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All Journal AGRIVITA, Journal of Agricultural Science
Ronnie S. Concepcion II
De La Salle University, Manila, Philippines
Agriculture, Biological Sciences & Forestry

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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
AGRIVITA, Journal of Agricultural Science Vol 42, No 3 (2020)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v42i3.2528

Abstract

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.
Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums Ronnie S. Concepcion II; Elmer P. Dadios
AGRIVITA, Journal of Agricultural Science Vol 43, No 1 (2021)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v43i1.2843

Abstract

Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.
Co-Authors Edwin Sybingco Elmer P. Dadios Jonnel D. Alejandrino Sandy C. Lauguico