Genetic Algorithm-Based Visible Band Tetrahedron Greenness Index Modeling for Lettuce Biophysical Signature Estimation

Lightness signal and color reflectance constitute the reflected luminance spectra from camera captured image to camera lenses. The intensity of lightness and visible RGB signals deviates as the camera distance to object varies. The presence of uneven distribution of photosynthetic light causes angular light effect of shadowing on the focal object and light emitting objects placed on the visually noisy background added a challenge in materializing an efficient greenness index for crop phenotyping. The proposed method in this study compensates excessive relative brightness on the image by introducing lightness rectification coefficient and employing genetic algorithm to derive a novel visible tetrahedron greenness index (gvTeGI) based on normalized green waveband. Hybrid neighborhood component analysis and Pearson’s correlation coefficient approach for feature selection resulted to retaining photosynthetic canopy area, and correlation and homogeneity texture features as highly important descriptors for biophysical signatures considered in this study which are lettuce fresh weight, height and number of spanning leaves. The selection, crossover and mutation rates used to optimize the genetic algorithm model are 0.2, 0.8 and 0.01 respectively. Indoor and outdoor aquaponic system was deployed for 6-week full crop life cycle cultivation. Regression machine learning models were used to estimate biophysical signatures from extracted gvTeGI channels. Optimized Gaussian processing regression model bested regression support vector machine and regression tree in estimating fresh weight, height and number of spanning leaves with R2 values of 0.7939, 0.7662 and 0.7446. The proposed gvTeGI proved to be more accurate than previously published greenness index for the estimation of biophysical signatures of lettuce using consumer-grade RGB camera.