Lettuce cultivation period modelling: an image processing and neuro-fuzzy based approach

  • Authors

    • Ira C.Valenzuela Technological University of the Philippines
    • Elmer P.Dadios De La Salle University
    2018-12-29
    https://doi.org/10.14419/ijet.v7i4.17421
  • Cultivation Period Modelling, Image Processing, Neuro-Fuzzy, Plant Growth Stage, Vision System.

  • In this study, the cultivation period of a lettuce was modeled using image processing and neuro-fuzzy inference system. The images of the lettuce were acquired using a camera and were processed using OpenCV. Image features were extracted such as pixel count and RGB and then converted into HSV, CIELab and YCbCr. To select which among these colors best represents the lettuce image with respect to cultivation period, a feature selection algorithm was used. The YCbCr features and pixel count were chosen based on their correlation value. These data became the input for the neuro-fuzzy inference system. The system was modeled for hybrid optimization with the use of generalized bell-type membership function which is best for smooth nonlinear function. A total of 81 fuzzy rules were developed. Based on the result, the model was able to determine the cultivation period of a lettuce with a 99.96% accuracy.

     

  • References

    1. [1] W. Xu, H. Jiang and J. Huang, "Regional Crop Yield Assessment by Combination of a Crop Growth Model and Phenology Information Derived from MODIS. Sensor Letters," 2011. https://doi.org/10.1166/sl.2011.1388.

      [2] H. M. Rawson and H. G. Macpherson, "IRRIGATED WHEAT," 2000. [Online]. Available: Section 3: Assessing and measuring your crop.

      [3] N. R. Canada, "Crop Monitoring & Damage Assessment," 25 11 2015. [Online]. Available: http://www.nrcan.gc.ca/node/14652.

      [4] M. Y. Reynolds, "Estimation crop yields and production by integrating the FAO Crop Speci. C Water Balance model with real-time satellite data and ground-based ancillary data," vol. 21, no. 18, pp. 3487-3508, 2000.

      [5] S. Shafian and D. Valadanzouj, "Assessment crop yield estimation methods by using satellite imagery," 11 11 2009. [Online]. Available: https://www.geospatialworld.net/article/assessment-crop-yield-estimation-methods-by-using-satellite-imagery/.

      [6] L. N. Smith, W. Zhang, M. F. Hansen, I. J. Hales and M. L. Smith, "Innovative 3D and 2D machine vision methods for analysis of plants and crops in the," Computers in Industry, 2018. https://doi.org/10.1016/j.compind.2018.02.002.

      [7] S. Sabzi, Y. Abbaspour-Gilandeh and H. Javadikia, "Machine vision system for the automatic segmentation of plants under different lighting conditions," Biosystems Engineering, 2017. https://doi.org/10.1016/j.biosystemseng.2017.06.021.

      [8] R. Urena, F. Rodrı´guez and M. Berenguel, "A machine vision system for seeds germination," Computers and Electronics in Agriculture, vol. 32, p. 1–20, 2001. https://doi.org/10.1016/S0168-1699(01)00150-8.

      [9] "Merriam-Webster," [Online]. Available: https://www.merriam-webster.com/dictionary/lettuce.

      [10] University of Illinois Extension, [Online]. Available: https://extension.illinois.edu/veggies/lettuce.cfm.

      [11] VeggieHarvest, "Lettuce Growing and Harvest Information," [Online]. Available: http://veggieharvest.com/vegetables/lettuce.html.

      [12] M. H. Kim, E. G. Choi, G. Y. Baek, C. H. Kim, B. O. Jink, B. E. Moon, D. E. Kim and H. T. Kim, "Lettuce growth prediction in plant factory," 2013.

      [13] W.-T. Chen, Y.-H. F. Yeh and T.-T. L. Ting-Yu Liu, "An Automatic Plant Growth Measurement System for Plant Factory," 2013.

      [14] "Poynton's Color FAQ," 27 February 1997. [Online]. Available: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/POYNTON1/ColorFAQ.html.

      [15] Orillo, J.W., Amado, T.M., Arago, N.M. and Fernandez, E., 2016. Rice Plant Disease Identification And Detection Technology Through Classification Of Microorganisms Using Fuzzy Neural Network. Jurnal Teknologi, 78(5-8), pp.25-31 https://doi.org/10.11113/jt.v78.8746.

      [16] Orillo, J.W., Cruz, J.D., Agapito, L., Satimbre, P.J. and Valenzuela, I., 2014, November. Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network. In Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on (pp. 1-6). IEEE.

      [17] Valenzuela, I., Amado, T. and Orillo, J.W., 2016. Urine Test Strip Analysis Using Image Processing For Mobile Application. Jurnal Teknologi, 78(5-7), p.2016. https://doi.org/10.11113/jt.v78.8720.

      [18] Valenzuela, I.C., Puno, J.C.V., Bandala, A.A., Baldovino, R.G., de Luna, R.G., De Ocampo, A.L., Cuello, J. and Dadios, E.P., 2017, December. Quality assessment of lettuce using artificial neural network. In Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017 IEEE 9th International Conference on (pp. 1-5). IEEE. https://doi.org/10.1109/HNICEM.2017.8269506

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  • How to Cite

    C.Valenzuela, I., & P.Dadios, E. (2018). Lettuce cultivation period modelling: an image processing and neuro-fuzzy based approach. International Journal of Engineering & Technology, 7(4), 4271-4277. https://doi.org/10.14419/ijet.v7i4.17421