Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrix

Ronnie S. Concepcion II, Sandy C. Lauguico, Jonnel D. Alejandrino, Elmer P. Dadios, Edwin Sybingco


Leaf canopy area is a fundamental crop growth characteristic that encompasses the spatial area covered by plants. However, non-destructive and automatic computation of lettuce canopy area is still open research. This study presents a vision-based system with color space thresholding and machine learning models in measuring the photosynthetic productivity of aquaponic lettuce based on canopy area derived from the numerical image textural features of Haralick and gray level co-occurrence matrix (GLCM). Lettuce images on different growth stages with varying photosynthetic pigment intensities and geometrical structures are extracted with contrast, correlation, energy, homogeneity, entropy, variance, and information measure of correlations 1 and 2 features. For multi-band color space thresholding, CIELab bested RGB, HSV, and YCbCr colour spaces in segmenting the lettuce plant with sensitivity and specificity measures of 94.77% and 97.16% respectively. For measuring the lettuce canopy area, RMSE was recorded as 50.23% for fitness function neural network (FFNN), 20.46% for radial basis function neural network (RBFNN), 15.11% for exact radial basic function neural network (RBEFNN) and 13.54% for generalized regression neural network (GRNN). Comparative analysis revealed that the two-hidden layer GRNN model with 0.09 spread value and 240 hidden neurons bested other machine learning models in terms of RMSE without overfitting.


Biosystems engineering; Computational intelligence; Intelligent systems; Lettuce; Machine vision

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DOI: http://doi.org/10.17503/agrivita.v42i3.2528

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