Review for "Towards operational phytoplankton recognition with automated high-throughput imaging and compact convolutional neural networks"

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture used solves limitations of previous research and can be used at scale in commercial breeding programs.

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An automatic RTL compiler for high-throughput FPGA implementation of diverse deep convolutional neural networks

2017 27th International Conference on Field Programmable Logic and Applications (FPL) ◽

10.23919/fpl.2017.8056824 ◽

2017 ◽

Cited By ~ 37

Author(s):

Yufei Ma ◽

Yu Cao ◽

Sarma Vrudhula ◽

Jae-sun Seo

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

High Throughput ◽

Fpga Implementation ◽

Deep Convolutional Neural Networks

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Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells

RSC Advances ◽

10.1039/d1ra05324h ◽

2021 ◽

Vol 11 (51) ◽

pp. 32126-32134

Author(s):

Mohammad J. Eslamibidgoli ◽

Fabian P. Tipp ◽

Jenia Jitsev ◽

Jasna Jankovic ◽

Michael H. Eikerling ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Fuel Cells ◽

Polymer Electrolyte ◽

Convolutional Neural Networks ◽

High Throughput ◽

High Throughput Screening ◽

Catalyst Layer ◽

Polymer Electrolyte Fuel Cells

Deep learning enables the robust and accurate classification of the TEM images of catalyst layer inks for the polymer electrolyte fuel cells.

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Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Plant Phenomics ◽

10.34133/2020/4152816 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22 ◽

Cited By ~ 14

Author(s):

Yu Jiang ◽

Changying Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

High Throughput ◽

Plant Stress ◽

Postharvest Quality ◽

Plant Phenotyping ◽

Future Research ◽

Phenotypic Traits ◽

Deep Convolutional Neural Networks ◽

Comprehensive Overview

Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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FPGA-accelerated deep convolutional neural networks for high throughput and energy efficiency

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.3850 ◽

2016 ◽

Vol 29 (20) ◽

pp. e3850 ◽

Cited By ~ 25

Author(s):

Yuran Qiao ◽

Junzhong Shen ◽

Tao Xiao ◽

Qianming Yang ◽

Mei Wen ◽

...

Keyword(s):

Neural Networks ◽

Energy Efficiency ◽

Convolutional Neural Networks ◽

High Throughput ◽

Deep Convolutional Neural Networks

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High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12213617 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3617

Author(s):

Rodrigo Trevisan ◽

Osvaldo Pérez ◽

Nathan Schmitz ◽

Brian Diers ◽

Nicolas Martin

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Ground Truth ◽

Breeding Programs ◽

Growing Seasons ◽

Maturity Date ◽

High Throughput Phenotyping ◽

Commercial Breeding

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs.

Download Full-text