Abstract-The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields would allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse -small samples are taken from the vineyards during the growing season and extrapolated to determine overall yield. We present an automated method that uses computer vision to identify and count grape berries. These counts are used to generate per vine estimates of crop yield. Both shape and visual texture are used to detect berries. We demonstrate detection of green berries against a green leaf background. We present crop yield estimation results, with the actual harvest yield as groundtruth for 200 vines (over 450 meters) of two different grape varieties. We calibrate our berry count to yield and find that we can predict yield to within 9.8% of actual crop weight.

Abstract-Geolocation of a ground object or target of interest from live video is a common task required of small and micro unmanned aerial vehicles (SUAVs and MAVs) in surveillance and rescue applications. However, such vehicles commonly carry low-cost and light-weight sensors providing poor bandwidth and accuracy whose contribution to observations is nonlinear, resulting in poor geolocation performance by standard techniques. This paper proposes the application of an efficient over-parameterized state representation to the problem of geolocation that is able to handle large, time-varying, and non-Gaussian sensor error to produce better geolocation estimates than typical approaches and which provides computing and communication benefits in applications such as predictive control and distributed collaboration. We evaluate our filter on real flight data, demonstrating its ability to efficiently produce a solution with tight confidence bounds given highly uncertain data.

Abstract-Rivers with heavy vegetation are hard to map from the air. Here we consider the task of mapping their course and the vegetation along the shores with the specific intent of determining river width and canopy height. A complication in such riverine environments is that only intermittent GPS may be available depending on the thickness of the surrounding canopy. We present a multimodal perception system to be used for the active exploration and mapping of a river from a small rotorcraft flying a few meters above the water. We describe three key components that use computer vision, laser scanning, and inertial sensing to follow the river without the use of a prior map, estimate motion of the rotorcraft, ensure collisionfree operation, and create a three dimensional representation of the riverine environment. While the ability to fly simplifies the navigation problem, it also introduces an additional set of constraints in terms of size, weight and power. Hence, our solutions are cognizant of the need to perform multi-kilometer missions with a small payload. We present experimental results along a 2km loop of river using a surrogate system.