An experimental method is described which allows estimation of remanent polarization and coercive field without assuming functional forms for the capacitive and electrical resistance terms. The method can be used to measure polarization in specimens with voltage-dependent conductivity (often arising from the presence of ions in the specimens), voltage-dependent capacitance, and significant amounts of space charge. It consists of: (1) performing bipolar current/voltage hysteresis loops to allow a steady state of remanent polarization and space charge to build up in the specimen, and (2) following a bipolar loop with two or more unipolar loops in which the polarization changes in the first unipolar loop. Both sinusoidal and linear time-dependent applied voltages may be used. Automatic data processing of hysteresis loops is described for cases in which specimen behavior may be considered to be ideal.

Motivation Image-based profiling combines high-throughput screening with multiparametric feature analysis to capture the effect of perturbations on biological systems. This technology has attracted increasing interest in the field of plant phenotyping, promising to accelerate the discovery of novel herbicides. However, the extraction of meaningful features from unlabeled plant images remains a big challenge. Results We describe a novel data-driven approach to find feature representations from plant time-series images in a self-supervised manner by using time as a proxy for image similarity. In the spirit of transfer learning, we first apply an ImageNet-pretrained architecture as a base feature extractor. Then, we extend this architecture with a triplet network to refine and reduce the dimensionality of extracted features by ranking relative similarities between consecutive and nonconsecutive time points. Without using any labels, we produce compact, organized representations of plant phenotypes and demonstrate their superior applicability to clustering, image retrieval and classification tasks. Besides time, our approach could be applied using other surrogate measures of phenotype similarity, thus providing a versatile method of general interest to the phenotypic profiling community. Availability Source code is provided in https://github.com/bayer-science-for-a-better-life/plant-triplet-net Supplementary information Supplementary data are available at Bioinformatics online.

Use of hyperspectral imaging (HSI) for automated characterisation of plants in a high-throughput plant phenotyping setup (HTPPS) is a challenging task. A challenge arises when the same plant is being monitored automatically during the experiment as it might not be in the same orientation as it was imaged last time. Such changes in orientation result in variations in illumination, which affects the signals recorded by the HSI setup. In addition, there are challenges with the use of threshold-based segmentation approaches such as normalised difference vegetation index (NDVI) for distinguishing between old and dead leaves, which might be observed in the later stages of experiments, from the soil background. Therefore, the potential of spectral normalisation for homogenising HS images and the use of supervised spectral set for plant segmentation is presented. Further, the effects of testing chemicals on plants were visualised using PCA of the HS images.