PhenoBot: an automated system for leaf area analysis using deep learning.

MAIN CONCLUSION: A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiation of the leaf canopy, making the leaf area index (LAI) a critical parameter for estimating yields. The growth rate of leaves at different growth stages determines the overall LAI, which is used by crop growth models (CGM) for simulating yield. Consequently, precise phenotyping of the leaves can help elucidate phenological processes relating to resource capturing. A stable system for acquiring images and a strong data processing backend play a vital role in reducing throughput time and increasing accuracy of calculations, compared to manual analysis. However, most available solutions are not dynamic, as they use color-based segmentation, which fails to capture leaves with varying shades and shapes. We have developed a system that uses a low-cost setup to acquire images and an automated pipeline to manage the data storage on the device and in the cloud. The system is powered by virtual machines that run multiple custom-trained deep learning models to segment out leaves, calculate leaf area (LA) for the whole set and at the individual leaf level, overlays important information on the images, and appends them on a compatible file used for CGMs with very high accuracy. The pipeline is dynamic and can be used for multiple crops. The use of open-source hardware, platforms, and algorithms makes this system affordable and reproducible.

Icy: an open bioimage informatics platform for extended reproducible research.

Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/.

Co-designing the Embodiment of a Minimalist Social Robot to Encourage Hand Hygiene Practises Among Children in India.

We conducted an empirical study to co-design a social robot with children to bring about long-term behavioural changes. As a case study, we focused our efforts to create a social robot to promote handwashing in community settings while adhering to minimalistic design principles. Since cultural views influence design preferences and technology acceptance, we selected forty children from different socio-economic backgrounds across India as informants for our design study. We asked the children to design paper mock-ups using pre-cut geometrical shapes to understand their mental models of such a robot. The children also shared their feedback on the eight resulting different conceptual designs of minimalistic caricatured social robots. Our findings show that children had varied expectations of the robot's emotional intelligence, interactions, and social roles even though it was being designed for a specific context of use. The children unequivocally liked and trusted anthropomorphized caricatured designs of everyday objects for the robot's morphology. Based on these findings, we present our recommendations for the physical and interaction features of a minimalist social robot assimilating the children's inputs and social robot design principles grounded in prior research. Future studies will examine the children's interactions with a built prototype.

Characterizing the 3-D field distortions in low numerical aperture fluorescence zooming microscope.

In this article, we characterize the lateral field distortions in a low numerical aperture and large field-of-view (FOV) fluorescence imaging system. To this end, we study a commercial fluorescence MACROscope setup, which is a zooming microscope. The versatility of this system lies in its ability to image at different zoom ranges, so that sample preparations can be examined in three-dimensions, at cellular, organ and whole body levels. Yet, we found that the imaging system's optics are optimized only for high magnifications where the observed FOV is small. When we studied the point-spread function (PSF) by using fluorescent polystyrene beads as "guide-stars", we noticed that the PSF is spatially varying due to field distortions. This variation was found to be laterally symmetrical and the distortions were found to increase with the distance from the center of the FOV. In this communication, we investigate the idea of using the field at the back focal plane of an optical system for characterizing distortions. As this field is unknown, we develop a theoretical framework to retrieve the amplitude and phase of the field at the back focal pupil plane, from the empirical bead images. By using the retrieved amplitude, we can understand and characterize the underlying cause of these distortions. We also propose a few approaches, before acquisition, to either avoid it or correct it at the optical design level.

Blind deconvolution for thin-layered confocal imaging.

We propose an alternate minimization algorithm for estimating the point-spread function (PSF) of a confocal laser scanning microscope and the specimen fluorescence distribution. A three-dimensional separable Gaussian model is used to restrict the PSF solution space and a constraint on the specimen is used so as to favor the stabilization and convergence of the algorithm. The results obtained from the simulation show that the PSF can be estimated to a high degree of accuracy, and those on real data show better deconvolution as compared to a full theoretical PSF model.

Parametric blind deconvolution for confocal laser scanning microscopy.

In this paper, we propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscopic (CLSM) images and parametric estimation of the acquisition system's Point Spread Function (PSF). The CLSM is an optical fluorescence microscope that scans a specimen in 3D and uses a pinhole to reject most of the out-of-focus light. However, the quality of the images suffers from two basic physical limitations. The diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier cause blur and photon counting noise respectively. These images can hence benefit from post-processing restoration methods based on deconvolution. An efficient method for parametric blind image deconvolution involves the simultaneous estimation of the specimen 3D distribution of fluorescent sources and the microscope PSF. By using a model for the microscope image acquisition physical process, we reduce the number of free parameters describing the PSF and introduce constraints. The parameters of the PSF may vary during the course of experimentation, and so they have to be estimated directly from the observed data. A priori model of the specimen is further applied to stabilize the alternate minimization algorithm and to converge to the solutions.