Recognizing Plant Species in the Wild: Deep Learning Results and a New Database

2016 ◽  
Author(s):  
Rene Octavio Queiroz Dias ◽  
Dibio Leandro Borges
Keyword(s):  
Deep Learning ◽  
Plant Species ◽  
In The Wild

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

scholarly journals The Affective Computing Approach to Affect Measurement

2017 ◽  
Vol 10 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Sidney D’Mello ◽  
Arvid Kappas ◽  
Jonathan Gratch
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Affective Computing ◽  
Measurement Accuracy ◽  
Computational Approach ◽  
Tipping Points ◽  
Affective Science ◽  
In The Wild ◽  
Machine Readable ◽  
Computing Approach

Affective computing (AC) adopts a computational approach to study affect. We highlight the AC approach towards automated affect measures that jointly model machine-readable physiological/behavioral signals with affect estimates as reported by humans or experimentally elicited. We describe the conceptual and computational foundations of the approach followed by two case studies: one on discrimination between genuine and faked expressions of pain in the lab, and the second on measuring nonbasic affect in the wild. We discuss applications of the measures, analyze measurement accuracy and generalizability, and highlight advances afforded by computational tipping points, such as big data, wearable sensing, crowdsourcing, and deep learning. We conclude by advocating for increasing synergies between AC and affective science and offer suggestions toward that direction.

scholarly journals Deep Learning Face Attributes in the Wild

2015 ◽  
Author(s):  
Ziwei Liu ◽  
Ping Luo ◽  
Xiaogang Wang ◽  
Xiaoou Tang
Keyword(s):  
Deep Learning ◽  
In The Wild

scholarly journals Propagation of Threatened Nepenthes khasiana: Methods and Precautions

2015 ◽  
Vol 7 (3) ◽  
pp. 313-315
Author(s):  
Jibankumar S. KHURAIJAM ◽  
Rup K. ROY
Keyword(s):  
Plant Species ◽  
Threatened Species ◽  
Ex Situ Conservation ◽  
Ex Situ ◽  
In The Wild ◽  
Propagation Technique ◽  
Subsequent Propagation ◽  
Nepenthes Khasiana

Ex-situ conservation is an important key in the management of rare, endangered and threatened (RET) plant species and its effectiveness depends on several factors. Maintenance of viable germplasm and its subsequent propagation plays an important role in long term conservation of many RET species. Nepenthes khasiana is a rare and gravely threatened species in the wild due to over-collection and other threats. The species needs urgent in-situ and ex-situ conservation. Development of easy to propagate techniques would pave faster multiplication for its use of educational, medicinal and horticultural purpose. In the present paper, successful propagation technique of Nepenthes khasiana through seeds is demonstrated along with detailed information on precautions to be taken during the adoption of the techniques.

scholarly journals A Comparison of Optimization Algorithms for Deep Learning

2020 ◽  
Vol 34 (13) ◽  
pp. 2052013 ◽  
Author(s):  
Derya Soydaner
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Optimization Algorithms ◽  
Gradient Methods ◽  
The Past ◽  
Basic Optimization ◽  
In The Wild ◽  
Image Datasets

In recent years, we have witnessed the rise of deep learning. Deep neural networks have proved their success in many areas. However, the optimization of these networks has become more difficult as neural networks going deeper and datasets becoming bigger. Therefore, more advanced optimization algorithms have been proposed over the past years. In this study, widely used optimization algorithms for deep learning are examined in detail. To this end, these algorithms called adaptive gradient methods are implemented for both supervised and unsupervised tasks. The behavior of the algorithms during training and results on four image datasets, namely, MNIST, CIFAR-10, Kaggle Flowers and Labeled Faces in the Wild are compared by pointing out their differences against basic optimization algorithms.

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