Abstract
Stomata are pores in the epidermal tissue of plants formed by specialized cells called occlusive cells or guard cells. Analyzing the number and behavior of stomata is a task carried out by studying microscopic images, and that can serve, among other things, to better manage crops in agriculture. However, quantifying the number of stomata in an image is an expensive process since a stomata image might contain dozens of stomata. Therefore, it is interesting to automate such a detection process. This problem can be framed in the context of object detection, a task widely studied in computer vision. Currently, the best approaches to tackle object detection problems are based on deep learning techniques. Although these techniques are very successful, they might be difficult to use. In this work, we face this problem, specifically for the detection of stomata, by building a Jupyter notebook in Google Colaboratory that allows biologists to automatically detect stomata in their images.
Partially supported by Ministerio de Industria, Economía y Competitividad, project MTM2017-88804-P; and Agencia de Desarrollo Económico de La Rioja, project 2017-I-IDD-00018. We also acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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Casado-García, Á., Heras, J., Sanz-Sáez, A. (2020). Google Colaboratory for Quantifying Stomata in Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_29
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