This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from $0.807$ to $0.838$ for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km²). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m²); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3% ± 6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity.

Visual Odometry is a key technology for robust and accurate navigation of unmanned aerial vehicles in a number of low altitude applications (<120 m), particularly in environments where access to a global positioning system is possible, but not guaranteed. Navigating via vision alone reduces dependence on a global positioning system and other global navigation satellite systems, enhancing navigation robustness even in the presence of jamming, spoofing or long dropouts. To date, however, most demonstrations of visual odometry are in close proximity to the ground or other structures and are often implemented as a monocular camera combined with inertial sensing, rather than vision alone, to account for scale drift. Stereo visual odometry has received little attention for applications beyond 30 m altitude due to the generally poor performance of stereo rigs for these extremely small baseline-to-depth ratios, otherwise termed long-range stereo. This paper demonstrates stereo visual pose estimation at altitudes of up to 120 m above ground level on a small fixed-wing unmanned aerial vehicle by adapting the traditional stereo visual odometry paradigm to explicitly account for inaccurate triangulation and poorly observed scale from the stereo baseline. In addition, issues related to long-range stereo such as biased sensing are investigated to justify the approach, and a novel bundle adjustment algorithm is presented capable of handling vibration induced structural deformation between the cameras. This is achieved by continually optimizing the stereo transform within a set of inequality bounds. Results are presented demonstrating the algorithm on field-gathered data from a 2 m wingspan fixed-wing unmanned aerial vehicle flying at 30-120 m altitude over a 6.5 km trajectory.