4.1 Article

Maize Tassel Detection From UAV Imagery Using Deep Learning

Related references

Note: Only part of the references are listed.
Article Biochemical Research Methods

Unsupervised Bayesian learning for rice panicle segmentation with UAV images

Md Abul Hayat et al.

PLANT METHODS (2020)

Article Biochemical Research Methods

Active learning with point supervision for cost-effective panicle detection in cereal crops

Akshay L. Chandra et al.

PLANT METHODS (2020)

Article Environmental Sciences

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Yunling Liu et al.

REMOTE SENSING (2020)

Article Computer Science, Artificial Intelligence

The Open Images Dataset V4 Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale

Alina Kuznetsova et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)

Article Biochemical Research Methods

Maize tassels detection: a benchmark of the state of the art

Hongwei Zou et al.

PLANT METHODS (2020)

Review Agriculture, Multidisciplinary

Deep learning in agriculture: A survey

Andreas Kamilaris et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Plant Sciences

Aerial Images and Convolutional Neural Network for Cotton Bloom Detection

Rui Xu et al.

FRONTIERS IN PLANT SCIENCE (2018)

Article Computer Science, Information Systems

Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks

Dunja Bozic-Stulic et al.

JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Article Biochemical Research Methods

TasselNet: counting maize tassels in the wild via local counts regression network

Hao Lu et al.

PLANT METHODS (2017)

Article Chemistry, Analytical

Deep Count: Fruit Counting Based on Deep Simulated Learning

Maryam Rahnemoonfar et al.

SENSORS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Predicting Ground-Level Scene Layout from Aerial Imagery

Menghua Zhai et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Agriculture, Multidisciplinary

Reliability of different color spaces to estimate nitrogen SPAD values in maize

Jose F. Reyes et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Article Imaging Science & Photographic Technology

Object Recognition in Aerial Images Using Convolutional Neural Networks

Matija Radovic et al.

JOURNAL OF IMAGING (2017)

Article Agriculture, Multidisciplinary

Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities

Chuanqi Xie et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Article Computer Science, Artificial Intelligence

Region-Based Convolutional Networks for Accurate Object Detection and Segmentation

Ross Girshick et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)

Article Chemistry, Analytical

DeepFruits: A Fruit Detection System Using Deep Neural Networks

Inkyu Sa et al.

SENSORS (2016)

Article Plant Sciences

Using Deep Learning for Image-Based Plant Disease Detection

Sharada P. Mohanty et al.

FRONTIERS IN PLANT SCIENCE (2016)

Article Multidisciplinary Sciences

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Yeyin Shi et al.

PLOS ONE (2016)

Article Agriculture, Multidisciplinary

Fine-grained maize tassel trait characterization with multi-view representations

Hao Lu et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2015)

Review Agronomy

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Sindhuja Sankaran et al.

EUROPEAN JOURNAL OF AGRONOMY (2015)

Article Computer Science, Artificial Intelligence

Detecting corn tassels using computer vision and support vector machines

Ferhat Kurtulmus et al.

EXPERT SYSTEMS WITH APPLICATIONS (2014)

Article Agriculture, Multidisciplinary

Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features

Kuo-Yi Huang

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2007)