4.7 Article

Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images

Related references

Note: Only part of the references are listed.
Article Computer Science, Theory & Methods

Transformers in Vision: A Survey

Salman Khan et al.

Summary: Transformer models have shown impressive results in computer vision tasks by simulating long dependencies, supporting parallel processing, and handling multi-modal data. They are widely used in visual recognition, generative modeling, multi-modal tasks, video processing, low-level vision, and three-dimensional analysis, showcasing their strengths in scalability and flexibility.

ACM COMPUTING SURVEYS (2022)

Article Agriculture, Multidisciplinary

Semantic segmentation model of cotton roots in-situ image based on attention mechanism

Jia Kang et al.

Summary: The growth and distribution of root system in soil plays a crucial role in plant growth and crop production. This study focused on the segmentation of cotton mature root system using a semantic segmentation model with attention mechanism, which showed higher accuracy and efficiency compared to other models. The proposed model accurately distinguishes cotton roots from complex soil background, providing important theoretical value and practical application reference for deep learning in plant root segmentation.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Review Agriculture, Multidisciplinary

A survey of deep learning techniques for weed detection from images

A. S. M. Mahmudul Hasan et al.

Summary: The rapid development of deep learning techniques has enabled efficient detection and classification of objects from images or videos, with applications in agriculture especially for weed management. Automated weed detection plays a key role in improving crop yields and fine-tuning pre-trained models on plant datasets has proven to achieve high accuracy.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Article Agriculture, Multidisciplinary

Computer vision-based citrus tree detection in a cultivated environment using UAV imagery

Cenk Donmez et al.

Summary: The study utilized high-resolution UAV images and CCL algorithm to successfully detect and count citrus trees in orchards, achieving an accuracy and precision higher than 95% in heterogeneous agricultural patches with various tree sizes and types.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Review Computer Science, Artificial Intelligence

A review on the attention mechanism of deep learning

Zhaoyang Niu et al.

Summary: This paper provides an overview of state-of-the-art attention models and defines a unified model suitable for most attention structures. It describes in detail each step of the attention mechanism implemented in the model and classifies existing attention models based on four criteria. Additionally, it summarizes the use of attention mechanisms in network architectures and typical applications.

NEUROCOMPUTING (2021)

Review Chemistry, Analytical

Review of Weed Detection Methods Based on Computer Vision

Zhangnan Wu et al.

Summary: This review elaborates on the solutions to weed detection problems using traditional image-processing methods and deep learning-based methods, providing an overview of various methods, advantages and disadvantages, as well as introducing related datasets and machinery. It also analyzes the problems and difficulties of existing weed detection methods and prospects the future research trends.

SENSORS (2021)

Article Remote Sensing

Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery

Huasheng Huang et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2020)

Article Robotics

Robotic weed control using automated weed and crop classification

Xiaolong Wu et al.

JOURNAL OF FIELD ROBOTICS (2020)

Article Computer Science, Hardware & Architecture

A compilation of UAV applications for precision agriculture

Panagiotis Radoglou-Grammatikis et al.

COMPUTER NETWORKS (2020)

Article Agriculture, Multidisciplinary

Detection of Colchicum autumnale in drone images, using a machine-learning approach

Lukas Petrich et al.

PRECISION AGRICULTURE (2020)

Article Agriculture, Multidisciplinary

Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach

Mohamed Kerkech et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Article Environmental Sciences

Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery

Arun Narenthiran Veeranampalayam Sivakumar et al.

REMOTE SENSING (2020)

Proceedings Paper Computer Science, Artificial Intelligence

An Introductory Survey on Attention Mechanisms in NLP Problems

Dichao Hu

INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 2 (2020)

Article Agricultural Engineering

A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images

Kavir Osorio et al.

AGRIENGINEERING (2020)

Article Computer Science, Information Systems

CRowNet: Deep Network for Crop Row Detection in UAV Images

Mamadou Dian Bah et al.

IEEE ACCESS (2020)

Review Agriculture, Multidisciplinary

A review on weed detection using ground-based machine vision and image processing techniques

Aichen Wang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Article Agronomy

Investigation of alternate herbicides for effective weed management in glyphosate-tolerant cotton

Nadeem Iqbal et al.

ARCHIVES OF AGRONOMY AND SOIL SCIENCE (2019)

Article Agricultural Engineering

Transfer learning for the classification of sugar beet and volunteer potato under field conditions

Hyun K. Suh et al.

BIOSYSTEMS ENGINEERING (2018)

Review Agriculture, Multidisciplinary

Deep learning in agriculture: A survey

Andreas Kamilaris et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Multidisciplinary Sciences

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Huasheng Huang et al.

PLOS ONE (2018)

Article Robotics

weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

Inkyu Sa et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2018)

Article Agriculture, Multidisciplinary

Weed detection in soybean crops using ConvNets

Alessandro dos Santos Ferreira et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Review Agriculture, Multidisciplinary

A survey of image processing techniques for plant extraction and segmentation in the field

Esmael Hamuda et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2016)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Computer Science, Information Systems

A systematic analysis of performance measures for classification tasks

Marina Sokolova et al.

INFORMATION PROCESSING & MANAGEMENT (2009)

Article Biochemical Research Methods

Prediction error estimation: a comparison of resampling methods

AM Molinaro et al.

BIOINFORMATICS (2005)