6 research outputs found
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Robotic Blossom Thinning System for Tree Fruit Crops
Tree fruit production industry around the world heavily relies on semi-skilled seasonal workforce for critical field operations such as training, pruning, blossom and fruitlet thinning, and harvesting. Blossom thinning is an essential crop-load management technique that relies heavily on laborious and labor-intensive manual operation to achieve the desired thinning result. While large-scale thinning approaches such as chemical and mechanical thinning are available, chemical thinning results can be unpredictable, and mechanical thinning may damage a significant part of the tree canopy while also offering no-to-limited selectivity. Therefore, developing an efficient system that can perform precise blossom thinning in the target canopy regions with high accuracy, effectiveness, and robustness is crucial.This study focused on the design, development, and field evaluation of a robotic blossom thinning system that employed a machine vision system, and a mechatronic system consisting of a robotic manipulator and end-effector for targeted, selective blossom thinning in tree fruit crops. Robust machine vision systems were investigated for the identification, segmentation, density estimation, localization, and counting of apple flowers. Furthermore, a miniature, electrically-actuated end-effector was designed, fabricated, and tested for blossom thinning in space-constrained locations in tree canopies. All these components were integrated to develop a robotic thinning system and evaluated in a commercial orchard. Two thinning methods, boundary and center thinning, were investigated to assess the integrated system performance in selectively thinning flower clusters in target canopy regions. Boundary thinning was used to thin flowers along the flower cluster boundary, whereas center thinning was used to thin flowers by actuating the thinning end-effector at the center of the target cluster. The field evaluation results demonstrated that the integrated system could selectively thin blossoms from targeted clusters based on the chosen thinning strategy. The boundary thinning approach achieved a 67.2% thinning with a cycle time of 9.0 seconds, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When implemented at a wider scale with additional improvements, the proposed system could address the problems associated with current hand, chemical, and mechanical blossom thinning approaches. Furthermore, the proposed system could aid in the commercial viability and practical adoption of the robotic systems intended for operation in tree fruit crops
Neuromorphic perception for greenhouse technology using event-based sensors
Event-Based Cameras (EBCs), unlike conventional cameras, feature independent pixels that asynchronously generate outputs upon detecting changes in their field of view. Short calculations are performed on each event to mimic the brain. The output is a sparse sequence of events with high temporal precision. Conventional computer vision algorithms do not leverage these properties. Thus a new paradigm has been devised. While event cameras are very efficient in representing sparse sequences of events with high temporal precision, many approaches are challenged in applications where a large amount of spatially-temporally rich information must be processed in real-time. In reality, most tasks in everyday life take place in complex and uncontrollable environments, which require sophisticated models and intelligent reasoning. Typical hard problems in real-world scenes are detecting various non-uniform objects or navigation in an unknown and complex environment. In addition, colour perception is an essential fundamental property in distinguishing objects in natural scenes. Colour is a new aspect of event-based sensors, which work fundamentally differently from standard cameras, measuring per-pixel brightness changes per colour filter asynchronously rather than measuring “absolute” brightness at a constant rate. This thesis explores neuromorphic event-based processing methods for high-noise and cluttered environments with imbalanced classes. A fully event-driven processing pipeline was developed for agricultural applications to perform fruits detection and classification to unlock the outstanding properties of event cameras. The nature of features in such data was explored, and methods to represent and detect features were demonstrated. A framework for detecting and classifying features was developed and evaluated on the N-MNIST and Dynamic Vision Sensor (DVS) gesture datasets. The same network was evaluated on laboratory recorded and real-world data with various internal variations for fruits detection such as overlap, variation in size and appearance. In addition, a method to handle highly imbalanced data was developed. We examined the characteristics of spatio-temporal patterns for each colour filter to help expand our understanding of this novel data and explored their applications in classification tasks where colours were more relevant features than shapes and appearances. The results presented in this thesis demonstrate the potential and efficacy of event- based systems by demonstrating the applicability of colour event data and the viability of event-driven classification