Agriculture plays a key role in the food security and economic development of developing countries, including Ethiopia. However, accurately identifying the geographical origin of agricultural products, such as Teff, can be challenging due to similarities in size, color, and shape. While previous studies have explored classification methods, the physical properties of Teff pose segmentation, noise, and classification challenges for traditional techniques. This study uses a multi-channel capsule network and computer vision to identify the geographical origin of Ethiopian Teff, including red and mixed varieties from six regions. The approach addresses issues such as viewpoint variance, overfitting, and spatial invariance faced by previous convolutional neural network methods. Teff images were collected from various regions in the Amhara region of Ethiopia, including East Gojjam, West Gojjam, North Gonder, and North Shoa, with a specific focus on locations such as Adet, Bichena, Dejen, Denbya, Debre Markos, and Minjar. A total of 2700 Teff images were acquired using a smartphone camera. To enhance the image quality, we used various techniques, including denoising filters such as median, Gaussian, and non-local means. We also applied Content-aware resizing to adjust the image dimensions accordingly. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to enhance the images further. The next step involved image segmentation, where we used a combination of multi-Otsu thresholding, region-based segmentation, watershed techniques, and the U-net method, which is widely used in deep learning applications. Subsequently, we extracted features from the segmented images using the BRISK and HOG methods. Once the feature vectors were obtained, we developed a model using Capsule Networks to determining the geographical origin of Ethiopian Teff. To train and evaluate the model, we divided the dataset into training and testing sets, allocating 90% of the data for training and 10% for testing the model's performance. By conducting 11 experiments, we achieved notable results in three different approaches. The first approach, using the BRISK feature extraction method, combined Multi-Otsu thresholding, region-based segmentation, watershed segmentation, and comprehensive noise removal, leading to the highest accuracy of 84%. In the second approach, combining region-based segmentation with median and Gaussian filtering, along with BRISK feature extraction, resulted in an accuracy of 80%. The third approach, using Histogram of Oriented Gradients (HOG) feature extraction, combined Multi-Otsu thresholding, region-based segmentation, watershed segmentation, and comprehensive noise removal, achieving an impressive accuracy of 92.5%. These findings demonstrate the successful development of a highly accurate model for determining the geographical origin of Ethiopian Teff, with a test accuracy rate of 92.5%. Keywords: Multi-Channel, Capsule Network, Computer vision, Geographical Origin, Teff, Deep Learning, Multi-Otsu, HOG, BRISK