ABSTRACT
The "WaterHyacinth" dataset, a recently gathered collection of images featuring four distinct species of Water hyacinth from different regions of Bangladesh, is presented in this article. There are four different classifications: Lemna minor, Eichhornia crassipes, Monochoria korsakowii, and Pistia stratiotes. The collection consists of 1790 original images and in addition 4050 augmented photos of Water hyacinth species. Every original picture was captured with the appropriate background and in sufficient natural light. Every image was correctly placed in its corresponding subfolder, providing optimal use of the pictures by various machine learning and deep learning models. Researchers could make major progress in agriculture, environmental monitoring, aquatic science, and remote sensing domains by utilizing this enormous dataset and various machine learning and deep learning approaches. In addition to opening opportunities for significant developments in these domains, it offers an essential asset for further study.
ABSTRACT
This study presents a recently compiled dataset called "BDHusk," which encompasses a wide range of husk images representing eight different husk species as a component of cattle feed sourced from different locales in Sirajganj, Bangladesh. The following are eight husk species: Oryza sativa, Zea mays, Triticum aestivum, Cicer arietinum, Lens culinaris, Glycine max, Lathyrus sativus, and Pisum sativum var. arvense L. Poiret. This dataset consists of a total of 2,400 original images and an additional 9,280 augmented images, all showcasing various husk species. Every single one of the original images was taken with the right backdrop and in enough amount of natural light. Every image was appropriately positioned into its respective subfolder, enabling a wide variety of machine learning and deep learning models to make the most effective use of the images. By utilizing this extensive dataset and employing various machine learning and deep learning techniques, researchers have the potential to achieve significant advancements in the fields of agriculture, food and nutrition science, environmental monitoring, and computer sciences. This dataset allows researchers to improve cattle feeding using data-driven methods. Researchers can improve cattle health and production by improving feed compositions. Furthermore, it not only presents potential for substantial advancements in these fields but also serves as a crucial resource for future research endeavors.
