  The first step toward Seed Phenotyping i.e. the comprehensive assessment of
complex seed traits such as growth, development, tolerance, resistance,
ecology, yield, and the measurement of pa-rameters that form more complex
traits is the identification of seed type. Generally, a plant re-searcher
inspects the visual attributes of a seed such as size, shape, area, color and
texture to identify the seed type, a process that is tedious and
labor-intensive. Advances in the areas of computer vision and deep learning
have led to the development of convolutional neural networks (CNN) that aid in
classification using images. While they classify efficiently, a key bottleneck
is the need for an extensive amount of labelled data to train the CNN before it
can be put to the task of classification. The work leverages the concepts of
Contrastive Learning and Domain Randomi-zation in order to achieve the same.
Briefly, domain randomization is the technique of applying models trained on
images containing simulated objects to real-world objects. The use of synthetic
images generated from a representational sample crop of real-world images
alleviates the need for a large volume of test subjects. As part of the work,
synthetic image datasets of five different types of seed images namely, canola,
rough rice, sorghum, soy and wheat are applied to three different
self-supervised learning frameworks namely, SimCLR, Momentum Contrast (MoCo)
and Build Your Own Latent (BYOL) where ResNet-50 is used as the backbone in
each of the networks. When the self-supervised models are fine-tuned with only
5% of the labels from the synthetic dataset, results show that MoCo, the model
that yields the best performance of the self-supervised learning frameworks in
question, achieves an accuracy of 77% on the test dataset which is only ~13%
less than the accuracy of 90% achieved by ResNet-50 trained on 100% of the
labels.
