Automated Extraction of Pharmacokinetic Parameters from Structured XML Scientific Articles: Enhancing Data Accessibility at Scale

In the field of pharmacology, there is a notable absence of centralized, comprehensive, and up-to-date repositories of PK data. This poses a significant challenge for R&D as it can be a time-consuming and challenging task to collect all the required quantitative PK parameters from diverse scientific publications. This quantitative PK information is predominantly organized in tabular format, mostly available as XML, HTML, or PDF files within various online repositories and scientific publications, including supplementary materials. This makes tables one of the crucial components and information elements of scientific or regulatory documents as they are commonly utilized to present quantitative information. Extracting data from tables is typically a labor-intensive process, and alternative automated machine learning models may struggle to accurately detect and extract the relevant data due to the complex nature and diverse layouts of tabular data. The difficulty of information extraction and reading order detection is largely dependent on the structural complexity of the tables. Efforts to understand tables should prioritize capturing the content of table cells in a manner that aligns with how a human reader naturally comprehends the information. FARAD has been manually extracting tabular data and other information from literature and regulatory agencies for over 40 years. However, there is now an urgent need to automate this process due to the large volume of publications released daily. The accuracy of this task has become increasingly challenging, as manual extraction is tedious and prone to errors, especially given the staffing shortages we are currently facing. This necessitates the development of AI algorithms for table detection and extraction that are able to precisely handle cells organized according to the table structure, as indicated by column and/or row header information.

Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery

Accurate, high-throughput phenotyping is a critical component of modern crop breeding programs, especially for improving traits such as mechanical stability, biomass production, and disease resistance. Stalk diameter is a key structural trait, but traditional measurement methods are labor-intensive, error-prone, and unsuitable for scalable phenotyping. In this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery. Our method integrates deep learning-based instance segmentation, 3D point cloud reconstruction, and axis-aligned slicing via Principal Component Analysis (PCA) to perform robust diameter estimation. By mitigating the effects of curvature, occlusion, and image noise, this approach offers a scalable and reliable solution to support high-throughput phenotyping in breeding and agronomic research.

Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time

Effective seed sowing in precision agriculture is hindered by challenges such as residue accumulation, low soil temperatures, and hair pinning (crop residue pushed in the trench by furrow opener), which obstruct optimal trench formation. Row cleaners are employed to mitigate these issues, but there is a lack of quantitative methods to assess trench cleanliness. In this study, a novel computer vision-based method was developed to evaluate row cleaner performance. Multiple air seeders were equipped with a video acquisition system to capture trench conditions after row cleaner operation, enabling an effective comparison of the performance of each row cleaner. The captured data were used to develop a segmentation model that analyzed key elements such as soil, straw, and machinery. Using the results from the segmentation model, an objective method was developed to quantify row cleaner performance. The results demonstrated the potential of this method to improve row cleaner selection and enhance seeding efficiency in precision agriculture.