SorghumWeedDataset_Classification and SorghumWeedDataset_Segmentation datasets for classification, detection, and segmentation in deep learning.

ABSTRACT: The intuition behind this data acquisition is to encourage research for addressing the problem of weeds in agriculture through computer vision applications. Data is acquired in the form of images from uniform and random crop-spacing fields. In other words, we have taken a step forward to identify weeds from fields that follow any method of sowing, which ultimately leads to the transformation of traditional agriculture into precision agriculture. Sorghum crop and its associated weeds are chosen as the research objects during this process. These acquired data are used in framing two datasets. The first dataset termed 'SorghumWeedDataset_Classification' is a crop-weed classification dataset created with 4312 data samples for addressing crop-weed classification problems. The second dataset ter

SUBMITTER: Justina MJ 

PROVIDER: S-EPMC10789999 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature