Results 301 to 310 of about 79,354 (341)

Remote Tiny Weeds Detection

2023
Weeds cost Australian farmers around $1.5 billion a year in weed control activities and a further $2.5 billion a year in lost agricultural production. Weed management requires a good understanding of weed inventories and distribution for effective management.
Zheng, Lihong, Rahaman, D. M.Motiur, Hamilton, Mark, Dehaan, Remy, Gonzalez, Felipe, Kelly, Jane, Cherry, Hillary   +6 more
openaire   +2 more sources

Sensing for Weed Detection

2021
Conventional weed control methods are based on uniform treatments of the whole field, however, weeds are not distributed uniformly within fields, which means that the uniform distribution of herbicides is inappropriate. Considerable research has been conducted on different aspects of site-specific weed management in the past three decades from ...
S. Christensen, M. Dyrmann, M. S. Laursen, R. N. Jørgensen, J. Rasmussen   +4 more
openaire   +3 more sources

Weed Detection Using Canopy Reflection

Precision Agriculture, 2002
For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them.
E. Vrindts, J. De Baerdemaeker, H. Ramon
openaire   +1 more source

WEED DETECTION USING HYPERSPECTRAL IMAGING

Automation Technology for Off-Road Equipment Proceedings of the 2004 Conference, 2013
The goal of this study is to develop the discrimination method between crop and weed which require in the automatic mechanical weeding. In this study, the hyperspectral images were used. As data for analysis and verification, the hyperspectral images were acquired in the field of the university farm.
null H. Okamoto, null T. Murata, null T. Kataoka, null S. Hata   +3 more
openaire   +1 more source

Detection and Identification of Weeds

2010
This chapter reviews the approaches for the automation of weed detection. Site-specific plant protection needs to address the varying weed infestation, but the automation is only partially solved and research is still ongoing. The properties for plant species distinction as well as approaches that use them are presented.
Martin Weis, Markus Sökefeld
openaire   +1 more source

WEED DETECTION USING COLOR MACHINE VISION

Transactions of the ASAE, 2000
Many weed species have reddish stems, but stems of wheat and soybean are green. These color features were used in this study to establish a simple weed-detection method using a color machine-vision system. This method is more practical than texture- or shape-based methods because of its low sensitivity to canopy overlap, leaf orientation, camera ...
null M. S. El-Faki, null N. Zhang, null D. E. Peterson   +2 more
openaire   +1 more source

Advanced detection technologies for weed scouting

2022
This chapter discusses advanced detection technologies for weed scouting. It starts by highlighting the current techniques that can be used to make optimising herbicide treatments more efficient, such as on-ground and remotely-sensed weed detection. The chapter then goes on to discuss the replacement of herbicide treatments for other control tactics. A
C. Fernandez-Quintanilla, J. Dorado, J. M. Peña, D. Andújar   +3 more
openaire   +1 more source

Effective Spatial Resolution for Weed Detection

2007 Minneapolis, Minnesota, June 17-20, 2007, 2007
Patch spraying herbicide to control weeds has distinct advantages. Machine vision using digital images can be used for patch spraying however, research has found varied results. Image processing requires considerable computational time when image resolutions are high and poses difficulties for real time application.
null Sunil K Mathanker, null Paul R Weckler, null Randal K Taylor   +2 more
openaire   +1 more source

Semantic segmentation for weed detection in corn

Pest Management Science
AbstractBACKGROUNDReliable, fast, and accurate weed detection in farmland is crucial for precision weed management but remains challenging due to the diverse weed species present across different fields. While deep learning models for direct weed detection have been developed in previous studies, creating a training dataset that encompasses all ...
Teng Liu, Xiaojun Jin, Kang Han, Feiyu He, Jinxu Wang, Xin Chen, Xiaotong Kong, Jialin Yu   +7 more
openaire   +2 more sources

Water Weed Hyacinth Detection

2023 International Conference on Integration of Computational Intelligent System (ICICIS), 2023
Archana Chaudhari, Shaunak Joshi, Shekhar Shegokar, Krushna Rudrawar   +3 more
openaire   +1 more source