Weed Plant Detection from Agricultural Field Images using YOLOv3 Algorithm

Dandekar, Yukta; Shinde, Kshitija; Gangan, Jai; Firdausi, Sabil; Bharne, Smita

doi:10.1109/iccubea54992.2022.10011010

Cited by 9 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, in [3], the author discussed an on-site weed management computer vision system that utilizes a public weed database sourced from Kaggle. However, this dataset contains only 1300 images in total, further compounded by the fact that it is an augmented dataset.…”

Section: Introductionmentioning

confidence: 99%

Weed Detection and Classification with Computer Vision Using a Limited Image Dataset

Moldvai,

Mesterházi,

Teschner

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.

show abstract

Section: Introductionmentioning

confidence: 99%

Weed Detection and Classification with Computer Vision Using a Limited Image Dataset

Moldvai,

Mesterházi,

Teschner

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

An Analytical Approach and Concept Mapping of Agricultural Issues Using Deep Learning Techniques

Gattani,

Pandey,

Dalvi

et al. 2024

Studies in Computational Intelligence

View full text Add to dashboard Cite

Weed detection in agricultural fields using machine vision

Moldvai,

Ambrus,

Teschner

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

Weeds have the potential to cause significant damage to agricultural fields, so the development of weed detection and automatic weed control in these areas is very important. Weed detection based on RGB images allows more efficient management of crop fields, reducing production costs and increasing yields. Conventional weed control methods can often be time-consuming and costly. It can also cause environmental damage through overuse of chemicals. Automated weed detection and control technologies enable precision agriculture, where weeds are accurately identified and targeted, minimizing chemical use and environmental impact. Overall, weed detection and automated weed control represent a significant step forward in agriculture, helping farmers to reduce production costs, increase crop safety, and develop more sustainable agricultural practices. Thanks to technological advances, we can expect more efficient and environmentally friendly solutions for weed control in the future. Developing weed detection and automated control technologies is crucial for enhancing agricultural efficiency. Employing RGB images for weed identification not only lowers production costs but also mitigates environmental damage caused by excessive chemical use. This study explores automated weed detection systems, emphasizing their role in precision agriculture, which ensures minimal chemical use while maximizing crop safety and sustainability.

show abstract

Weed Plant Detection from Agricultural Field Images using YOLOv3 Algorithm

Cited by 9 publications

References 8 publications

Weed Detection and Classification with Computer Vision Using a Limited Image Dataset

Weed Detection and Classification with Computer Vision Using a Limited Image Dataset

An Analytical Approach and Concept Mapping of Agricultural Issues Using Deep Learning Techniques

Weed detection in agricultural fields using machine vision

Contact Info

Product

Resources

About