Journal ArticleDOI
LeafNet: A computer vision system for automatic plant species identification
Reads0
Chats0
TLDR
A deep learning system to learn discriminative features from leaf images along with a classifier for species identification of plants is developed and it is shown that learning the features by a convolutional neural network can provide better feature representation for leaf images compared to hand-crafted features.About:
This article is published in Ecological Informatics.The article was published on 2017-07-01. It has received 203 citations till now. The article focuses on the topics: Plant identification & Deep learning.read more
Citations
More filters
Journal ArticleDOI
A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
TL;DR: A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildews, and target leaf spots, and results showed that the DCNN was a robust tool for recognizing the cucumbers in field conditions.
Journal ArticleDOI
Applications for Deep Learning in Ecology
TL;DR: It is argued that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.
Journal ArticleDOI
Analysis of transfer learning for deep neural network based plant classification models
Aydin Kaya,Ali Seydi Keceli,Cagatay Catal,Hamdi Yalin Yalic,Huseyin Temucin,Bedir Tekinerdogan +5 more
TL;DR: This experimental study demonstrates that transfer learning can provide important benefits for automated plant identification and can improve low-performance plant classification models.
Journal ArticleDOI
Automated plant species identification-Trends and future directions.
TL;DR: The technical status quo on computer vision approaches for plant species identification is reviewed, the main research challenges to overcome in providing applicable tools are highlighted, and a discussion of open and future research thrusts is discussed.
Journal ArticleDOI
Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?
Robert Lücking,M. Catherine Aime,Barbara Robbertse,Andrew N. Miller,Hiran A. Ariyawansa,Takayuki Aoki,Gianluigi Cardinali,Pedro W. Crous,Pedro W. Crous,Irina S. Druzhinina,Irina S. Druzhinina,David M. Geiser,David L. Hawksworth,Kevin D. Hyde,Laszlo Irinyi,Rajesh Jeewon,Peter R. Johnston,Paul M. Kirk,Elaine Malosso,Tom W. May,Wieland Meyer,Maarja Öpik,Vincent Robert,Vincent Robert,Marc Stadler,Marco Thines,Duong Vu,Andrey Yurkov,Ning Zhang,Conrad L. Schoch +29 more
TL;DR: A conceptual framework for the identification of fungi is provided, encouraging the approach of integrative (polyphasic) taxonomy for species delimitation, i.e. the combination of genealogy, phenotype, and phenotype-based approaches to catalog the global diversity of fungi and establish initial species hypotheses.
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Posted Content
CNN Features off-the-shelf: an Astounding Baseline for Recognition
TL;DR: A series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13 suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.
Proceedings ArticleDOI
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
TL;DR: This paper employs probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification with an accuracy greater than 90%.