Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

(2021) Yajun Chen et al.   SENSORS

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

(2021) Ruoling Deng et al.   SENSORS

Reconstruction of damaged herbarium leaves using deep learning techniques for improving classification accuracy

(2021) Burhan Rashid Hussein et al.   Ecological Informatics

Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

(2021) Ruifeng Bai et al.   SENSORS

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

(2020) Daliana Lobo Torres et al.   SENSORS

Schrödinger’s phenotypes: herbarium specimens show two‐dimensional images are both good and (not so) bad sources of morphological data

(2020) Leonardo M. Borges et al.   Methods in Ecology and Evolution

GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens

(2020) Tankred Ott et al.   Applications in Plant Sciences

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

(2020) William N. Weaver et al.   Applications in Plant Sciences

Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning

(2020) Alexander E. White et al.   Applications in Plant Sciences

Real-Time Detection for Wheat Head Applying Deep Neural Network

(2020) Bo Gong et al.   SENSORS

Detection and annotation of plant organs from digitised herbarium scans using deep learning

(2020) Sohaib Younis et al.   Biodiversity Data Journal

Survey on semantic segmentation using deep learning techniques

(2019) Fahad Lateef et al.   NEUROCOMPUTING

An Efficient Hardware-Oriented Single-Pass Approach for Connected Component Analysis

(2019) Fanny Spagnolo et al.   SENSORS

Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features

(2019) Xuedong Yao et al.   SENSORS

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

(2019) Carlos S. Pereira et al.   SENSORS

The unrealized potential of herbaria for global change biology

(2018) Emily K. Meineke et al.   ECOLOGICAL MONOGRAPHS

Automated plant species identification—Trends and future directions

(2018) Jana Wäldchen et al.   PLoS Computational Biology

Herbarium specimens can reveal impacts of climate change on plant phenology; a review of methods and applications

(2018) Casey A. Jones et al.   PeerJ

Using herbaria to study global environmental change

(2018) Patricia L. M. Lang et al.   NEW PHYTOLOGIST

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

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Old Plants, New Tricks: Phenological Research Using Herbarium Specimens

(2017) Charles G. Willis et al.   TRENDS IN ECOLOGY & EVOLUTION

Is Shape of a Fresh and Dried Leaf the Same?

(2016) Dominik Tomaszewski et al.   PLoS One

The Pascal Visual Object Classes Challenge: A Retrospective

(2014) Mark Everingham et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Herbaria are a major frontier for species discovery

(2010) D. P. Bebber et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA