CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture

05/17/2023
by   Talha Ilyas, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 4

page 7

page 8

page 9

page 12

research
06/04/2023

Deep learning powered real-time identification of insects using citizen science data

Insect-pests significantly impact global agricultural productivity and q...
research
09/11/2019

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Being heavily reliant on animals, it is our ethical obligation to improv...
research
03/15/2021

I-Nema: A Biological Image Dataset for Nematode Recognition

Nematode worms are one of most abundant metazoan groups on the earth, oc...
research
08/04/2022

Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models

In recent years, deep learning models have become the standard for agric...
research
10/09/2018

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning

Robotic weed control has seen increased research in the past decade with...
research
09/18/2023

ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition

Developing reliable computational frameworks for early parasite detectio...

Please sign up or login with your details

Forgot password? Click here to reset