Results 31 to 40 of about 11,448 (161)

Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n

Agriculture
Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control.
Yan Li, Zhonghui Guo, Yan Sun, Xiaoan Chen, Yingli Cao   +4 more
doaj   +1 more source

Weed detection in soybean crops using custom lightweight deep learning models

Journal of Agriculture and Food Research, 2022
Weed detection has become an integral part of precision farming that leverages the IoT framework. Weeds have become responsible for 45% of the agriculture industry's crop losses due mainly to the competition with crops. An efficient weed detection method
Najmeh Razfar, Julian True, Rodina Bassiouny, Vishaal Venkatesh, Rasha Kashef   +4 more
doaj   +1 more source

Image Prompt Adapter-Based Stable Diffusion for Enhanced Multi-Class Weed Generation and Detection

AgriEngineering
The curation of large-scale, diverse datasets for robust weed detection is extremely time-consuming and resource-intensive in practice. Generative artificial intelligence (AI) opens up opportunities for image generation to supplement real-world image ...
Boyang Deng, Yuzhen Lu
doaj   +1 more source

Weed Detection Using Image Processing

International Journal for Research in Applied Science and Engineering Technology, 2023
Abstract: This project focuses on the development of an image processing based system for the detection of weeds in agricultural fields. The proposed system uses computer vision techniques to extract relevant features from the images of the field and classify the presence of weeds in the field.
openaire   +1 more source

Weed detection by using image processing

Indonesian Journal of Electrical Engineering and Computer Science, 2023
In agricultural regions, the procedure of weed removal is crucial. Weed removal in the classic way, takes longer and requires greater physical effort. The idea is to eliminate weeds from agricultural fields automatically. The proposed study uses a deep learning algorithm to detect weeds growing between crops.
Vijaykumar Bidve, Sulakshana Mane, Pradip Tamkhade, Ganesh Pakle   +3 more
openaire   +1 more source

Static laser weeding system based on improved YOLOv8 and image fusion

Journal of Agricultural Engineering
Laser weeding is one of the promising weed control methods for weed management in organic agriculture. However, the complex field environments lead to low weed detection accuracy, which makes it difficult to meet the requirements of high-precision laser
Xiwang Du, Xia Li, Fangtao Duan, Jiawei Hua, Mengchao Hu   +4 more
doaj   +1 more source

Temperature and Farm Labor in Nigeria

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT We estimate the impact of temperature shocks on the composition of farm labor in rural Nigeria using a nationally representative household panel survey. Leveraging plausibly exogenous year‐to‐year variation in growing season temperatures, we find that warmer temperatures significantly alter farm labor composition, prompting a substantial shift
Andu Berha
wiley   +1 more source

Weed25: A deep learning dataset for weed identification

Frontiers in Plant Science, 2022
Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as ...
Pei Wang, Pei Wang, Pei Wang, Yin Tang, Fan Luo, Lihong Wang, Chengsong Li, Qi Niu, Hui Li, Hui Li   +9 more
doaj   +1 more source

Broad-Leaf Weed Detection in Pasture [PDF]

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018
Weed control in pasture is a challenging problem that can be expensive and environmentally unfriendly. This paper proposes a novel method for recognition of broad-leaf weeds in pasture such that precision weed control can be achieved with reduced herbicide use.
Wenhao Zhang, Mark F. Hansen, Timothy N. Volonakis, Melvyn Smith, Lyndon Smith, Jim Wilson, Graham Ralston, Laurence Broadbent, Glynn Wright   +8 more
openaire   +1 more source

STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field

Agriculture
Rapeseed is one of the primary oil crops; yet, it faces significant threats from weeds. The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds.
Tao Tao, Xinhua Wei
doaj   +1 more source