Background: Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. While most studies focus on the measurements of leaf area index or detection of specific events such as emergence, little attention has been put on the identification of kinetics of events of early seedling development on a seed to seed basis. Result: Imaging systems screened the whole seedling growth process from the top view. Precise annotation of emergence out of the soil, cotyledon opening, and appearance of first leaf was conducted. This annotated data set served to train deep neural networks. Various strategies to incorporate in neural networks, the prior knowledge of the order of the developmental stages were investigated. Best results were obtained with a deep neural network followed with a long short term memory cell, which achieves more than 90% accuracy of correct detection. Conclusion: This work provides a full pipeline of image processing and machine learning to classify three stages of plant growth plus soil on the different accessions of two species of red clover and alfalfa but which could easily be extended to other crops and other stages of development.

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( ${10}^4$ instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science. Scatter transform only trained on synthetic data shows an accuracy of $85\%$ when tested on real data.

Extremely small cameras and many cell phones simply do not have enough room to allow users to move a rigid lens the distance required for a varying range of focal lengths. An adaptive liquid lens, however, enables small cameras to focus without needing extra room. An autofocus liquid lens provides several advantages over a traditional lens in terms of efficiency, cost, compactness, and flexibility. But one of the main challenges in these lenses is a high driving voltage requirement of around at least 1.8 kV. In this paper, we propose a new design of a liquid lens based on a dielectric elastomer stack actuator (DESA), which significantly overcomes the aforementioned existing problem. The lens consists of a frame (a thin DESA membrane with a hole in the middle), silicon oil, and water. A voltage range is applied on the membrane in order to change the hole dimension. Due to change of hole dimension, a change in meniscus occurs that changes the focal length of the lens. In this research work, various experimental results are achieved by configuring two DESA with different active areas. Depending on the active area of the membrane, the length of the laser beam on the plane varies from 6 to 35 mm, and the driving voltage is in the range of 50–750 V.