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 solutions.Sha...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 solutions.Shadow and angular brightness due to the presence of photosynthetic light unevenly illuminate crop canopy.In 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 intensities.Consumer-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life cycle.The 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 images.Hybrid 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 homogeneity.Strong 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,respectively.This 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.展开更多
Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In response to the trend of device minimization and reduced out...Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In 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 paper.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 structure.Aquaphotomics was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK compounds.Princi-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 respectively.These activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient concentrations.Exper-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%,respectively.The 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) respectively.This novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a 50%reduction of energy consumption.This same approach can be extended to measure secondary macronutrients and micronutrients.展开更多
文摘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 solutions.Shadow and angular brightness due to the presence of photosynthetic light unevenly illuminate crop canopy.In 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 intensities.Consumer-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life cycle.The 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 images.Hybrid 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 homogeneity.Strong 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,respectively.This 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.
文摘Water quality assessment is currently based on time-consuming and costly laboratory pro-cedures and numerous expensive physicochemical sensors deployment.In 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 paper.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 structure.Aquaphotomics was applied on ultraviolet,visible light,and near-infrared spectral regions,100 to 1000 nm,to determine NPK compounds.Princi-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 respectively.These activated water bands were used as wave-length protocols to spectrophotometrically measure macronutrient concentrations.Exper-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%,respectively.The 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) respectively.This novel approach of developing a physicochemical estimation model predicted macronutrient concentra-tions in real-time using physical limnological sensors with a 50%reduction of energy consumption.This same approach can be extended to measure secondary macronutrients and micronutrients.
