Agriculturally beneficial soil microorganisms (biologicals) have long been recognized as a component to enhance crop growth and yield. However, efficacy and predictability of the microbes have been low because of a knowledge gap between lab-based selection and field performance. Bayer's Crop Science research group focuses on a key prerequisite to close this gap by understanding the plant physiological parameters improved by the application of several hundred biologicals as seed coatings and then selecting the best performers using desirable phenotypes at greenhouse prior to field screening. In such large scale screening efforts, the challenge lies in obtaining manual phenotyping measurements, which is both tedious and inefficient. Digital phenotyping tools were adopted to address this challenge. PlantEye (PE) laser scanner was selected as the high-throughput phenotyping technology for evaluation. Specifically, suitability of PE was evaluated in the greenhouse to screen biological seed treatments in soybean [Glycine max (L.) Merr.]. The adoption of PE digital phenotyping technology was based on successfully evaluating and establishing its comparability to a standard manual measurement using a leaf area meter (i.e., reference method). Initial study performed in soybean showed a strong correlation (0.91) between reference and PE measurements. However, an important bottleneck is defining criteria for validating the results in the context of application to a screening program. In this study, exploratory analyses were performed to evaluate effect of experimental parameters (e.g., soil type, plant location in greenhouse) on plant phenotypic variation. In addition, a standard methodology is proposed to establish the comparability between reference method and PE by formulating a quantitative approach. The approach can be applied to PE as well as similar digital phenotyping tools.

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.