CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in
  Precision Agriculture
Talha Ilyas Dewa Made Sri Arsa Khubaib Ahmad Yong Chae Jeong Okjae Won Jong Hoon Lee Hyongsuk Kim
The growing demand for precision agriculture necessitates efficient and
accurate crop-weed recognition and classification systems. Current datasets
often lack the sample size, diversity, and hierarchical structure needed to
develop robust deep learning models for discriminating crops and weeds in
agricultural fields. Moreover, the similar external structure and phenomics of
crops and weeds complicate recognition tasks. To address these issues, we
present the CWD30 dataset, a large-scale, diverse, holistic, and hierarchical
dataset tailored for crop-weed recognition tasks in precision agriculture.
CWD30 comprises over 219,770 high-resolution images of 20 weed species and 10
crop species, encompassing various growth stages, multiple viewing angles, and
environmental conditions. The images were collected from diverse agricultural
fields across different geographic locations and seasons, ensuring a
representative dataset. The dataset's hierarchical taxonomy enables
fine-grained classification and facilitates the development of more accurate,
robust, and generalizable deep learning models. We conduct extensive baseline
experiments to validate the efficacy of the CWD30 dataset. Our experiments
reveal that the dataset poses significant challenges due to intra-class
variations, inter-class similarities, and data imbalance. Additionally, we
demonstrate that minor training modifications like using CWD30 pretrained
backbones can significantly enhance model performance and reduce convergence
time, saving training resources on several downstream tasks. These challenges
provide valuable insights and opportunities for future research in crop-weed
recognition. We believe that the CWD30 dataset will serve as a benchmark for
evaluating crop-weed recognition algorithms, promoting advancements in
precision agriculture, and fostering collaboration among researchers in the
field.