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| Detection of Moisture Content in Strawberry Leaves Using Hyperspectral and Broad Learning System |
| LI Ze-qi1, YANG Zheng1, ZHOU Zhuang-fei1, PENG Ji-yu1, ZHU Feng-le1*, HE Qing-hai2 |
1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023，China
2. Shandong Academy of Agricultural Machinery Science, Jinan 250100, China
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Abstract Moisture content is a critical factor influencing the growth and development of strawberries, and it holds significant importance for their cultivation. Traditional methods of moisture measurement, although precise, are cumbersome and destructive. Hyperspectral Imaging (HSI) has emerged as an ideal technique for plant moisture detection due to its efficiency, non-destructive nature, and multi-attribute detection capabilities. However, the large volume and redundancy of HSI data present challenges. While deep learning methods can extract deep features from the data, their reliance on large-scale annotated datasets limits their application. To address this issue, our study introduces a Broad Learning System (BLS) to solve the training problem with small sample sizes. It proposes a BLS-based method for detecting moisture content in strawberry leaves. The study first prepared samples of healthy and drought-stressed strawberry leaves, obtaining their hyperspectral images and moisture content data. By analyzing three hyperparameter tuning methods and four preprocessing algorithms, a BLS moisture determination model was constructed and its performance was evaluated against comparative models, including Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Gradient Boosting Decision Tree Regression (GBDTR), and Residual Network (ResNet). The results showed that the BLS model achieved a coefficient of determination (R2p) of 0.797 4 and a Root Mean Square Error (RMSE) of 0.004 5 on the test set, outperforming the other models and exceeding the ResNet model by 0.039 4, demonstrating its superior generalization ability and prediction accuracy. Additionally, the optimal model was used to visualize the moisture content in strawberry leaves by generating false-color maps, providing an intuitive display of the leaves' moisture status. The findings suggest that the BLS model is suitable for analyzing hyperspectral data and detecting moisture content in strawberry leaves using small samples, providing a theoretical basis for the online detection of moisture content in strawberry leaves.
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Received: 2024-07-23
Accepted: 2025-02-06
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Corresponding Authors:
ZHU Feng-le
E-mail: zhufl@zjut.edu.cn
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[1] Sun H, Liu N, Wu L, et al. IFAC-PapersOnLine, 2018, 51(17): 443.
[2] ElMasry G, Wang N, ElSayed A, et al. Journal of Food Engineering, 2007, 81(1): 98.
[3] Li C C, Xiao Z, Liu Y H, et al. Agronomy, 2023, 13(1): 56.
[4] Asaari M S M, Mishra P, Mertens S, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138: 121.
[5] Li X, Chen Z, Wang J, et al. Sensors, 2023, 23(7): 3687.
[6] Aptoula E, Ozdemir M C, Yanikoglu B. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1970.
[7] Yang H, Zhang T, Yang W L, et al. International Agrophysics, 2023, 37(3): 225.
[8] An T, Yu H, Yang C S, et al. Journal of Food Process Engineering, 2020, 43(7): e13428.
[9] Lu Y Z, Chen D, Olaniyi E, et al. Computers and Electronics in Agriculture, 2022, 200: 107208.
[10] Ahmed S F, Alam M S B, Hassan M, et al. Artificial Intelligence Review, 2023, 56(11): 13521.
[11] Chen C L P, Liu Z. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10.
[12] Zhao G X, Wang X S, Kong Y, et al. Remote Sensing, 2021, 13(4): 583.
[13] Ma Y, Liu Z, Chen C L P. Applied Intelligence, 2022, 52(3): 2801.
[14] YE Rong-ke, KONG Qing-chen, LI Dao-liang, et al (叶荣珂，孔庆辰，李道亮, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2022, 42(1): 164.
[15] Sinija V R, Mishra H N. Food and Bioprocess Technology, 2011, 4(1): 136.
[16] Li X L, Sun C J, Luo L B, et al. Computers and Electronics in Agriculture, 2015, 112: 28.
[17] Devos O, Downey G, Duponchel L. Food Chemistry, 2014, 148: 124.
[18] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立，袁洪福，陆婉珍). Progress in Chemistry(化学进展), 2004, 16(4): 528.
[19] Yao K S, Sun J, Chen C, et al. Journal of Food Engineering, 2022, 325: 111024.
[20] Wu Y F, Peng S L, Xie Q, et al. Chemometrics and Intelligent Laboratory Systems, 2019, 185: 114.
[21] Amigo J M, Santos C. Preprocessing of Hyperspectral and Multispectral Images, in Data Handling in Science and Technology Elsevier, 2019, 37.
[22] Han J C, Zhang Z, Cao J, et al. Remote Sensing, 2020, 12(2): 236.
[23] Zhan D X, Mu Y Q, Duan W X, et al. Agriculture, 2023, 13(5): 1088.
[24] He K M, Zhang X Y, Ren S Q, et al. Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770.
[25] Huang Q J, Mao J L, Liu Y. An Improved Grid Search Algorithm of SVR Parameters Optimization, 2012 IEEE 14th International Conference on Communication Technology, 2012: 1022.
[26] Bai Y, Shi L S, Zha Y Y, et al. Computers and Electronics in Agriculture, 2023, 215: 108349.
[27] Azimi S, Kaur T, Gandhi T K. Proceedings of 2020 25th International Conference on Pattern Recognition (ICPR), 2021, 8500.
[28] Zahedi S M, Moharrami F, Sarikhani S, et al. Scientific Reports, 2020, 10(1): 17672.
[29] Zahir S A D M, Jamlos M F, Omar A F, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024, 304: 123273.
[30] Horler D N H, Dockray M, Barber J, et al. International Journal of Remote Sensing, 1983, 4(2): 273.
[31] Zhang Z J, Jiang D Y, Chang Q R, et al. Remote Sensing, 2023, 15(7): 1732.
[32] Yang L, Shami A. Neurocomputing, 2020, 415: 295.
[33] Haghbin N, Bakhshipour A, Zareiforoush H. Plant Methods, 2023, 19(1): 53.
[34] Mou Y, You X G, Xu D Q, et al. Chemometrics and Intelligent Laboratory Systems, 2014, 132: 168.
[35] Zhou J, Li W, Wang J X, et al. Physica A: Statistical Mechanics and its Applications, 2019, 534: 122370.
[36] Bejani M M, Ghatee M. Artificial Intelligence Review, 2021, 54(8): 6391.
[37] Jin J W, Liu Z L, Chen C L P. Science China Information Sciences, 2018, 61(11): 112209.
[38] Song Z Y, Yan S H, Zang Z Y, et al. IEEE Transactions on Terahertz Science and Technology, 2018, 8(5): 520.
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