RRC ID 70587
著者 Toda Y, Tameshige T, Tomiyama M, Kinoshita T, Shimizu KK.
タイトル An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation.
ジャーナル Front Plant Sci
Abstract Recent technical advances in the computer-vision domain have facilitated the development of various methods for achieving image-based quantification of stomata-related traits. However, the installation cost of such a system and the difficulties of operating it on-site have been hurdles for experimental biologists. Here, we present a platform that allows real-time stomata detection during microscopic observation. The proposed system consists of a deep neural network model-based stomata detector and an upright microscope connected to a USB camera and a graphics processing unit (GPU)-supported single-board computer. All the hardware components are commercially available at common electronic commerce stores at a reasonable price. Moreover, the machine-learning model is prepared based on freely available cloud services. This approach allows users to set up a phenotyping platform at low cost. As a proof of concept, we trained our model to detect dumbbell-shaped stomata from wheat leaf imprints. Using this platform, we collected a comprehensive range of stomatal phenotypes from wheat leaves. We confirmed notable differences in stomatal density (SD) between adaxial and abaxial surfaces and in stomatal size (SS) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.
巻・号 12
ページ 715309
公開日 2021-1-1
DOI 10.3389/fpls.2021.715309
PMID 34394171
PMC PMC8358771
IF 4.402
リソース情報
コムギ KU-2076 KU-199-1 LPGKU2305 LPGKU2306 LPGKU2307 LPGKU2308 LPGKU2309 LPGKU2310 LPGKU2311 LPGKU2312 LPGKU2313 LPGKU2314 LPGKU2315 LPGKU2316 LPGKU2317 LPGKU2318 LPGKU2319 LPGKU2320 LPGKU2321 LPGKU2322 LPGKU2323 LPGKU2324 LPGKU2325 LPGKU2326 LPGKU2327 LPGKU2328 LPGKU2329