): 85-88.CHEN G D, LIU D J, LI T X, et al. Design of the portable fruit hardness measuring instrument[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(7): 85-88(in Chinese).
[8] PARK B, SHIN T S, CHO J S, et al. Characterizing hyperspectral microscope imagery for classification of blueberry firmness with deep learning methods[J]. Agronomy, 2021, 12(1): 85. doi: 10.3390/agronomy12010085
[9] QIAO M, XU Y, XIA G, et al. Determination of hardness for maize kernels based on hyperspectral imaging[J]. Food Chemistry, 2021, 366(2): 130559.
[10] 马帅帅, 于慧春, 殷勇, 等. 黄瓜水分和硬度高光谱特征波长选择与预测模型构建[J]. 食品与机械, 2021, 37(2): 145-151.MA Sh Sh, YU H Ch, YIN Y, et al. Selection of hyperspectral characteristic wavelength and construction of prediction model for cucumber hardness and moisture[J]. Food & Machinery, 2021, 37(2): 145-151(in Chinese).
[11] ERKINBAEV Ch, DERKSEN K, PALIWAL J, et al. Single kernel wheat hardness estimation using near infrared hyperspectral imaging[J]. Infrared Physics and Technology, 2019, 98(6): 250-255.
[12] SU J Y, YI D W, LIU C J, et al. Dimension reduction aided hyperspectral image classification with a small-sized training dataset: Experimental comparisons[J]. Sensors, 2017, 17(12): 2726. doi: 10.3390/s17122726
[13] XIONG J T, LIN R, BU R B, et al. A micro-damage detection method of litchi fruit using hyperspectral imaging technology[J]. Sensors, 2018, 18(3): 700. doi: 10.3390/s18030700
[14] WU D, MENG L W, YANG L, et al. Feasibility of laser-induced breakdown spectroscopy and hyperspectral imaging for rapid detection of thiophanate-methyl residue on mulberry fruit[J]. International Journal of Molecular Sciences, 2019, 20(8): 2017. doi: 10.3390/ijms20082017
[15] 赵凡, 闫昭如, 宋海燕. 应用高光谱鉴别黑枸杞和唐古特白刺果[J]. 光谱学与光谱分析, 2021, 41(7): 2240-2244.ZHAO F, YAN Zh R, SONG H Y. Hyperspectra used to recognize Black Goji berry and nitraria Tanggu[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2240-2244(in Chinese).
[16] 北京市农林科学院林业果树研究所; 农业部果品及苗木质量监督检验测试中心. 水果硬度的测定: NY/T 2009-2011[S]. 北京: 中华人民共和国农业部, 2011: 1-5.BEIJING ACADEMY OF FORESTRY AND POMOLOGY SCIENCES, LABORATORY OF QUALITY & SAFETY RISK ASSESSMENT FOR FRUIT. Determination of fruit firmness: NY/T 2009-2011[S]. Beijing: Ministry of Agriculture of the PRC, 2011: 1-5(in Chinese).
[17] 姜一河, 王涛, 常红伟. 高光谱图像特征提取方法研究综述[J]. 电光与控制, 2020, 27(10): 73-77.JIANG Y H, WANG T, CHANG H W. An overview of hyperspectral image feature extraction[J]. Electronics Optics & Control, 2020, 27(10): 73-77(in Chinese).
[18] 白丽敏, 李粉玲, 常庆瑞, 等. 结合SPA和PLS法提高冬小麦冠层全氮高光谱估算的精确度[J]. 植物营养与肥料学报, 2018, 24(5): 1178-1184.BAI L M, LI F L, CHANG Q R, et al. Increasing accuracy of hyper-spectral remote sensing for total nitrogen of winter wheat canopy by use of SPA and PLS methods[J]. Journal of Plant Nutrition and Fertilizers, 2018, 24(5): 1178-1184(in Chinese).
[19] 欧阳爱国, 万启明, 李雄, 等. 高光谱成像的水稻螟虫蛀入检测方法[J]. 光谱学与光谱分析, 2021, 41(12): 3844-3850.OUYANG A G, WAN Q M, LI X, et al. Research on rich borer detection methods based on hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2021, 41(12): 3844-3850(in Chinese).
[20] FAN L L, ZHAO J L, XU X G, et al. Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables[J]. Sensors, 2019, 19(13): 2898.
[21] 刘璐, 邵慧, 孙龙, 等. 利用高光谱激光雷达检测木材的霉变与含水量[J]. 激光技术, 2023, 47(5): 620-626.LIU L, SHAO H, SUN L, et al. Detection of mildew and moisture content in timber by hyperspectral LiDAR[J]. Laser Technology, 2023, 47(5): 620-626(in Chinese).
[22] MARSHALL M, BELGIU M, BOSCHETTI M, et al. Field-level crop yield estimation with PRISMA and Sentinel-2[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187(2): 191-210.
[23] 高升, 徐建华. 高光谱成像的红提总酸与硬度的预测及其分布可视化[J]. 食品科学, 2023, 44(2): 327-336.GAO Sh, XU J H. Hyperspectral imaging for prediction and distribution visualization of total acidity and hardness of red globe grapes[J]. Food Science, 2023, 44(2): 327-336(in Chinese).
[24] SAVAŞLI E, KARADUMAN Y, ÖNDER O, et al. Estimating technological quality parameters of bread wheat using sensor-based norma-lized difference vegetation index[J]. Journal of Cereal Science, 2022, 107(8): 103535.
[25] QIN J, BAI H Y, ZHAO P, et al. Dendrochronology-based norma-lized difference vegetation index reconstruction in the Qinling Mountains, North-Central China[J]. Forests, 2022, 13(3): 443.
[26] PAVLO L. Forecasting oil crops yields on the regional scale using normalized difference vegetation index[J]. Journal of Ecological Engineering, 2021, 22(3): 53-57.
[27] PENG Y, FAN M, BAI L, et al. Identification of the best hyperspectral indices in estimating plant species richness in sandy grasslands[J]. Remote Sensing, 2019, 11(5): 558.