Data from: In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics
收藏DataCite Commons2026-01-28 更新2026-04-25 收录
下载链接:
https://datadryad.org/dataset/doi:10.5061/dryad.9kd51c5vm
下载链接
链接失效反馈官方服务:
资源简介:
Precision agriculture aims to increase crop yield while reducing the use
of harmful chemicals (e.g., pesticides, excess fertilizer) by employing
minimal, tailored interventions. These strategies, however, are limited by
(i) sensor quality, which typically relies on visual plant expressions,
and (ii) the manual, destructive nature of many non-visual measurement
methods, such as the Scholander pressure bomb. By automating more intimate
interactions with foliage, in vivo, it would be possible to inject
chemical and biological probes that reveal more phenotypes, such as water
stress in response to varying environmental conditions, and visible gene
expression to measure the success of gene engineering applications. To
address this, we developed a soft robotic leaf gripper and
stamping-injection method to improve foliar delivery of nanoscale
synthetic and biological probes. This allows for non-destructive, in situ,
multi-species applications. We used two probes: (i) Agrobacterium
tumefaciens carrying the RUBY gene as a reporter system for plant
transformation, and (ii) nanoparticle hydrogels for measuring leaf water
potential (ψ). Our hourglass-shaped design enabled the gripper to achieve
higher forces with reduced radial expansion, resulting in an injection
success rate above 91%. Studies on sunflower (Helianthus annuus L.) and
cotton (Gossypium hirsutum L.) showed our method achieved an average
12-fold increase in infiltration areas, with significantly less leaf
damage—3.6% in sunflower and none in cotton—compared to the needle-free
syringe method. Enabling long periods of successful in vivo phenotyping on
both species following precise and safe foliar delivery underscores the
potential of the leaf gripper for robotic plant bioengineering.
提供机构:
Dryad
创建时间:
2025-12-11



