AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize
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AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize # Stomata Images Dataset (2025) ## 1. Project Description This dataset contains images of stomata taken with a hand-held digital microscope (DinoLite). Each image was manually labeled to recognize the genotype from which it was taken. ## 2. Dataset Structure In the dataset_imaging folder there are all the acquired images. ## 3. Labeling Methodology - Tool used: DinoCapture - Zoom: 220x magnification - Format: jpeg - Area image: 2.39 mm^2 ## 4. Label Format Each `.jpeg` file follows this structure: treat_code.batch.block.plant_number.top/bottom.image_replicate Example: 131.3.2.5.t.2 ## 5. Interpretation - 131 = `treat_code` is always the code related to the genotype, it is associated to a geno_code for each genotype - 3 = `batch` is the first level of grouping the genotype inside the experiment (from 1 to 4) - 2 = `block` is the second level of grouping the genotype inside the experiment (from 1 to 4) - 5 = `plant_number` is the second level of grouping the genotype inside the experiment (from 1 to 6) - t = `top/bottom` is related to the upper or lower side of the leaf (t or b) - 2 = `image_replicate` is related to the number of image taken from the same side (1 and 2) In the experiment there are: - 4 batch (batch) - in each batch 4 blocks (block) - in each block 24 entry (treat_code) - for each entry we have up to 6 plants (plant_number) ## 6. Author - Name: Martina Sechi - Contact: martina.sechi@santannapisa.it Images were acquired with the contribution of Martina Pallaoro.
