TL;DR: A deep learning-based semantic segmentation method was developed for automatically performing segmenting trunks, branches, and trellis wires for automating apple tree training in orchard environment and showed a promising potential for adopting deep learning.
TL;DR: A tree trunk detection pipeline for identifying individual trees in a trellis structured apple orchard, using ground‐based lidar and image data and a hidden semi‐Markov model to leverage from contextual information provided by the repetitive structure of an orchard is presented.
Abstract: The ability of robots to meticulously cover large areas while gathering sensor data has widespread applications in precision agriculture. For autonomous operations in orchards, a suitable information management system is required, within which we can gather and process data relating to the state and performance of the crop over time, such as distinct yield count, canopy volume, and crop health. An efficient way to structure an information system is to discretize it to the individual tree, for which tree segmentation/detection is a key component. This paper presents a tree trunk detection pipeline for identifying individual trees in a trellis structured apple orchard, using ground-based lidar and image data. A coarse observation of trunk candidates is initially made using a Hough transformation on point cloud lidar data. These candidates are projected into the camera images, where pixelwise classification is used to update their likelihood of being a tree trunk. Detection is achieved by using a hidden semi-Markov model to leverage from contextual information provided by the repetitive structure of an orchard. By repeating this over individual orchard rows, we are able to build a tree map over the farm, which can be either GPS localized or represented topologically by the row and tree number. The pipeline was evaluated at a commercial apple orchard near Melbourne, Australia. Data were collected at different times of year, covering an area of 1.6 ha containing different apple varieties planted on two types of trellis systems: a vertical I-trellis structure and a Guttingen V-trellis structure. The results show good trunk detection performance for both apple varieties and trellis structures during the preharvest season 87-96% accuracy and near perfect trunk detection performance 99% accuracy during the flowering season.
TL;DR: Calamus moti and C. australis in tropical forest on the Atherton Tableland, Queensland, Australia, climb with the aid of long whip-like flagella covered with sharp hooks to span larger gaps between successive supports than other types of climbers.
Abstract: Calamus moti and C. australis in tropical forest on the Atherton Tableland, Queensland, Australia, climb with the aid of long whip-like flagella covered with sharp hooks. Stiff stems and long flagella allow climbing palms to span larger gaps between successive supports than other types of climbers. Furthermore, recurved hooks on the flagella serve as a ratchet-like mechanism that draws climbing palms closer to supporting plants. Although both palm species climbed up through closed canopy forest, they were more abundant on treefall gap margins. Many gap-edge climbers survived after their supporting trees fell and grew back upwards on gap-edge trees. Once in the canopy, the climbing palms avoided growing up and out of the tops of their supporting trees through the combined effects of decreased internode length and downward slippage of the dangling stem.
TL;DR: In this paper, ultraviolet energy in the germicidal band is directed towards grape vines on the vineyard trellis, providing a lethal exposure (irradiation) for the purpose of eradicating microorganisms, and stimulating host plant defense mechamisms.
Abstract: Ultraviolet energy in the germicidal band is directed towards plants, particularly the grape vines on the vineyard trellis, providing a lethal exposure (irradiation) for the purpose of eradicating microorganisms, and stimulating host plant defense mechamisms.