Abstract: The robotic system for harvesting, and performing pre- and post-harvesting tasks of date palm trees includes three main parts: a truck platform, a scissor lift, and a multi-task system. The truck platform provides mobility for the whole system, while the scissor lift raises the multi-task system to the top of the palm tree. The multi-task system is fixed on top of the scissor lift and includes a large basin that catches fruits during harvesting. A circular track is fixed within the basin and includes a 2-degree-of-freedom (DOF) slider base mounted thereon. On top of the 2-DOF slider base is a robotic arm fitted with the proper tool (harvesting tool, maintenance tool or bagging system) for the task to be performed. The circular track has pivoting sections, and the basin has a flexible portion in the rear of the truck platform to allow the system to surround the palm tree.

Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.

Abstract: Presented herein are systems and methods of classifying biomedical images. A computing system may identify a first plurality of tiles from a first biomedical image of a first sample. The computing system may determine a first category for the first sample by applying the plurality of tiles to a classification model. The classification model may include a tile encoder to determine, based on the first plurality of tiles, a corresponding plurality of feature vectors in a feature space. The classification model may include a clusterer to select a subset of feature vectors from the plurality of feature vectors based on a plurality of centroids defined in the feature space. The classification model may include an aggregator to generate, based on the subset of feature vectors, the first category for the sample. The computing system may store an association between the first category and the first biomedical image.

Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.

Abstract: Presented herein are systems and methods for determining scores from biomedical images. A computing system may identify a plurality of tiles in a first biomedical image derived from a sample of a subject. Each tile may correspond to features of the sample. The computing system may apply the plurality of tiles to a machine learning (ML) model. The ML model may include: an encoder to generate a plurality of feature vectors based on the plurality of tiles; a clusterer to select a subset from the plurality of feature vectors; and an aggregator to determine a first score indicative of a time to an event for the subject resulting from the features of the sample. The model may be trained in accordance with a loss derived from second scores determined for second biomedical images. The computing system may store an association between the score and the first biomedical image.

Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.