Results 51 to 60 of about 11,448 (161)

HAD-YOLO: An Accurate and Effective Weed Detection Model Based on Improved YOLOV5 Network

Agronomy
Weeds significantly impact crop yields and quality, necessitating strict control. Effective weed identification is essential to precision weeding in the field.
Long Deng, Zhonghua Miao, Xueguan Zhao, Shuo Yang, Yuanyuan Gao, Changyuan Zhai, Chunjiang Zhao   +6 more
doaj   +1 more source

MKD8: An Enhanced YOLOv8 Model for High-Precision Weed Detection

Agriculture
Weeds are an inevitable element in agricultural production, and their significant negative impacts on crop growth make weed detection a crucial task in precision agriculture.
Wenxuan Su, Wenzhong Yang, Jiajia Wang, Doudou Ren, Danny Chen   +4 more
doaj   +1 more source

A Canopy Height Model Derived from Unmanned Aerial System Imagery Provides Late-Season Weed Detection and Explains Variation in Crop Yield

Agronomy
Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection.
Fred Teasley, Alex L. Woodley, Robert Austin   +2 more
doaj   +1 more source

PMDNet: An Improved Object Detection Model for Wheat Field Weed

Agronomy
Efficient and accurate weed detection in wheat fields is critical for precision agriculture to optimize crop yield and minimize herbicide usage. The dataset for weed detection in wheat fields was created, encompassing 5967 images across eight well ...
Zhengyuan Qi, Jun Wang
doaj   +1 more source

Real-Time Detection and Localization of Weeds in Dictamnus dasycarpus Fields for Laser-Based Weeding Control

Agronomy
Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in
Yanlei Xu, Zehao Liu, Jian Li, Dongyan Huang, Yibing Chen, Yang Zhou   +5 more
doaj   +1 more source

YOLOv7 for Weed Detection in Cotton Fields Using UAV Imagery

AgriEngineering
Weed detection is critical for precision agriculture, enabling targeted herbicide application to reduce costs and enhance crop health. This study utilized UAV-acquired RGB imagery from cotton fields to develop and evaluate deep learning models for weed ...
Anindita Das, Yong Yang, Vinitha Hannah Subburaj   +2 more
doaj   +1 more source

Deep Learning Techniques for Weed Detection in Agricultural Environments: A Comprehensive Review

IEEE Access
Agriculture has been completely transformed by Deep Learning (DL) techniques, which allow for quick object localization and detection. However, because weeds and crops are similar in color, form, and texture, weed detection and categorization can be ...
Deepthi G Pai, Radhika Kamath, Mamatha Balachandra   +2 more
doaj   +1 more source

A comprehensive survey on weed and crop classification using machine learning and deep learning

Artificial Intelligence in Agriculture
Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating the transition from conventional to precision
Faisal Dharma Adhinata, Wahyono, Raden Sumiharto   +2 more
doaj   +1 more source

Applicability of precision weed detection technologies

Acta Agraria Debreceniensis, 2013
In an agricultural field or horticultural plantation, weeds compete with cultivated plants for water and nutrients. The transpirated water by the weeds is needed to be replaced, which saddles surplus costs on the farmer, which could reduce the profitability of crop production.
Péter Riczu, János Tamás
openaire   +2 more sources

Weed Detection in Sesame Seed

International Journal for Research in Applied Science and Engineering Technology
Contemporary agricultural systems encounter substantial obstacles in controlling weed proliferation, which diminishes harvest yields and escalates operational expenditures. This study introduces WeedScan AI, a novel deep learning architecture that utilizes satellite imaging and advanced computer vision methodologies for autonomous weed identification ...
openaire   +1 more source