  Robotic crop phenotyping has emerged as a key technology to assess crops'
morphological and physiological traits at scale. These phenotypical
measurements are essential for developing new crop varieties with the aim of
increasing productivity and dealing with environmental challenges such as
climate change. However, developing and deploying crop phenotyping robots face
many challenges such as complex and variable crop shapes that complicate
robotic object detection, dynamic and unstructured environments that baffle
robotic control, and real-time computing and managing big data that challenge
robotic hardware/software. This work specifically tackles the first challenge
by proposing a novel Digital-Twin(DT)MARS-CycleGAN model for image augmentation
to improve our Modular Agricultural Robotic System (MARS)'s crop object
detection from complex and variable backgrounds. Our core idea is that in
addition to the cycle consistency losses in the CycleGAN model, we designed and
enforced a new DT-MARS loss in the deep learning model to penalize the
inconsistency between real crop images captured by MARS and synthesized images
sensed by DT MARS. Therefore, the generated synthesized crop images closely
mimic real images in terms of realism, and they are employed to fine-tune
object detectors such as YOLOv8. Extensive experiments demonstrated that our
new DT/MARS-CycleGAN framework significantly boosts our MARS' crop object/row
detector's performance, contributing to the field of robotic crop phenotyping.
