BDHusk: A Comprehensive Dataset of Different Husk Species Images as a Component of Cattle Feed from Different Regions of Bangladesh.

Published: 16 October 2023| Version 1 | DOI: 10.17632/h754ntdtfx.1


The newly established dataset "Husk" encompasses comprehensive information on various species of husk commonly encountered in various parts of Bangladesh. This dataset comprises a representation of eight distinct species of husk. Such as Oryza sativa, Zea mays, Triticum aestivum, Cicer arietinum, Lens culinaris, Glycine max, Lathyrus sativus, and Pisum sativum var. arvense L. Poiret. The dataset contains a total of 2,400 original images, along with an additional 9280 augmented images. We took great care to meticulously capture each original image, ensuring that we utilised natural lighting conditions and a suitable background. We diligently collected these images over a span of two months, specifically from July 2023 to August 2023, capturing them from various distinct locations within the Sirajganj district. The extensive dataset possesses great potential for researchers to leverage a variety of machine learning and deep learning methods to make significant advancements in the agricultural sector. This resource is of great worth for subsequent inquiries as it lays the groundwork for pivotal advancements in these fields.


Steps to reproduce

The dataset was acquired through several systematic steps. First and foremost, we explored the study of the husk and its various species that are found in our local regions. Then, we went to some cattle feed stores in Sirajganj district to collect some husks. Subsequently, we diligently collected raw images of these species from various locations. The raw images were collected using the cameras of the Redmi Note 8 and the Samsung M21. The dataset consists of a total of 11,680 images, comprising 2,400 original images and 9,280 augmented images. The extensive dataset will greatly assist researchers in contributing to image classification and various machine learning and deep learning techniques.


Khwaja Yunus Ali University


Artificial Intelligence, Computer Vision, Agricultural Engineering, Animal Feed, Animal Feeding, Beef Cattle Diagnostics, Dairy Cattle Nutrition, Machine Learning, Image Classification, Agricultural Development, Food Quality Assessment, Characterization of Food, Agriculture Industry, Deep Learning, Nutrition Security