Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

被引：0
作者：
Noutfia, Younes [1 ]
Ropelewska, Ewa [1 ]
Jozwiak, Zbigniew [1 ]
Rutkowski, Krzysztof [1 ]
机构：
[1] Natl Inst Hort Res, Fruit & Vegetable Storage & Proc Dept, Konstytucji 3 Maja 1-3, PL-96100 Skierniewice, Poland
来源：
SENSORS | 2024年 / 24卷 / 23期
基金：
欧盟地平线“2020”;
关键词：
freezing; 'Mejhoul'; date cultivar; image processing; machine learning; prediction; classification;
D O I：
10.3390/s24237560
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
The emergence of new technologies focusing on "computer vision" has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh and frozen 'Mejhoul' and 'Boufeggous' date palm cultivars stored for 6 months at -10 degrees C and -18 degrees C. Their quality was evaluated, in a non-destructive manner, based on texture features extracted from images acquired using a digital camera and flatbed scanner. The whole process of image processing was carried out using MATLAB R2024a and Q-MAZDA 23.10 software. Then, extracted features were used as inputs for pre-established algorithms-groups within WEKA 3.9 software to classify frozen date fruit samples after 0, 2, 4, and 6 months of storage. Among 599 features, only 5 to 36 attributes were selected as powerful predictors to build desired classification models based on the "Functions-Logistic" classifier. The general architecture exhibited clear differences in classification accuracy depending mainly on the frozen storage period and imaging device. Accordingly, confusion matrices showed high classification accuracy (CA), which could reach 0.84 at M0 for both cultivars at the two frozen storage temperatures. This CA indicated a remarkable decrease at M2 and M4 before re-increasing by M6, confirming slight changes in external quality before the end of storage. Moreover, the developed models on the basis of flatbed scanner use allowed us to obtain a high correctness rate that could attain 97.7% in comparison to the digital camera, which did not exceed 85.5%. In perspectives, physicochemical attributes can be added to developed models to establish correlation with image features and predict the behavior of date fruit under storage.
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页数：12
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共 32 条
  • [11] Chen M., Chen Z., Luo L., Tang Y., Cheng J., Wei H., Wang J., Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard, Comput. Electron. Agric, 219, (2024)
  • [12] Noutfia Y., Ropelewska E., What can artificial intelligence approaches bring to an improved and efficient harvesting and postharvest handling of date fruit (Phoenix dactylifera L.)? A review, Postharvest Biol. Technol, 213, (2024)
  • [13] Mahanti N.K., Pandiselvam R., Kothakota A., Chakraborty S.K., Kumar M., Cozzolino D., Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis, Trends Food Sci. Technol, 120, pp. 418-438, (2022)
  • [14] Mohammed M., Munir M., Aljabr A., Prediction of date fruit quality attributes during cold storage based on their electrical properties using artificial neural networks models, Foods, 11, (2022)
  • [15] Mohammed M., Srinivasagan R., Alzahrani A., Alqahtani N.K., Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres, Sustainability, 15, (2023)
  • [16] Srinivasagan R., Mohammed M., Alzahrani A., TinyML-sensor for shelf life estimation of fresh date fruits, Sensors, 23, (2023)
  • [17] Szczypinski P.M., Klepaczko A., Kociolek M., QMaZda—Software tools for image analysis and pattern recognition, Proceedings of the 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 217-221
  • [18] Jain A.K., Fundamentals of Digital Image Processing, 2, pp. 1375-1382, (1989)
  • [19] Mallat S.G., Multifrequency channel decompositions of images and wavelet models, IEEE Trans. Acoust. Speech Signal Process, 37, pp. 2091-2110, (1989)
  • [20] Noutfia Y., Ropelewska E., Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.), Agriculture, 13, (2023)
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