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Crop type maps are essential for a wide range of applications such as crop monitoring, and yield estimation. In addition, Earth Observation (EO) systems allow robust and timely mapping of the earth’s surface, usually based on time-series. Yet, existing crop type maps are either global at coarse spatial resolution, or have a local or regional scope. The reasons for this gap can be linked to the scarcity of global crop type datasets at field level to train the models, their bias towards the Northern Hemisphere, as well as the limited transferability of existing crop type models across different regions. One of the main limitations on the transferability is driven by the phenological shift of the crops’ radiometric time series detected by Earth Observation (EO) systems, which is mainly induced by the different climates across regions. In this study, we explore the normalization of EO-based wheat time series with the accumulation of Growing Degree Days (GDD): the Thermo-RAdiometric Normalization of Crop Observations (TRANCO) system. The TRANCO system is based on the assumption that crop phenology evolution is mainly driven by temperature accumulation, represented by the accumulated GDD from Start of Season (SOS) to End of Season (EOS) dates, derived from a crop calendar. We tested the proposed method to normalize wheat on a database of globally distributed samples, whose results show a great improvement of the GDD F1 score (0.90) compared to a simpler normalization approach based on Time windows defined from SOS calendars (0.87) and a baseline without normalization (0.83).

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community., (© 2024 Wiley Periodicals LLC.)

The design of biopharmaceutical processes is predominantly driven by the domain of experimental process design. This approach can be further improved by combining multiple domain information such as experiments, unit models, and flowsheet models. Approaches consisting of methods and flowsheet models provide the framework for exploring, analyzing, and ultimately evaluating the combinatorial space of all possible designs within the molecule-to-manufacturing value chain. In recent years, modular process designs are of interest in the pharmaceutical industry because of the shift toward multiproduct, mutiprocess processes. Therefore, a systematic approach for how to evaluate the utilization of the modular plug-n-play concept provides metrics that can propel modular design from a viable design alternative to the selected alternative for full-scale manufacturing. The objective of this paper is to present such an in silico approach for the evaluation of modular designs. The approach is presented as a systematic method and then, is exemplified through the manufacture of an active pharmaceutical ingredient (API). The application of the method shows how to transition from a typical design-for-purpose design alternative to a modular design through the utilization of data, modeling, simulation, and uncertainty/sensitivity analyses for quantification of various selection metrics such as process robustness and flexibility., (© 2023 Wiley Periodicals LLC.)