Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy

References

• Yang, X.; Chen, J.; Jia, L.; Yu, W.; Wang, D.; Wei, W.; Li, S.; Tian, S.; Wu, D. Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics. Sensors (Basel). 2020 Mar 27. 20(7), 1866. DOI: 10.3390/s20071866 PMID: 32230958; PMCID: PMC7181052
   PubMed Web of Science ®Google Scholar
• Wang, Z.; Künnemeyer, R.; McGlone, A.; Sun, J.; Burdon, J.; Cree, M. J. Non-Destructive Detection of Chilling Injury in Kiwifruit Using a Dual-Laser Scanning System with a Principal Component Analysis - Back Propagation Neural Network. J. Near Infrared Spectrosc. 2022, 30(2), 67–73. DOI: 10.1177/09670335211061842.
   Web of Science ®Google Scholar
• Liu, D. H.; Zhang, S. J.; Wang, B. Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging. Guang pu xue yu Guang pu fen xi= Guang pu. 2015, 35(11), 3167–3171.
   PubMed Web of Science ®Google Scholar
• Li, C.; Su, B.; Zhao, T.; Li, C.; Chen, J.; Zhu, S. Feasibility Study on the Use of Near-Infrared Spectroscopy for Rapid and Nondestructive Determination of Gossypol Content in Intact Cottonseeds. J. Cotton Res. 2021, 4(1), 138–146. DOI: 10.1186/s42397-021-00088-2.
   Google Scholar
• Huang, W. H. (Huang, Weihua). Identification of Adulterated Milk Powder Based on Convolutional Neural Network and Laser-Induced Breakdown Spectroscopy [J]. Journal, May: Microchemical, 2022, p. 176.
   Google Scholar
• Tian, F.; Tan, F.; Li, H. An Rapid Nondestructive Testing Method for Distinguishing Rice Producing Areas Based on Raman Spectroscopy and Support Vector Machine. Vib. Spectrosc : An International Journal Devoted To Applications Of Infrared And Raman Spectroscopy. 2020, 107(1), 103017. DOI: 10.1016/j.vibspec.2019.103017.
   Google Scholar
• Weng, S.; Shuan, Y.; Dong, R.; Pan, F.; Liang, D. Fangfang Pan,dong Liang.Nondestructive Detection of Storage Time of Strawberries Using Visible/near-Infrared Hyperspectral Imaging. Int. J. Food Prop. 2020, 23(1), 269–281. DOI: 10.1080/10942912.2020.1716793.
   Web of Science ®Google Scholar
• Ning, J.; Ye, H.; Sun, Y.; Zhang, J.; Mei, Z.; Xiong, S.; Zhang, S.; Li, Y.; Hui, G.; Yi, X., et al. Study on Apple Damage Detecting Method Based on Relaxation Single-Wavelength Laser and Convolutional Neural Network. Food Meas. 2022, 16(5), 3321–3330.
   Web of Science ®Google Scholar
• Li, L.; Li, C.; Wu, Y.; Yang, Y.; Zhang, Y.; Zhang, H.; Wu, B.; Liu, L.; Xie, R.Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm.APWeb/WAIM Workshops.2018. DOI: 10.1007/978-3-030-01298-4_6
   Google Scholar
• Yao, K.; Sun, J.; Zhou, X.; Nirere, A.; Tian, Y.; Wu, X. Nondestructive Detection for Egg Freshness Grade Based on Hyperspectral Imaging Technology. J. Food Process Eng. 2020, 43(7), e13422.1–e13422.9. DOI: 10.1111/jfpe.13422.
   Web of Science ®Google Scholar
• Wang, W.; Peng, Y.; Sun, H.; Zheng, X.; Wei, W. Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. Food Anal. Methods. 2018, 11(10), 2707–2730. DOI: 10.1007/s12161-018-1256-4.
   Web of Science ®Google Scholar
• Mohammed, A. M.; Shuming, Y. Detection and Quantification of Cow Milk Adulteration Using Portable Near-Infrared Spectroscopy Combined with Chemometrics. Afr. J. Agric. Res. 2021, 17(2), 198–207. DOI: 10.5897/AJAR2020.15321.
   Google Scholar
• Fu, H.; Shi, Q.; Wei, L.; Xu, L.; Guo, X.; Hu, O.; Lan, W.; Xie, S.; Yang, T. Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics. J. Spectrosc. 2019, 2019(20), 1–9. DOI: 10.1155/2019/2467185.
   Google Scholar
• Vasafi, P. S.; Paquet-Durand, O.; Brettschneider, K.; Hinrichs, J.; Hitzmann, B. Anomaly Detection During Milk Processing by Autoencoder Neural Network Based on Near-Infrared Spectroscopy. J. Food Eng. 2021, 299(Juna), .110510.1–.110510.10. DOI: 10.1016/j.jfoodeng.2021.110510.
   Google Scholar
• Zhu, R.; Bai, Z.; Qiu, Y.; Zheng, M.; Jianfeng, G.; Yao, X. Comparison of Mutton Freshness Grade Discrimination Based on Hyperspectral Imaging, Near Infrared Spectroscopy and Their Fusion Information. J. Food Process. Eng. 2021, 44(4), e13642.1–e13642.12. DOI: 10.1111/jfpe.13642.
   Web of Science ®Google Scholar
• Tan, S. H.; Pui, L. P.; Solihin, M. I.; Keat, K. S.; Lim, W. H.; Ang, C. K. Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld Near-Infrared Spectrometer. J. Food Process Preserv. 2021, 45(7), e15576.1–e15576.17. DOI: 10.1111/jfpp.15576.
   Web of Science ®Google Scholar
• Liu, Y.; Yongyu, L.; Peng, Y.; Yang, Y.; Wang, Q. Detection of Fraud in High-Quality Rice by Near-Infrared Spectroscopy. J. Food Sci. 2020, 85(9), 2773–2782. DOI: 10.1111/1750-3841.15314.
   PubMed Web of Science ®Google Scholar
• Cai, C.; Wang, Q.; Cai, W.; Yang, Y.; Hu, Y.; Li, L.; Wang, Y.; Zhou, G. Identification of Grape Leaf Diseases Based on VN-BWT and Siamese DWOAM-DRNet. Eng. Appl. Artif. Intell. 2023, 123, 106341. DOI: 10.1016/j.engappai.2023.106341.
   Web of Science ®Google Scholar
• Fang, S.; Wang, Y.; Zhou, G.; Chen, A.; Cai, W.; Wang, Q.; Hu, Y.; Li, L. Multi-Channel Feature Fusion Networks with Hard Coordinate Attention Mechanism for Maize Disease Identification Under Complex Backgrounds. Comput. Electron. Agric. 2022, 203, 107486. DOI: 10.1016/j.compag.2022.107486.
   Web of Science ®Google Scholar
• van Dael, M.; Verboven, P.; Zanella, A.; Nicolai, B. Combination of Shape and X-Ray Inspection for Apple Internal Quality Control: In silico Analysis of the Methodology Based on X-Ray Computed Tomography; Postharvest Biology and Technology: 2018. DOI: 10.1016/j.postharvbio.2018.05.020.
   Google Scholar
• Defraeye, T.; Lehmann, V.; Gross, D.; Holat, C.; Nicolai, B. M.; Verboven, P.; Verlinden, B. E.; Nicolai, B. M. Application of Mri for Tissue Characterisation of ‘Braeburn’ Apple. Postharvest Biol. Technol. 2013, 75, 95–105. DOI: 10.1016/j.postharvbio.2012.08.009.
   Web of Science ®Google Scholar
• Wei, L.; Zhang, H.; Liu, X. Hyperspectral/Multispectral Reflectance Imaging Combining with Watershed Segmentation Algorithm for Detection of Early Bruises on Apples with Different Peel Colors. Food Anal. Methods. 2019, 12(5), 1218–1228. DOI: 10.1007/s12161-019-01456-0.
   Web of Science ®Google Scholar
• Mahmoud Soltani, F.; Farahmandi, A.; Hosseinpour, S. Early Detection of Freeze Damage in Navel Orange Fruit Using Nondestructive Low Intensity Ultrasound Coupled with Machine Learning. Food Anal. Methods. 2021, 14(6), 1140–1149. DOI: 10.1007/S12161-020-01942-W.
   Web of Science ®Google Scholar
• Yu, S.; Liu, Y.; Tang, Y.; Li, X.; Li, W.; Li, C.; Lan, H.; Lan, H. Non-Destructive Quality Assessment Method for Korla Fragrant Pears Based on Electrical Properties and Adaptive Neural-Fuzzy Inference System. Comput. Electron. Agric. 2022, 203, 107492. DOI: 10.1016/J.COMPAG.2022.107492.
   Web of Science ®Google Scholar
• Lin, M.; Fawole, O. A.; Saeys, W.; Wu, D.*, Wang, J.; Opara, U. L.; Nicolai, B.; Chen, K. Mechanical Damages and Packaging Methods Along the Fresh Fruit Supply Chain: A Review. Crit. Rev. Food Sci. Nutr. 2022. 1–20. *, DOI: 10.1080/10408398.2022.2078783.
   Web of Science ®Google Scholar
• He, Y.; Xiao, Q.; Bai, X.; Zhou, L.; Liu, F.; Zhang, C. Recent Progress of Nondestructive Techniques for Fruits Damage Inspection: A Review. Crit. Rev. Food Sci. Nutr. 2022, 62(20), 5476–5494.
   PubMed Web of Science ®Google Scholar
• Mahanti, N. K.; Pandiselvam, R.; Kothakota, A.; Chakraborty, S. K. Emerging Non-Destructive Imaging Techniques for Fruit Damage Detection: Image Processing and Analysis - Sciencedirect. Trends Food Sci. Technol. 2022, 120, 418–438
   Web of Science ®Google Scholar
• Sa, I.; Ge, Z.; Dayoub, F.; Upcroft, B.; Perez, T.; McCool, C. DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors (Basel, Switzerland). 2016, 16(8), 1222. DOI: 10.3390/s16081222.
   PubMed Web of Science ®Google Scholar
• Zhang, M.; Jiang, Y.; Li, C.; Yang, F.; Li, C. Fully Convolutional Networks for Blueberry Bruising and Calyx Segmentation Using Hyperspectral Transmittance Imaging. Biosyst. Eng. 2020, 192, 159. DOI: 10.1016/j.biosystemseng.2020.01.018.
   Web of Science ®Google Scholar
• Wang, Z.; Hu, M.; Zhai, G. Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data. Sensors (Basel). 2018 Apr 7 18 (4), 1126. DOI: 10.3390/s18041126
   PubMed Web of Science ®Google Scholar
• Gao, F.; Fu, L.; Zhang, X.; Majeed, Y.; Li, R.; Karkee, M.; Zhang, Q. Multi-Class Fruit-On-Plant Detection for Apple in Snap System Using Faster R-Cnn. Comput. Electron. Agric. 2020, 176, 105634. DOI: 10.1016/j.compag.2020.105634.
   Web of Science ®Google Scholar
• Khan, A. I.; Quadri, S. M. K.; Saba, B.; Shah Junaid, L. Deep Diagnosis: A Real-Time Apple Leaf Disease Detection System Based on Deep Learning. Comput. Electron. Agric. 2022, 198, 107093. DOI: 10.1016/J.COMPAG.2022.107093.
   Web of Science ®Google Scholar
• Bin, Y.; Pan, F.; Xiaoyan, L.; Zhijie, L.; Fuzeng, Y. A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sensing(9). 2021, 13(9), 1619. DOI: 10.3390/RS13091619.
   Web of Science ®Google Scholar
• Xue, J.; Shen, B. A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm. Syst. Sci. Control Eng. 2020, 8(1), 22–34. DOI: 10.1080/21642583.2019.1708830.
   Web of Science ®Google Scholar