Results 21 to 30 of about 79,354 (341)

Deep learning–based approaches for weed detection in crops [PDF]

Frontiers in Plant Science
Deep learning has become a transformative technology for modern weed detection, offering significant advantages over traditional machine vision in robustness, scalability, and recognition accuracy. This review provides a comprehensive synthesis of recent
Hua Zhao, Yan Wang
doaj   +2 more sources

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   +2 more sources

Evaluation of Inference Performance of Deep Learning Models for Real-Time Weed Detection in an Embedded Computer [PDF]

Sensors
The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time.
Canicius Mwitta, Glen C. Rains, Eric Prostko   +2 more
doaj   +2 more sources

Dataset for weed detection in fruit orchards. [PDF]

Data Brief
Salcedo-Navarro A, Montalban-Faet G, Garcia-Pineda M, Segura-Garcia J.   +3 more
europepmc   +2 more sources

Enhancing rangeland weed detection through convolutional neural networks and transfer learning

Crop Design
The detection of weed species in rangeland environments is a challenging task due to various factors such as dense, variable species vegetation, ocular occlusion, and a wide variety of plant morphology.
Christian Shackleton, Raja Hashim Ali, Talha Ali Khan   +2 more
doaj   +2 more sources

Automated weed monitoring and control: enhancing detection accuracy using a YOLOv7-AlexNet fusion network [PDF]

Frontiers in Plant Science
The agricultural sector is crucial to global sustainability, but it still faces challenges, particularly from weed invasions that severely compromise crop yields.
Muhammad Faizan Zeb, Abid Iqbal, Ghassan Husnain, Wisal Zafar, Ahmad Junaid, Ali Saeed Alzahrani, Syed Hashim Raza Bukhari, Ramasamy Srinivasaga Naidu   +7 more
doaj   +2 more sources

CoFly-WeedDB: A UAV image dataset for weed detection and species identification. [PDF]

Data Brief, 2022
Krestenitis M, Raptis EK, Kapoutsis AC, Ioannidis K, Kosmatopoulos EB, Vrochidis S, Kompatsiaris I.   +6 more
europepmc   +2 more sources

Weed detection by aerial imagery : toward weed management by UAV

, 2016
The agricultural framework aims to reduce pesticide use on fields. Weed management, which is highly herbicide consuming, became a great issue. In order to develop a weed management service using UAV, this PhD dissertation studies how to adapt the acquisition system (UAV + multispectral camera) developed by AIRINOV to detect weeds in row crops.
Marine Louargant
openalex   +3 more sources

Performance evaluation of deep learning object detectors for weed detection for cotton

Smart Agricultural Technology, 2023
Alternative non-chemical or chemical-reduced weed control tactics are critical for future integrated weed management, especially for herbicide-resistant weeds. Through weed detection and localization, machine vision technology has the potential to enable
Abdur Rahman, Yuzhen Lu, Haifeng Wang
doaj   +1 more source

TIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the field

Frontiers in Plant Science, 2022
IntroductionDevelopment of weed and crop detection algorithms provides theoretical support for weed control and becomes an effective tool for the site-specific weed management. For weed and crop object detection tasks in the field, there is often a large
Aichen Wang, Tao Peng, Huadong Cao, Yifei Xu, Yifei Xu, Xinhua Wei, Bingbo Cui   +6 more
doaj   +1 more source