arXiv · · Significant research
This paper introduces a convolutional transformer model for classifying tomato maturity, along with a new UAE-sourced dataset, KUTomaData, for training segmentation and classification models. The model combines CNNs and transformers and was tested against two public datasets. Results showed state-of-the-art performance, outperforming existing methods by significant margins in mAP scores across all three datasets.
tomato maturity · convolutional transformer · KUTomaData · classification · CNN
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arXiv ·
Researchers introduce TomFormer, a transformer-based model for accurate and early detection of tomato leaf diseases, with the goal of deployment on the Hello Stretch robot for real-time diagnosis. TomFormer combines a visual transformer and CNN, achieving state-of-the-art results on KUTomaDATA, PlantDoc, and PlantVillage datasets. KUTomaDATA was collected from a greenhouse in Abu Dhabi, UAE.
arXiv ·
The researchers introduce KAU-CSSL, the first continuous Saudi Sign Language (SSL) dataset focusing on complete sentences. They propose a transformer-based model using ResNet-18 for spatial feature extraction and a Transformer Encoder with Bidirectional LSTM for temporal dependencies. The model achieved 99.02% accuracy in signer-dependent mode and 77.71% in signer-independent mode, advancing communication tools for the SSL community.
arXiv ·
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.