A scale-invariant lettuce leaf area calculation using machine vision and knowledge-based methods

  • Authors

    • Pocholo James M. Loresco De La Salle University/FEU Institute of Techology
    • Elmer P. Dadios
    2019-02-26
    https://doi.org/10.14419/ijet.v7i4.26553
  • Knowledge-Based, Lettuce Leaf Area, Machine Vision, Scale Invariance.

  • Leaf area is one of the most significant reference tools to characterize plant growth and predict growth stages. Scale invariance in calculating leaf area needs to be understood in the lettuce growth monitoring context. Using machine vision and knowledge-based classifiers, this research produced a system for a scale invariant area calculation of lettuce leaf area by detecting a template marker with a known area for scaling area measurements. Results showed that knowledge-based algorithm can improve the performance of the machine vision classifiers for rejecting false positives even for a limited number of training datasets. Area measurements produced by the system performed well in terms of root-mean-square error (RMSE).

     

     

    Author Biography

    • Pocholo James M. Loresco, De La Salle University/FEU Institute of Techology

      Image Processing

      Machine Vision

      Machine Learning

      Optimization

  • References

    1. [1] J. R. d. Cruz, R. G. Baldovino, A. A. Bandala and E. P. Dadios, "Water usage optimization of Smart Farm Automated Irrigation System using artificial neural network," Proceedings of 2017 5th International Conference on Information and Communication Technology (ICoIC7), 2017.

      [2] D. Escarabajal-Henarejos, J. Molina-Martinez, D. Fernandez-Pacheco and G. Garcia-Mateos, "Methodology for obtaining prediction models of the root depth of lettuce for its application in irrigation automation.," Agriculture Water Management, vol. 151, pp. 167-173, 2015. https://doi.org/10.1016/j.agwat.2014.10.012.

      [3] A. Rajput, S. Rajput and G. Jha, ""Physiological Parameters Leaf Area Index, Crop Growth Rate, Relative Growth Rate and Net Assimilation Rate of Different Varieties of Rice Grown Under Different Planting Geometries and Depths in SRI,"," International Journal of Pure and Applied Bioscience, vol. 5, no. 1, pp. 362-267, 2017. https://doi.org/10.18782/2320-7051.2472.

      [4] Q. Xie, W. Huang, D. Liang, P. Chen, C. Wu, G. Yang, J. Zhang, L. Huang and D. Zhang, "Leaf Area Index Estimation Using Vegetation Indices Derived from Airborne Hyperspectral Images in Winter Wheat," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 8, pp. 3586-3594, 2014. https://doi.org/10.1109/JSTARS.2014.2342291.

      [5] D. Fernandez-Pacheco, D. Escarabajal-Henarejos, A. Ruiz-Canales, J. Conesa and J. Molina-Martinez, "A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain," Biosyst. Eng., vol. 117, pp. 23-34, 2014. https://doi.org/10.1016/j.biosystemseng.2013.07.014.

      [6] P.J.M. Loresco, I.C. Valenzuela, E.P. Dadios, Color Space Analysis Using KNN for Lettuce Crop Stages Identification in Smart Farm Setup, Proceedings of TENCON 2018 IEEE Region 10 Conference, 2018.

      [7] R. R. Fang Y., "Current and prospective methods for plant disease detection," Biosensors, vol. 5, no. 3, pp. 537-561, 2015. https://doi.org/10.3390/bios5030537.

      [8] I. C. Valenzuela, J. C. V. Puno, A. A. Bandala, R. G. Baldovino, R. G. Luna, A. L. De ocampo, J. De Cuello and E. P. Dadios, "Quality assessment of lettuce using artificial neural network," Proceedings of 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-5, 2017. https://doi.org/10.1016/j.agwat.2014.10.012.

      [9] D. Escarabajal-Henarejos, J. Molina-Martinez, D. Fernandez-Pacheco and G. Garcia-Mateos, "Methodology for obtaining prediction models of the root depth of lettuce for its application in irrigation automation.," Agriculture Water Management, vol. 151, pp. 167-173, 2015.

      [10] P. Srivastava & A. Khare “Content-based image retrieval using scale invariant feature transform and moments†, Proceedings of 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), pp. 162 – 166, 2016. https://doi.org/10.1109/UPCON.2016.7894645.

      [11] C. Shi-Gang, L. Heng, W. Xing-Li, Z. Yong-Li and H. Lin, "Study on segmentation of lettuce image based on morphological reorganization and watershed algorithm," Proceedings of 2018 Chinese Control and Decision Conference (CCDC), 2018. https://doi.org/10.1109/CCDC.2018.8408290.

      [12] M. Nehru, S. Padmavathi, “Illumination invariant face detection using viola jones algorithm†Proceedings of 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp.1-4, 2017. https://doi.org/10.1109/ICACCS.2017.8014571.

      [13] Y. Ma, M. Kan, S. Shan, X. Chen, “Hierarchical training for large scale face recognition with few samples per subject†Proceedings of 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2401 – 2405, 2018. https://doi.org/10.1109/ICIP.2018.8451561.

      [14] P.J. M, Loresco, A. Africa, “ECG print-out features extraction using spatial-oriented image processing techniquesâ€, Journal of Telecommunication, Electronic and Computer Engineering (JTEC) Vol 10, No 1-5, pp. 15-20, 2018.

      [15] S. Ervural and M. Ceylan, "Increasing lesion specificity with fusion of manually and automatically segmented liver MR images," Proceedings of 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018. https://doi.org/10.1109/SIU.2018.8404559.

      [16] P.J.M. Loresco, R.Q. Neyra, A. A. Bandala, “Human gesture recognition using computer vision for robot navigationâ€, Proceedings of 5th International Conference on Communication and Computer Engineering (ICOCOE-2018), 2018.

      [17] L. Greche, M. Jazouli, N. Es-Sbai, A. Majda, A. Zarghili, “Comparison between euclidean and manhattan distance measure for facial expressions classificationâ€, Proceedings of 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 1-4, 2017. https://doi.org/10.1109/WITS.2017.7934618.

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  • How to Cite

    James M. Loresco, P., & P. Dadios, E. (2019). A scale-invariant lettuce leaf area calculation using machine vision and knowledge-based methods. International Journal of Engineering & Technology, 7(4), 4880-4885. https://doi.org/10.14419/ijet.v7i4.26553