scholarly journals Corrigendum: Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

2018 ◽  
Vol 8 ◽  
Author(s):  
Jordan R. Ubbens ◽  
Ian Stavness
Keyword(s):  
Deep Learning ◽  
Plant Phenotyping ◽  
Learning Platform ◽  
Plant Phenomics

scholarly journals Review: Application of Artificial Intelligence in Phenomics

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4363
Author(s):  
Shona Nabwire ◽  
Hyun-Kwon Suh ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
Byoung-Kwan Cho
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Collection ◽  
New Technologies ◽  
Plant Traits ◽  
Imaging Techniques ◽  
Plant Phenotyping ◽  
Plant Phenomics ◽  
Field Phenotyping ◽  
High Throughput Phenotyping

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

scholarly journals Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Weighted Average ◽  
Age Related Macular Degeneration ◽  
Learning Platform ◽  
Age Related ◽  
Public Data ◽  
Primary Test ◽  
Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Semantic segmentation for plant phenotyping using advanced deep learning pipelines

2021 ◽  
Author(s):  
Pullalarevu Karthik ◽  
Mansi Parashar ◽  
S. Sofana Reka ◽  
Kumar T. Rajamani ◽  
Mattias P. Heinrich
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Plant Phenotyping

QuantMed: Component-based deep learning platform for translational research

2020 ◽  
Author(s):  
Jan Klein ◽  
Markus Wenzel ◽  
Daniel Romberg ◽  
Alexander Köhn ◽  
Peter Kohlmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Translational Research ◽  
Learning Platform

Violence recognition using convolutional neural network: A survey

2020 ◽  
Vol 39 (5) ◽  
pp. 7931-7952
Author(s):  
Gaurav Tripathi ◽  
Kuldeep Singh ◽  
Dinesh Kumar Vishwakarma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Law And Order ◽  
Linear Features ◽  
Automatic Feature Extraction ◽  
Learning Platform ◽  
Violence Detection ◽  
The World ◽  
Crowd Behaviour ◽  
Behaviour Changes

Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain.

scholarly journals DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

2020 ◽  
Vol 253 ◽  
pp. 107206 ◽  
Author(s):  
Yuzhi Zhang ◽  
Haidi Wang ◽  
Weijie Chen ◽  
Jinzhe Zeng ◽  
Linfeng Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Potential Energy ◽  
Concurrent Learning ◽  
Energy Models ◽  
Learning Platform

BCI cinematics – A pre-release analyser for movies using H2O deep learning platform

2019 ◽  
Vol 74 ◽  
pp. 547-556 ◽  
Author(s):  
Sundhara Kumar K.B ◽  
Krishna G ◽  
Bhalaji N ◽  
Chithra S
Keyword(s):  
Deep Learning ◽  
Learning Platform
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