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

 
 
 
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  • Abstract


    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.

     


  • Keywords


    Cultivation Period Modelling; Image Processing; Neuro-Fuzzy; Plant Growth Stage; Vision System.

  • References


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Article ID: 17421
 
DOI: 10.14419/ijet.v7i4.17421




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