  Seed phenotyping is the idea of analyzing the morphometric characteristics of
a seed to predict the behavior of the seed in terms of development, tolerance
and yield in various environmental conditions. The focus of the work is the
application and feasibility analysis of the state-of-the-art object detection
and localization neural networks, Mask R-CNN and YOLO (You Only Look Once), for
seed phenotyping using Tensorflow. One of the major bottlenecks of such an
endeavor is the need for large amounts of training data. While the capture of a
multitude of seed images is taunting, the images are also required to be
annotated to indicate the boundaries of the seeds on the image and converted to
data formats that the neural networks are able to consume. Although tools to
manually perform the task of annotation are available for free, the amount of
time required is enormous. In order to tackle such a scenario, the idea of
domain randomization i.e. the technique of applying models trained on images
containing simulated objects to real-world objects, is considered. In addition,
transfer learning i.e. the idea of applying the knowledge obtained while
solving a problem to a different problem, is used. The networks are trained on
pre-trained weights from the popular ImageNet and COCO data sets. As part of
the work, experiments with different parameters are conducted on five different
seed types namely, canola, rough rice, sorghum, soy, and wheat.
