IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2022i1p74-d1016065.html
   My bibliography  Save this article

Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ ( Phoenix dactylifera L.) Using Image Analysis and Quality Attribute Measurements

Author

Listed:
  • Younés Noutfia

    (Agri Food and Quality Department, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco)

  • Ewa Ropelewska

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

Abstract

An in-depth determination of date fruit properties belonging to a given variety can have an impact on their consumption, processing, and storage. The objective of this study was to characterize date fruits of the ‘Mejhoul’ variety using (i) objective and non-destructive image-analysis features and (ii) measurements of physicochemical parameters. Based on images acquired using a digital camera, more than 1600 texture parameters from the individual color channels L , a , b , R , G , B , X , Y , and Z , and 40 geometric characteristics (including linear dimensions and shape factors for each fruit), were determined. Additionally, pomological features, water content, water activity, color parameters ( L* , a* , b* ), total soluble solids (TSS), reducing sugars, and total sugars were measured. As a main result, the application of machine vision allowed for the correct detection of ‘Mejhoul’ dates and the determination of the image features. The differences in the values of the histogram’s mean (HMean texture) for individual color channels were determined. The ‘Mejhoul’ date fruit images in color channel a (aHMean equal to 145.88) and color channel b (bHMean: 145.49) were the brightest, and in channel Z they were the darkest (ZHMean: 4.23). Due to the determination of the elliptic shape factor ( W 1 ) of 1.000 and the circular shape factor ( W 2 ) of 0.110, the elliptical shape of the fruit was confirmed. On the other hand, ‘Mejhoul’ dates were characterized by a length of 47.3 mm, a diameter of 26.4 mm, flesh thickness of 6.25 mm, total soluble solids of 62.1%, water content of 28.0%, water activity of 0.652, hardness of 694 g, reducing sugars of 13.8%, and total sugars of 58.8%. Due to the determination of many image features and other parameters, this paper presents the first comprehensive characterization of ‘Mejhoul’ date fruits using a non-destructive imaging technique linked to some physicochemical quality attributes.

Suggested Citation

  • Younés Noutfia & Ewa Ropelewska, 2022. "Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ ( Phoenix dactylifera L.) Using Image Analysis and Quality Attribute Measurements," Agriculture, MDPI, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:74-:d:1016065
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/1/74/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/1/74/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan, 2021. "Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning," Agriculture, MDPI, vol. 11(12), pages 1-12, December.
    2. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    3. Ewa Ropelewska, 2022. "Assessment of the Influence of Storage Conditions and Time on Red Currants ( Ribes rubrum L.) Using Image Processing and Traditional Machine Learning," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    4. Ewa Ropelewska & Justyna Szwejda-Grzybowska, 2022. "Relationship of Textures from Tomato Fruit Images Acquired Using a Digital Camera and Lycopene Content Determined by High-Performance Liquid Chromatography," Agriculture, MDPI, vol. 12(9), pages 1-12, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang Chen & Xiaoyulong Chen & Jianwu Lin & Renyong Pan & Tengbao Cao & Jitong Cai & Dianzhi Yu & Tomislav Cernava & Xin Zhang, 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agriculture, MDPI, vol. 12(12), pages 1-22, November.
    2. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    3. Lu Lu & Wei Liu & Wenbo Yang & Manyu Zhao & Tinghao Jiang, 2022. "Lightweight Corn Seed Disease Identification Method Based on Improved ShuffleNetV2," Agriculture, MDPI, vol. 12(11), pages 1-18, November.
    4. Mahdieh Parsaeian & Mohammad Rahimi & Abbas Rohani & Shaneka S. Lawson, 2022. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
    5. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
    6. Kadir Sabanci & Muhammet Fatih Aslan & Vanya Slavova & Stefka Genova, 2022. "The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line," Agriculture, MDPI, vol. 12(10), pages 1-11, October.
    7. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    8. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    9. Yonis Gulzar & Zeynep Ünal & Hakan Aktaş & Mohammad Shuaib Mir, 2023. "Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    10. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    11. Jiapeng Cui & Feng Tan, 2023. "Rice Plaque Detection and Identification Based on an Improved Convolutional Neural Network," Agriculture, MDPI, vol. 13(1), pages 1-15, January.
    12. Rodrigo Cupertino Bernardes & André De Medeiros & Laercio da Silva & Leo Cantoni & Gustavo Ferreira Martins & Thiago Mastrangelo & Arthur Novikov & Clíssia Barboza Mastrangelo, 2022. "Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology," Agriculture, MDPI, vol. 12(11), pages 1-14, October.
    13. Ewa Ropelewska & Dorota E. Kruczyńska & Ahmed M. Rady & Krzysztof P. Rutkowski & Dorota Konopacka & Karolina Celejewska & Monika Mieszczakowska-Frąc, 2023. "Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning," Agriculture, MDPI, vol. 13(3), pages 1-16, February.
    14. Maged Mohammed & Ramasamy Srinivasagan & Ali Alzahrani & Nashi K. Alqahtani, 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    15. Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    16. Haixia Sun & Shujuan Zhang & Rui Ren & Liyang Su, 2022. "Maturity Classification of “Hupingzao” Jujubes with an Imbalanced Dataset Based on Improved MobileNet V2," Agriculture, MDPI, vol. 12(9), pages 1-16, August.
    17. Yonis Gulzar, 2023. "Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    18. Shahnawaz Ayoub & Yonis Gulzar & Jaloliddin Rustamov & Abdoh Jabbari & Faheem Ahmad Reegu & Sherzod Turaev, 2023. "Adversarial Approaches to Tackle Imbalanced Data in Machine Learning," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    19. Poonam Dhiman & Amandeep Kaur & V. R. Balasaraswathi & Yonis Gulzar & Ali A. Alwan & Yasir Hamid, 2023. "Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review," Sustainability, MDPI, vol. 15(12), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:74-:d:1016065. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.