WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion [PDF]
Frontiers in Plant Science, 2023 Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under ...
Zhiqiang Guo +5 more
doaj +2 more sources
Evaluating Cross-Applicability of Weed Detection Models Across Different Crops in Similar Production Environments [PDF]
Frontiers in Plant Science, 2022 Convolutional neural networks (CNNs) have revolutionized the weed detection process with tremendous improvements in precision and accuracy. However, training these models is time-consuming and computationally demanding; thus, training weed detection ...
Bishwa B. Sapkota +3 more
doaj +2 more sources
OpenWeedLocator (OWL): an open-source, low-cost device for fallow weed detection [PDF]
Scientific Reports, 2022 The use of a fallow phase is an important tool for maximizing crop yield potential in moisture limited agricultural environments, with a focus on removing weeds to optimize fallow efficiency.
Guy Coleman +2 more
doaj +2 more sources
Deep learning–based approaches for weed detection in crops [PDF]
Frontiers in Plant ScienceDeep 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
WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification [PDF]
Scientific ReportsWeed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices.
Taminul Islam +4 more
doaj +2 more sources
PD-YOLO: a novel weed detection method based on multi-scale feature fusion [PDF]
Frontiers in Plant ScienceIntroductionThe deployment of robots for automated weeding holds significant promise in promoting sustainable agriculture and reducing labor requirements, with vision based detection being crucial for accurate weed identification. However, weed detection
Shengzhou Li +4 more
doaj +2 more sources
A lightweight weed detection model for cotton fields based on an improved YOLOv8n [PDF]
Scientific ReportsIn modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance.
Jun Wang +3 more
doaj +2 more sources
Evaluation of Inference Performance of Deep Learning Models for Real-Time Weed Detection in an Embedded Computer [PDF]
SensorsThe 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 +2 more
doaj +2 more sources
Automated weed monitoring and control: enhancing detection accuracy using a YOLOv7-AlexNet fusion network [PDF]
Frontiers in Plant ScienceThe agricultural sector is crucial to global sustainability, but it still faces challenges, particularly from weed invasions that severely compromise crop yields.
Muhammad Faizan Zeb +7 more
doaj +2 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