Unmanned aircraft systems (UAS) are now widely used in field phenotyping by industry and academia because they are convenient, fast, and require less labor than manual phenotyping methods. UAS-based phenotyping requires significant effort in data collection, processing, and resource management, as well as specialized expertise in flight operation and remote sensing. The Indiana Corn and Soybean Innovation Center (ICSC) at Purdue University operates multisensor platforms to provide reliable field phenotyping services to many clients in academia and industry. According to the experience with our multisensor platforms to date, we present practical data collection and processing pipelines for (a) creating geospatial datasets from RGB, hyperspectral, lidar, and thermal sensors, (b) extracting plant phenotypes from the multisensor datasets, and (c) combining the resultant datasheets with ground truth and treatment information. While processing techniques may vary depending on the vendor or sensing mechanisms, the general concept is up-to-date and can be relevant to those who are currently operating or planning to adopt UAS phenotyping systems in the near future.

Hyperspectral imaging is a non-destructive imaging technique used in plant phenotyping to collect and analyze an array of electromagnetic information in visible (380-700 nm) and near-infrared wavelengths region (700-2,500 nm). Hyperspectral imaging can provide information of plant responses under various biotic and abiotic stress, e.g., drought, temperature rising, disease, and nutrition deficiency. We present a hyperspectral data processing pipeline designed for the data collected at Ag Alumni Seed Phenotyping Facility (AAPF) in Purdue University, USA. The procedure consists of initializing a processing session, radiometric calibration with white and dark references, geometric calibration (registration) of visible and near infrared (VNIR) and shortwave infrared (SWIR) images, vegetation and non-vegetation classification, vegetation indices calculation of a plant area, exporting data products, and quality control. In concern of large data size of hyperspectral data, we highlight the need to save memory usage during computation and save disk space for data products. We also address the need of human interpretable images in the hyperspectral data products for plant scientists without experiences in hyperspectral imaging. We expect the developed procedure could improve robustness of large hyperspectral data processing and promote the usage of hyperspectral data by increasing interpretability.