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Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky–Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.
Kirkegaard JA, Lilley JM, Morrison MJ. Drivers of trends in Australian canola productivity and future prospects. Crop Pasture Sci. 2016;67(4):ⅰ.
Lu C, Napier JA, Clemente TE, Cahoon EB. New frontiers in oilseed biotechnology: Meeting the global demand for vegetable oils for food, feed, biofuel, and industrial applications. Curr Opin Biotechnol. 2011;22(2):252–259.
Tan H, Yang X, Zhang F, Zheng X, Qu C, Mu J, Fu F, Li J, Guan R, Zhang H, et al. Enhanced seed oil production in canola by conditional expression of Brassica napus LEAFY COTYLEDON1 and LEC1-LIKE in developing seeds. Plant Physiol. 2011;156(3):1577–1588.
Menendez YC, Botto JF, Gomez NV, Miralles DJ, Rondanini DP. Physiological maturity as a function of seed and pod water concentration in spring rapeseed (Brassica napus L.). Field Crop Res. 2019;231:1–9.
Manley M. Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chem Soc Rev. 2014;43(24):8200–8214.
Lebrun M, Plotto A, Goodner K, Ducamp M-N, Baldwin E. Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography. Postharvest Biol Technol. 2008;48(1):122–131.
Arias R, Lee T-C, Logendra L. Correlation of lycopene measured by HPLC with the L, a, b color readings of a hydroponic tomato and the relationship of maturity with color and lycopene content. J Agric Food Chem. 2000;48(5):1697–1702.
Septiarini A, Sunyoto A, Hamdani H, Kasim AA, Utaminingrum F, Hatta HR. Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Sci Hortic. 2021;286:110245.
Van de Poel B, Bulens I, Hertog MLATM, Van Gastel L, De Proft MP, Nicolai BM, Geeraerd AH. Model-based classification of tomato fruit development and ripening related to physiological maturity. Postharvest Biol Technol. 2012;67:59–67.
ElManawy AI, Sun D, Abdalla A, Zhu Y, Cen HJC. HSI-PP: A flexible open-source software for hyperspectral imaging-based plant phenotyping. Comput Electron Agric. 2022;200:Article 107248.
Wieme J, Mollazade K, Malounas I, Zude-Sasse M, Zhao M, Gowen A, Argyropoulos D, Fountas S, Van Beek JJBE. Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosyst Eng. 2022;222:156–176.
Fan L, Zhao J, Xu X, Liang D, Yang G, Feng H, Yang H, Wang Y, Chen G, Wei PJS. Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables. Sensors. 2019;19(13):2898.
Ugarte Fajardo J, Maridueña-Zavala M, Cevallos-Cevallos J, Ochoa Donoso DJP. Effective methods based on distinct learning principles for the analysis of hyperspectral images to detect black sigatoka disease. Plants. 2022;11(19):2581.
Ma C, Ren Z, Zhang Z, Du J, Jin C, Yin XJVS. Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology. Vib Spectrosc. 2021;114:Article 103230.
Tian Y, Sun J, Zhou X, Yao K, Tang N. Detection of soluble solid content in apples based on hyperspectral technology combined with deep learning algorithm. J Food Process Preserv. 2022;46(4):Article e16414.
Tang H, Liao G. The rapid detection method of chlorophyll content in rapeseed based on hyperspectral technology. Turk J Agric For. 2021;45(4):465–474.
Wang Z, Tian X, Fan S, Zhang C, Li J. Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models. Infrared Phys Technol. 2021;112:Article 103596.
Xuan G, Gao C, Shao Y, Wang X, Wang Y, Wang KJPB. Maturity determination at harvest and spatial assessment of moisture content in okra using vis-NIR hyperspectral imaging. Postharvest Biol Technol. 2021;180:Article 111597.
Liu D, Sun D-W, Zeng X-AJF, Technology B. Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioproc Tech. 2014;7:307–323.
Jiang H, Hu Y, Jiang X, Zhou HJM. Maturity stage discrimination of Camellia oleifera fruit using visible and near-infrared hyperspectral imaging. Molecules. 2022;27(19):6318.
Wang F, Zhao C, Yang H, Jiang H, Li L, Yang G. Non-destructive and in-site estimation of apple quality and maturity by hyperspectral imaging. Comput Electron Agric. 2022;195:Article 106843.
Gao Z, Shao Y, Xuan G, Wang Y, Liu Y. Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artif Intell Agric. 2020;4:31–38.
Jia B, Wang W, Ni X, Lawrence KC, Zhuang H, Yoon S-C, Gao ZJC, Systems IL. Essential processing methods of hyperspectral images of agricultural and food products. Chemometr Intell Lab Syst. 2020;198:Article 103936.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern Syst. 1979;9(1):62–66.
Zhang X, Sun J, Li P, Zeng F, Wang HJL. Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. LWT. 2021;152:Article 112295.
Kennard RW, Stone LAJT. Computer aided design of experiments. Technometrics. 1969;11(1):137–148.
Soares SFC, Gomes AA, Araujo MCU, Filho ARG, Galvão RKH. The successive projections algorithm. TrAC Trends Anal Chem. 2013;42:84–98.
Li H, Liang Y, Xu Q, Cao D. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta. 2009;648(1):77–84.
Deng BC, Yun YH, Ma P, Lin CC, Ren DB, Liang YZ. A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals. Analyst. 2015;140(6):1876–1885.
Tang G, Huang Y, Tian K, Song X, Yan H, Hu J, Xiong Y, Min SJA. A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm. Analyst. 2014;139(19):4894–4902.
Zhang J, Ma Y, Liu G, Fan N, Li Y, Sun YJFC. Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms. Food Control. 2022;135:Article 108815.
Guo Z, Zhang J, Ma C, Yin X, Guo Y, Sun X. Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds. J Food Compos Anal. 2023;116:Article 105048.
Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: Theory and applications. Neurocomputing. 2006;70(1-3):489–501.
Xiong L, Yao YJB. Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm. Build Environ. 2021;202:Article 108026.
Liu G, Zhao H, Fan F, Liu G, Xu Q, Nazir SJS. An enhanced intrusion detection model based on improved kNN in WSNs. Sensors. 2022;22(4):1407.
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
Si Y, Brumercik F, Yang C, Glowacz A, Ma Z, Siarry P, Sulowicz M, Gupta MK. Prediction and evaluation of energy and exergy efficiencies of a nanofluid-based photovoltaic-thermal system with a needle finned serpentine channel using random forest machine learning approach. Eng Anal Bound Elem. 2023;151:328–343.
Allen A, Williams MR, Sigman MEJFC. Application of likelihood ratios and optimal decision thresholds in fire debris analysis based on a partial least squares discriminant analysis (PLS-DA) model. Forensic Chem. 2019;16:Article 100188.
Zhang Z, Pu Y, Wei Z, Liu H, Zhang D, Zhang B, Zhang Z, Zhao J, Hu JJIP. Combination of interactance and transmittance modes of vis/NIR spectroscopy improved the performance of PLS-DA model for moldy apple core. Infrared Phys Technol. 2022;126:Article 104366.
Duarte JM, Sales NGS, Braga JWB, Bridge C, Maric M, Sousa MH, de Andrade Gomes JJT. Discrimination of white automotive paint samples using ATR-FTIR and PLS-DA for forensic purposes. Talanta. 2022;240:Article 123154.
Zhang X-L, Liu F, Nie P-C, He Y, Bao Y-D. Rapid detection of nitrogen content and distribution in oilseed rape leaves based on hyperspectral imaging. Guang Pu Xue Yu Guang Pu Fen Xi. 2014;34(9):2513–2518.
Wang D, Li X, Ma F, Yu L, Zhang W, Jiang J, Zhang L, Li P. A rapid and nondestructive detection method for rapeseed quality using nir hyperspectral imaging spectroscopy and chemometrics. Appl Sci. 2023;13(16):9444.
Bensaeed OM, Shariff AM, Mahmud AB, Shafri H, Alfatni M. Oil palm fruit grading using a hyperspectral device and machine learning algorithm. IOP Conf Ser Earth Environ Sci. 2014;20(1):Article 012017.
Singh KD, Duddu HSN, Vail S, Parkin I, Shirtliffe SJ. UAV-based hyperspectral imaging technique to estimate canola (Brassica napus L.) seedpods maturity. Can J Remote Sens. 2021;47(1):33–47.
Ke J, Rao L, Zhou L, Chen X, Zhang Z. Non-destructive determination of volatile oil and moisture content and discrimination of geographical origins of Zanthoxylum bungeanum Maxim. by hyperspectral imaging. Infrared Phys Technol. 2020;105:Article 103185.
Panda BK, Mishra G, Ramirez WA, Jung H, Singh CB, Lee S-H, Lee I. Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J Food Eng. 2022;318:Article 110889.
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