IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i9p1461-d914303.html
   My bibliography  Save this article

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Author

Listed:
  • Meftah Salem M. Alfatni

    (Libyan Authority for Scientific Research, Tripoli P.O. Box 80045, Libya)

  • Siti Khairunniza-Bejo

    (Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Laboratory of Plantation System Technology and Mechanization (PSTM), Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Smart Farming Technology Research Centre (SFTRC), Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Mohammad Hamiruce B. Marhaban

    (Centre for Control System and Signal Processing, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Osama M. Ben Saaed

    (Libyan Authority for Scientific Research, Tripoli P.O. Box 80045, Libya)

  • Aouache Mustapha

    (Division Télécom, Centre de Développement des Technologies Avancées, CDTA, Baba-Hassen 16303, Algeria)

  • Abdul Rashid Mohamed Shariff

    (Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Laboratory of Plantation System Technology and Mechanization (PSTM), Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Smart Farming Technology Research Centre (SFTRC), Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

Suggested Citation

  • Meftah Salem M. Alfatni & Siti Khairunniza-Bejo & Mohammad Hamiruce B. Marhaban & Osama M. Ben Saaed & Aouache Mustapha & Abdul Rashid Mohamed Shariff, 2022. "Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis," Agriculture, MDPI, vol. 12(9), pages 1-28, September.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1461-:d:914303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/9/1461/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/9/1461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fa Zhao & Guijun Yang & Hao Yang & Huiling Long & Weimeng Xu & Yaohui Zhu & Yang Meng & Shaoyu Han & Miao Liu, 2022. "A Method for Prediction of Winter Wheat Maturity Date Based on MODIS Time Series and Accumulated Temperature," Agriculture, MDPI, vol. 12(7), pages 1-14, June.
    2. Eddy Plasquy & José M. Garcia & Maria C. Florido & Rafael R. Sola-Guirado, 2021. "Estimation of the Cooling Rate of Six Olive Cultivars Using Thermal Imaging," Agriculture, MDPI, vol. 11(2), pages 1-13, February.
    3. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.
    4. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    5. Jia Quan Goh & Abdul Rashid Mohamed Shariff & Nazmi Mat Nawi, 2021. "Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of ," Agriculture, MDPI, vol. 11(12), pages 1-20, November.
    6. Habib Khan & Ijaz Ul Haq & Muhammad Munsif & Mustaqeem & Shafi Ullah Khan & Mi Young Lee, 2022. "Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique," Agriculture, MDPI, vol. 12(8), pages 1-20, August.
    7. Daniele Silvestro & Stefano Goria & Thomas Sterner & Alexandre Antonelli, 2022. "Improving biodiversity protection through artificial intelligence," Nature Sustainability, Nature, vol. 5(5), pages 415-424, May.
    8. Peng Wang & Tong Niu & Dongjian He, 2021. "Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism," Agriculture, MDPI, vol. 11(11), pages 1-13, October.
    9. Armacheska Rivero Mesa & John Y. Chiang, 2021. "Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    10. Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022. "Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus," Agriculture, MDPI, vol. 12(7), pages 1-27, July.
    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. Yaliu Yang & Yuan Wang & Yingyan Zhang & Conghu Liu, 2022. "Data-Driven Coupling Coordination Development of Regional Innovation EROB Composite System: An Integrated Model Perspective," Mathematics, MDPI, vol. 10(13), pages 1-25, June.
    2. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    3. Keyan Zheng & Fagang Hu & Yaliu Yang, 2023. "Data-Driven Evaluation and Recommendations for Regional Synergy Innovation Capability," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    4. Lee, Chien-Chiang & Yan, Jingyang & Wang, Fuhao, 2024. "Impact of population aging on food security in the context of artificial intelligence: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. Kadukothanahally Nagaraju Shivaprakash & Niraj Swami & Sagar Mysorekar & Roshni Arora & Aditya Gangadharan & Karishma Vohra & Madegowda Jadeyegowda & Joseph M. Kiesecker, 2022. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    6. Xuan Chu & Pu Miao & Kun Zhang & Hongyu Wei & Han Fu & Hongli Liu & Hongzhe Jiang & Zhiyu Ma, 2022. "Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging," Agriculture, MDPI, vol. 12(4), pages 1-18, April.
    7. agarwal, shekhar, 2022. "India’s Rising Technology Economy: Sources and Consequences," OSF Preprints x6yzm, Center for Open Science.
    8. Odunayo David Adeniyi & Hauwa Bature & Michael Mearker, 2024. "A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas," Land, MDPI, vol. 13(3), pages 1-22, March.
    9. Junchi Zhou & Wenwu Hu & Airu Zou & Shike Zhai & Tianyu Liu & Wenhan Yang & Ping Jiang, 2022. "Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    10. Carè, R. & Weber, O., 2023. "How much finance is in climate finance? A bibliometric review, critiques, and future research directions," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Eugenia Gonzalez Ehlinger & Fabian Stephany, 2023. "Skills or Degree? The Rise of Skill-Based Hiring for AI and Green Jobs," CESifo Working Paper Series 10817, CESifo.
    12. István Kristó & Marianna Vályi-Nagy & Attila Rácz & Katalin Irmes & Lajos Szentpéteri & Márton Jolánkai & Gergő Péter Kovács & Mária Ágnes Fodor & Apolka Ujj & Klára Veresné Valentinyi & Melinda Tar, 2023. "Effects of Nutrient Supply and Seed Size on Germination Parameters and Yield in the Next Crop Year of Winter Wheat ( Triticum aestivum L.)," Agriculture, MDPI, vol. 13(2), pages 1-17, February.
    13. Eugenia Gonzalez Ehlinger & Fabian Stephany, 2023. "Skills or Degree? The Rise of Skill-Based Hiring for AI and Green Jobs," Papers 2312.11942, arXiv.org.
    14. Cuiling Li & Xiu Wang & Liping Chen & Xueguan Zhao & Yang Li & Mingzhou Chen & Haowei Liu & Changyuan Zhai, 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns," Agriculture, MDPI, vol. 13(9), pages 1-26, August.
    15. Yu, Yubing & Xu, Jiawei & Zhang, Justin Z. & Liu, Yulong (David) & Kamal, Muhammad Mustafa & Cao, Yanhong, 2024. "Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagement," International Journal of Production Economics, Elsevier, vol. 270(C).
    16. Arjun Srivathsa & Divya Vasudev & Tanaya Nair & Stotra Chakrabarti & Pranav Chanchani & Ruth DeFries & Arpit Deomurari & Sutirtha Dutta & Dipankar Ghose & Varun R. Goswami & Rajat Nayak & Amrita Neela, 2023. "Prioritizing India’s landscapes for biodiversity, ecosystem services and human well-being," Nature Sustainability, Nature, vol. 6(5), pages 568-577, May.
    17. Sun, Yunpeng & Jia, Ruoya & Razzaq, Asif & Bao, Qun, 2024. "Social network platforms and climate change in China: Evidence from TikTok," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. Jingmin Shi & Fanhuai Shi & Xixia Huang, 2023. "Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network," Agriculture, MDPI, vol. 13(2), pages 1-16, February.
    19. Dorijan Radočaj & Mateo Gašparović & Mladen Jurišić, 2024. "Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review," Agriculture, MDPI, vol. 14(7), pages 1-19, 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:12:y:2022:i:9:p:1461-:d:914303. 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.