Automatic seed classification for four paramo plant species by neural networks and optic RGB images

There is a need for robust methodological approaches to improve our capacity to automatically detect plant species from seed samples tohelp support plant management strategies. In this study, we tested different neural network techniques to automatically detect native species from seeds from the Andean paramo region based on optic RGB images. Specifically, we compared i) simple feed-forward networks (SNNs), consisting of feed-forward nets with error back-propagation, holding one hidden layer with different number of neurons; and ii) deep convolutional neural networks (CNNs), which have their convolutional layers-built form multiple 3x3 kernels. First, we sampled 50 seeds from four common plant species in the La Rusia Paramo (Colombia): Espeletia congestiflora, Bucquetia glutinosa, Calamagrostis effusa and Puya santosii. We took RGB images of individual seeds for each species on contrasted white and black backgrounds, and then classified all images under both SNNs and CNNs. Under a double cross-validation scheme, the SNN approach with 14 neurons approached 88% of test accuracy, while CNN achieved 93%. Moreover, when increasing the image sample in the training dataset fed to models, CNN performed with 100% accuracy when used on testing and validation datasets. Overall, the neural network approach explored here suggests a promising methodology for species prediction from seeds based on optical RGB images, with potential for automatic seed recognition and counting on the field.

Automated summary

This study tested different neural network techniques to automatically detect native plant species from seed samples based on optic RGB images. The deep convolutional neural network (CNN) approach achieved 100% accuracy when used on testing and validation datasets, showing promise for species prediction from seeds based on optical RGB images.