☆ 4.6 Article

Weed Detection in Potato Fields Based on Improved YOLOv4: Optimal Speed and Accuracy of Weed Detection in Potato Fields

ELECTRONICS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Agriculture, Multidisciplinary

A deep learning approach incorporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings

Qifan Wang et al.

Summary: A convolution neural network model, YOLO-CBAM, is constructed in this paper for the detection of Solanum rostratum Dunal seedlings. High-resolution images are sliced using the overlap rate to construct datasets, and multiscale training methods are used to improve model performance. Comparison tests show that the proposed model outperforms the baseline model YOLO v5 in terms of precision and recall. Field trials demonstrate that the deployed model achieves high precision and recall for real-time recognition.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

添加到收藏夹

Article Agronomy

SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Jian-Lin Zhang et al.

Summary: This study proposes a method combining squeeze-and-excitation network with YOLOv5x for weed-crop classification and lettuce localization in the field. The method exhibits the highest performance in identifying weed and lettuce plants and accurately detecting the location of lettuce stems based on plant morphological characteristics.

AGRONOMY-BASEL (2022)

添加到收藏夹

Article Agronomy

Weed Detection in Peanut Fields Based on Machine Vision

Hui Zhang et al.

Summary: This paper proposes a weed identification model named EM-YOLOv4-Tiny, which incorporates multiscale detection and attention mechanisms to accurately and quickly identify weeds in peanut fields. Experimental results show that the EM-YOLOv4-Tiny network outperforms other models in terms of detection accuracy and speed.

AGRICULTURE-BASEL (2022)

添加到收藏夹

Article Computer Science, Information Systems

An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Syed-Ali Hassan et al.

Summary: Recent advancements in machine learning have provided opportunities for research on autonomous devices. This study employs a deep learning approach to early detection of road issues and constructs a convolutional neural network model to improve accuracy. By using a revised dataset for road cracks, potholes, and the yellow lane, the study achieves automatic detection and UAV autonomous navigation.

ELECTRONICS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Testing the Suitability of Automated Machine Learning for Weeds Identification

Borja Espejo-Garcia et al.

Summary: The study evaluated a fully open-source Automated Machine Learning system on two different datasets, showing high accuracy in plant protection but with room for further improvement.

AI (2021)

添加到收藏夹

Article Chemistry, Analytical

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

Lydia Elstone et al.

SENSORS (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Subhankar Roy et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

添加到收藏夹

Article Environmental Sciences

Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks

Gerassimos G. Peteinatos et al.

REMOTE SENSING (2020)

添加到收藏夹

Article Agriculture, Multidisciplinary

Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence

Victor Partel et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

添加到收藏夹

Article Agriculture, Multidisciplinary

Evaluation of support vector machine and artificial neural networks in weed detection using shape features

Adel Bakhshipour et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

添加到收藏夹

Article Agronomy

Assessment of yield and economic losses in agriculture due to weeds in India

Yogita Gharde et al.

CROP PROTECTION (2018)

添加到收藏夹

Article Agriculture, Multidisciplinary

Robotic in-row weed control in vegetables

Trygve Utstumo et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

Weed recognition by SVM texture feature classification in outdoor vegetable crop images

Camilo Pulido et al.

INGENIERIA E INVESTIGACION (2017)

添加到收藏夹

Article Engineering, Multidisciplinary

Weed recognition by SVM texture feature classification in outdoor vegetable crop images

Camilo Pulido et al.

INGENIERIA E INVESTIGACION (2017)

添加到收藏夹

Article Agriculture, Multidisciplinary

Weed detection in soybean crops using ConvNets

Alessandro dos Santos Ferreira et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

添加到收藏夹

Article Agriculture, Multidisciplinary

Weed identification based on K-means feature learning combined with convolutional neural network

JingLei Tang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

添加到收藏夹

Review Agriculture, Multidisciplinary

Automatic GPS-based intra-row weed knife control system for transplanted row crops

M. Perez-Ruiz et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2012)

添加到收藏夹

Article Agriculture, Multidisciplinary

Performance evaluation of a crop/weed discriminating microsprayer

Henrik Skov Midtiby et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2011)

添加到收藏夹

Article Agricultural Engineering

Mechanical within-row weed control for transplanted crops using computer vision

N. D. Tillett et al.

BIOSYSTEMS ENGINEERING (2008)

添加到收藏夹

Article Agricultural Engineering

Application accuracy of a machine vision-controlled robotic micro-dosing system

H. T. Sogaard et al.

BIOSYSTEMS ENGINEERING (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.