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
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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 +6 more
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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
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Architecture and hyperparameter optimization of YOLOv5 and YOLOv8 for weed detection in garlic fields [PDF]
Scientific ReportsWeed adaptability to environmental conditions poses a major challenge in agricultural production, often leading to yield reduction or even complete crop loss.
Siavash Shamohammadi +2 more
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Accurate weed detection in UAV grassland images via intelligent annotation and multi-dimensional network enhancement [PDF]
Frontiers in Plant ScienceIntroductionAccurate weed detection from unmanned aerial vehicle (UAV) images remains difficult because weed targets often blend with surrounding vegetation, vary substantially in scale, and exhibit irregular boundaries under cluttered grassland ...
Qiang Wang +3 more
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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
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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
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Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n
AgricultureWeeds 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 +4 more
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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
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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
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