Single and multi-analyte deep learning-based analysis framework for class prediction in biological images

Abstract

Measurement of biological analytes, characterizing flavor in fruits, is a cumbersome, expensive and time-consuming process. Fruits with higher concentration of analytes have greater commercial or nutritional values. Here, we tested a deep learning-based framework with fruit images to predict the class (sweet or sour and high or low) of analytes using images from two types of trees in a single and multi-analyte mode. We used fruit images from kinnow (n = 3451), an edible hybrid mandarin and neem (n = 1045), a tree with agrochemical and pharmaceutical properties. We measured sweetness in kinnows and five secondary metabolites in neem fruits (azadirachtin or A, deacetyl-salannin or D, salannin or S, nimbin or N and nimbolide or E) using a refractometer and high-performance liquid chromatography, respectively. We trained the models for 300 epochs, before and after evolution of hyper-parameters, using 300 generations with 50 epochs/generation, estimated the best models and evaluated their performance on 10 percent of independent images. The validation F1score and test accuracies were 0.79 and 0.77 and 82.55 percent and 60.8 percent respectively for kinnow and neem A analyte. A multi-analyte model enhanced the prediction of the neem model to high class when the D N S combined class predictions were high low high and to low class when D N combined class predictions were low:high respectively. The test accuracy increased further to nearly 70 percent with a 10-fold cross-validation error of 0.257 across ten randomly split train validation test sets proving the potential of a multi-analyte model to enhance the prediction accuracy, especially when the numbers of images are limiting.