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

作　　者：Ronnie Concepcion II Elmer Dadios Edwin Sybingco Argel Bandala 

机构地区：[1]Department of Manufacturing Engineering and Management,De La Salle University,Manila 1004,Philippines [2]Department of Electronics and Computer Engineering,De La Salle University,Manila 1004,Philippines [3]Center for Engineering and Sustainable Development Research,De La Salle University,Manila 1004,Philippines

出　　处：《Information Processing in Agriculture》2023年第3期312-333,共22页农业信息处理（英文）

摘　　要：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.

关 键 词：LETTUCE Plant phenotype Precision farming Remote sensing Swarm intelligence Vegetation index 

分 类 号：S636.2[农业科学—蔬菜学]