Anupama Anupama

TL;DR: A novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification shows a superior performance in contrast to the existing stomATA detection methods in terms of precision and recall.

TL;DR: In this paper, morphological, physiological, biochemical and molecular variations between drought tolerant (PB6 and Moroberakan) and drought sensitive (Way Rarem) varieties have been evaluated, and notable differences have been observed in root morphology, root xylem number and area, stomata number, relative water content, proline content, protein and gene expression.

TL;DR: Assessment of the correlation between genotypic and phenotypic traits that can contribute towards the emerging field of rice phenomics finds that there is a notable difference in gene expression of OsPIP2;5 and OsNip2;1 in various indica varieties of rice at different time periods of stress.

TL;DR: A novel automated framework for stomata quantification is proposed based on a hybrid approach where the candidateStomata region is first detected by a convolutional neural network and the occlusion is dealt with an inpainting algorithm to solve the problem of shape, scale and Occlusion in an end-to-end manner.

TL;DR: An image processing pipeline is developed that comprises of low level processing which enables high-throughput detection of xylem vessels and successfully captures the phenotypic difference between MTU-1010 (d drought susceptible rice cultivar) and Sahbhagi Dhan (drought tolerant Rice cultivar).