{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:09:18Z","timestamp":1772788158436,"version":"3.50.1"},"reference-count":63,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"103","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2025,6,11]]},"abstract":"<jats:p>\n            Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including 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\u2014such 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 nondestructive, in situ, multispecies applications. We used two probes:\n            <jats:italic toggle=\"yes\">Agrobacterium tumefaciens<\/jats:italic>\n            carrying the\n            <jats:italic toggle=\"yes\">RUBY<\/jats:italic>\n            gene as a reporter system for plant transformation and nanoparticle hydrogels for measuring leaf water potential (\u03c8). Our hourglass-shaped design enabled the gripper to exert higher forces with reduced radial expansion compared with conventional designs, achieving an injection success rate above 91%. Studies on sunflower (\n            <jats:italic toggle=\"yes\">Helianthus annuus<\/jats:italic>\n            L.) and cotton (\n            <jats:italic toggle=\"yes\">Gossypium hirsutum<\/jats:italic>\n            L.) showed that our method achieved an average 12-fold increase in infiltration areas, with substantially less leaf damage\u20143.6% in sunflower and none in cotton\u2014compared with the needle-free syringe method. Enabling long periods of successful in vivo phenotyping on both species after precise and safe foliar delivery underscores the potential of the leaf gripper for robotic plant bioengineering.\n          <\/jats:p>","DOI":"10.1126\/scirobotics.adu2394","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T17:58:14Z","timestamp":1749664694000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":4,"title":["In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics"],"prefix":"10.1126","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-5217","authenticated-orcid":true,"given":"Mehmet Mert","family":"\u0130lman","sequence":"first","affiliation":[{"name":"Sibley School of Aerospace and Mechanical Engineering, Cornell University, Ithaca, NY 14853, USA."},{"name":"Department of Mechanical Engineering, Hasan Ferdi Turgutlu Faculty of Technology, Manisa Celal Bayar University, Manisa 45400, T\u00fcrkiye."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0937-2324","authenticated-orcid":true,"given":"Annika","family":"Huber","sequence":"additional","affiliation":[{"name":"Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7423-7850","authenticated-orcid":true,"given":"Anand K.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Sibley School of Aerospace and Mechanical Engineering, Cornell University, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1015-5316","authenticated-orcid":true,"given":"Sabyasachi","family":"Sen","sequence":"additional","affiliation":[{"name":"Sibley School of Aerospace and Mechanical Engineering, Cornell University, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2755-1369","authenticated-orcid":true,"given":"Fumin","family":"Wang","sequence":"additional","affiliation":[{"name":"Boyce Thompson Institute, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1216-6442","authenticated-orcid":true,"given":"Tiffany","family":"Lin","sequence":"additional","affiliation":[{"name":"Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9675-934X","authenticated-orcid":true,"given":"Georg","family":"Jander","sequence":"additional","affiliation":[{"name":"Boyce Thompson Institute, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8145-9977","authenticated-orcid":true,"given":"Abraham D.","family":"Stroock","sequence":"additional","affiliation":[{"name":"Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA."},{"name":"School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA."},{"name":"Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY 14853, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0631-9587","authenticated-orcid":true,"given":"Robert F.","family":"Shepherd","sequence":"additional","affiliation":[{"name":"Sibley School of Aerospace and Mechanical Engineering, Cornell University, Ithaca, NY 14853, USA."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1183899"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-005-0681-8"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0168-1699(02)00096-0"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-020-09517-2"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eja.2021.126373"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2019.107886"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160701422213"},{"key":"e_1_3_2_9_2","first-page":"102111","article-title":"Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data","volume":"89","author":"Meng X.","year":"2020","unstructured":"X. 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