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 +4 more sources
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
Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops [PDF]
Agronomy, 2022 As the tomato (Solanum lycopersicum L.) is one of the most important crops worldwide, and the conventional approach for weed control compromises its potential productivity. Thus, the automatic detection of the most aggressive weed species is necessary to
Juan Manuel López-Correa +3 more
doaj +2 more sources
Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4 [PDF]
Agriculture, 2022 Effective maize and weed detection plays an important role in farmland management, which helps to improve yield and save herbicide resources. Due to their convenience and high resolution, Unmanned Aerial Vehicles (UAVs) are widely used in weed detection.
Haotian Pei +5 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
Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf [PDF]
Frontiers in Plant Science, 2023 Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect
Xiaojun Jin +5 more
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
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 +4 more
doaj +2 more sources