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 termed 'SorghumWeedDataset_Segmentation' is a crop-weed segmentation dataset that contains 5555 manually pixel-wise annotated data segments from 252 data samples for addressing crop-weed localization, detection, and segmentation problems. All data samples are acquired in April and May 2023 from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. Manually annotated data samples and data segments are verified by agronomists. The datasets are made publicly available to the research community to solve the crop-weed problems using state-of-the-art image processing, machine learning, and deep learning algorithms. To the best of our knowledge, these are the first open-access crop-weed research datasets from Indian fields for classification and segmentation to deal with weed issues in uniform and random crop-spacing fields. However, other available datasets (from Indian fields) are either non-research datasets or available on subscription/request.

Keywords: Autonomous weeding; Crop-weed dataset; Crop-weed identification; Machine learning and deep learning; Precision agriculture; Research_data; Sorghum_dataset; Uniform and random crop-spacing.

Sorghum-Weed Classification and Segmentation datasets.

Fig. 1

Directory structure of ‘SorghumWeedDataset_Classification’.

Fig. 2

Directory structure of ‘SorghumWeedDataset_Segmentation’.

Fig. 3

(a), (b), and (c) shows the various stages of Sorghum samplings. Grasses are shown in (d), (e), and (f). Broad leaf weeds are shown in (g), (h), and (i).

Fig. 4

(a), (b), and, (b) present the data samples and (d), (e), and (f) show a representation of their respective annotations.

Fig. 5

Plots S1 to S6 - Diagrammatic representation and actual field.

Fig. 6

S4 plot layout - Diagrammatic representation and actual field.

Fig. 7

Various growth stages of the sorghum crop during data acquisition.

Fig. 8

(a) and (b) displays uniform spacing plot before and after weeding, (c) and (d) displays random spacing plot before and after weeding.

Fig. 9

Challenges faced during the data acquisition process.