Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm²), whereas image-based methods allow the sampling of entire plots (2 to 30 m²). A higher aerial position allows the rapid measurement of large numbers of plots if the altitude is low (10 to 40 m) and the flight control is sufficiently precise to collect high-resolution images. This paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

This paper presents the design of an autonomous unmanned helicopter system for low-altitude remote sensing. The proposed concepts and methods are generic and not limited to a specific helicopter. The development was driven by the need for a dependable, modular, and affordable system with sufficient payload capacity suitable for both research and real-world deployment. The helicopter can be safely operated without a backup pilot in a contained area beyond visual range. This enables data collection in inaccessible or dangerous areas. Thanks to its terrain following and obstacle avoidance capability, the system does not require a priori information about terrain elevation and obstacles. Missions are specified in state diagrams and flight plans. We present performance characteristics of our system and show results of its deployment in real-world scenarios. We have successfully completed several dozen infrastructure inspection missions and crop monitoring missions facilitating plant phenomics studies.

Satellite imagery, UAV imagery, and geo-referenced underwater photo transects (from the surface) are different methods used in marine monitoring and benthic habitat mapping applications to collect observations at different spatial scales. There are however challenges in linking them all together to provide fine-grained mapping and analysis for underwater, benthic habitats with complex geometric and ecological properties. We propose a novel framework called SkySea that offers users access to aligned observational data at multiple spatial scales. SkySea can integrate satellite images (e.g., from SENTINEL-2 at 10m resolution), UAV images (<5cm ground sampling distance), detailed underwater images, 3D reconstruction of the seafloor/benthos from underwater images, and make the data available through a commonly used user interface, such as QGIS. Initial evaluation indicates that the spatial overlay achieves sub-meter-level accuracy, while the underwater 3D reconstruction reaches an average relative error of less than 10% for size estimation with reference objects. We believe that this is a novel and innovative framework to achieve a seamless connection across an enormous gap of scales from satellite images, regional UAV images, local underwater images and local 3D reconstruction of the underwater environment, for benthic habitat mapping. It enables marine biologists to perform survey planning, species mapping, and model validation tasks in an integrated pipeline.

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.