scholarly journals Automated recognition of optical image based potato leaf blight diseases using deep learning

2021 ◽  
pp. 101781
Author(s):  
Kulendu Kashyap Chakraborty ◽  
Rashmi Mukherjee ◽  
Chandan Chakroborty ◽  
Kangkana Bora
Keyword(s):  
Deep Learning ◽  
Leaf Blight ◽  
Optical Image ◽  
Potato Leaf ◽  
Automated Recognition

scholarly journals ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

2021 ◽  
Author(s):  
Hayley Weir ◽  
Keiran Thompson ◽  
Amelia Woodward ◽  
Benjamin Choi ◽  
Augustin Braun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Quantum Chemistry ◽  
Automated Recognition ◽  
Domain Expertise

Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline,...

scholarly journals Deep Learning for SAR-Optical Image Matching

2019 ◽  
Author(s):  
Lloyd Haydn Hughes ◽  
Nina Merkle ◽  
Tatjana Burgmann ◽  
Stefan Auer ◽  
Michael Schmitt
Keyword(s):  
Deep Learning ◽  
Image Matching ◽  
Optical Image

scholarly journals The Effectiveness of Endophytic Fungi Origin of Potato Plant Roots in Coffee Skin Compost Media to Suppress Development of Potato Leaf Disease (Phytophtora infestans)

2020 ◽  
Vol 2 (1) ◽  
pp. 101-105
Author(s):  
Anna Fitriana ◽  
Lukman Hakim ◽  
Marlina Marlina
Keyword(s):  
Experimental Method ◽  
Endophytic Fungi ◽  
Potato Plant ◽  
Potato Production ◽  
Leaf Blight ◽  
Plant Roots ◽  
Potato Leaf ◽  
Potato Plants ◽  
Pathogen Attack ◽  
Blight Disease

Potato leaf blight is caused by Phytophthora infestans fungus is one of the important diseases in potato plants. The decrease in potato production due to P. infestans can reach 90%. Until now, P. infestans pathogen attack is an important problem and there is no fungicide that is really effective against the disease. This study aims to examine the effectiveness of endophytic fungi from potato roots in suppressing the development of P. infestans potato leaf blight disease carried out at University Farm Stasiun Riset Bener Meriah (UFBM) Syiah Kuala University Tunyang Village, Timang Gajah District, Bener Meriah Regency from May to October 2014. The method used is the experimental method. The results of this study indicate that endophytic fungi from the roots of potato plants in coffee skin compost media can suppress the development of leaf blight caused by P. infestans, endophytic fungi from potato plant roots in coffee skin compost media. The best results were found in B9 endophytic fungi isolates with the intensity of the pathogen attack P. infestans 48.00%, the intensity of damage to potato plants due to pathogen P. infestans and 2.60%, the weight of healthy tubers 332.4 grams.

scholarly journals BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingyu Gao ◽  
Fei Wang ◽  
Peng Song ◽  
Junyan Liu ◽  
DaWei Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Image Features ◽  
Optical Image ◽  
Training Time ◽  
Fine Grained ◽  
Detection Model ◽  
Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

scholarly journals Deep Learning‐Based Single‐Cell Optical Image Studies

2020 ◽  
Vol 97 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Jing Sun ◽  
Attila Tárnok ◽  
Xuantao Su
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Optical Image

scholarly journals Automated recognition of white blood cells using deep learning

2020 ◽  
Vol 10 (3) ◽  
pp. 359-367 ◽  
Author(s):  
Amin Khouani ◽  
Mostafa El Habib Daho ◽  
Sidi Ahmed Mahmoudi ◽  
Mohammed Amine Chikh ◽  
Brahim Benzineb
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Automated Recognition

Tomato And Potato Leaf Disease Prediction With Health Benefits Using Deep Learning Techniques

2021 ◽  
Author(s):  
K. Karthik ◽  
S. Rajaprakash ◽  
S. Nazeeb Ahmed ◽  
Rishan Perincheeri ◽  
C. Risho Alexander
Keyword(s):  
Deep Learning ◽  
Health Benefits ◽  
Disease Prediction ◽  
Potato Leaf ◽  
Leaf Disease ◽  
Learning Techniques

scholarly journals Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

2018 ◽  
Vol 7 (10) ◽  
pp. 389 ◽  
Author(s):  
Wei He ◽  
Naoto Yokoya
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Super Resolution ◽  
Optical Image ◽  
Optical Data ◽  
Image Simulation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Multi Temporal ◽  
Sar Data

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.

Sign in / Sign up

Export Citation Format

Share Document