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A novel artificial bee colony-optimized visible oblique dipyramid greenness index for visionbased aquaponic lettuce biophysical signatures estimation

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Information Processing in Agriculture 2023年第3期10卷 312-333页

作者： Ronnie Concepcion II Elmer Dadios Edwin Sybingco argel bandala Department of Manufacturing Engineering and Management De La Salle UniversityManila 1004Philippines Department of Electronics and Computer Engineering De La Salle UniversityManila 1004Philippines Center for Engineering and Sustainable Development Research De La Salle UniversityManila 1004Philippines

In response to the challenges in providing real-time extraction of crop biophysical signatures,computer vision in computational crop phenotyping highlights the opportunities of computational intelligence *** and angular brightness due to the presence of photosynthetic light unevenly illuminate crop *** this study,a novel vegetation index named artificial bee colony-optimized visible band oblique dipyramid greenness index(vODGIabc)was proposed to enhance vegetation pixels by correcting the saturation and brightness levels,and the ratio of visible RGB reflectance ***-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life *** introduced saturation rectification coeffi-cient(X),value rectification coefficient(m),green–red wavelength adjustment factor(a),and green–blue wavelength adjustment factor(b)on the original triangular greenness index resulted in 3D canopy reflectance spectrum with two oblique tetrahedrons formed by connecting the vertices of visible RGB band reflectance and maximum wavelength point map to corresponding saturation and value of lettuce-captured *** neighborhood component analysis(NCA),minimum redundancy maximum relevance(MRMR),Pearson’s correlation coefficient(PCC),and analysis of variance(ANOVA)weighted most of the canopy area,energy,and *** linear relationships were exhibited by using vODGIabc in estimating lettuce crop fresh weight,height,number of spanning leaves,leaf area index,and growth stage with R2 values of 0.9368 for InceptionV3,0.9574 for ResNet101,0.9612 for ResNet101,0.9999 for Gaussian processing regression,and accuracy of 88.89%for ResNet101,*** low-cost approach on developing greenness index for biophysical signatures estimation proved to be more accurate than the previously established triangular greenness index(TGI)using RGB smartphone camera.

关键词： Lettuce Plant phenotype Precision farming Remote sensing Swarm intelligence Vegetation index

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Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming

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Information Processing in Agriculture 2022年第4期9卷 497-513页

作者： Ronnie Concepcion II Sandy Lauguico Jonnel Alejandrino Elmer Dadios Edwin Sybingco argel bandala Department of Manufacturing Engineering and Management De La Salle UniversityManila 1004Philippines Department of Electronics and Computer Engineering De La Salle UniversityManila 1004Philippines Center for Engineering and Sustainable Development Research De La Salle UniversityManila 1004Philippines

Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors *** response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring,the integration of aquaphotomics and computational intelligence is presented in this *** study used the combination of temperature,pH,and electrical conductivity sensors in predicting crop growth primary macronutrient concentration(nitrate,phos-phate,and potassium(NPK)),thus,limiting the number of deployed sensors.A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36℃ with 2℃ increments to mimic ambient range,which varies water compositional *** was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK ***-pal component analysis emphasized nutrient dynamics through selecting the highly corre-lated water absorption bands resulting in 250 nm,840 nm,and 765 nm for nitrate,phosphate,and potassium *** activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient ***-iments have shown that multigene symbolic regression genetic programming(MSRGP)obtained the optimal performance in parameterizing and predicting nitrate,phosphate,and potassium concentrations based on water physical properties with an accuracy of 87.63%,88.73%,and 99.91%,*** results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30℃ and phosphate below 25℃ with pH and electrical con-ductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm^(-1) *** novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a

关键词： Aquaphotomics Genetic programming Plant nutrients Physicochemical composition Spectrophotometry Water quality monitoring

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