PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System

Yasrab, Robail; Pound, Michael P.; French, A. P.; Pridmore, Tony

doi:10.1101/2020.05.03.075184

Cited by 15 publications

(6 citation statements)

References 47 publications

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“…forecast of haemorrhage into the cranium [20]), the plant domain, (e.g. estimating the characteristics of the plant [21] and recognizing the different headlines of Clickbait and classify it [22]), the pollution of air domain, (e.g. achieving a good prediction accuracy of air quality [23]), the financial domain, (e.g.…”

Section: Time Series Prediction Using Hybrid Neural Network Methods 21 Related Work Of Cnn-lstm Methodsmentioning

confidence: 99%

Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

2021

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The prediction lifetime of a Lithium-ion battery is able to be utilized as an early warning system to prevent the battery’s failure that makes it very significant for assuring safety and reliability. This paper represents a benchmark study that compares its RUL prediction results of single and hybrid methods with similar articles. We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining useful life (RUL) of Lithium-ion battery. We selected three statistical indicators (MAE, R², and RMSE) to assess the results of performance prediction. Experimental validation is performed using the lithium-ion battery dataset from the NASA and results reveal that the effectiveness of the suggested hybrid method in reducing the prediction error and in achieving better RUL prediction performance compared to the other algorithms.

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Section: Time Series Prediction Using Hybrid Neural Network Methods 21 Related Work Of Cnn-lstm Methodsmentioning

confidence: 99%

Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

2021

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“…The reconstruction of the data as 2D or 3D representations of the root system, and root segmentation from the medium usually assumes a high contrast between root and background, which is not always the case ( Atkinson et al, 2019 ). Machine and deep learning-based tools have been developed for root segmentation in 2D or 3D ( Iyer-Pascuzzi et al, 2010 ; Bucksch et al, 2014 ; Falk et al, 2020 ; Yasrab et al, 2020a ), including very thin (1–3 pixels) roots grown in visible medium (RootNet; Yasrab et al, 2020b ) and in soil ( Soltaninejad et al, 2020 ), while other tools aimed for RSA trait quantification ( Atkinson et al, 2017a ; Falk et al, 2020 ). Although there are many potential approaches to perform root segmentation, most are not suited for newer image data types.…”

Section: Applications Of Htpmentioning

confidence: 99%

“…In addition, few tools are capable of linking observed RSA to genotypic information. Recently, deep learning models have been employed to attempt to bridge phenotype to genotype predictions ( Pound et al, 2017a ; Yasrab et al, 2020a ) and can achieve similar results for QTL identification as user supervised methods ( Pound et al, 2017a ). However, to effectively integrate high-throughput phenotype to genotype tools into the breeding process requires refined tools.…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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“…With the purpose of showing the effectiveness of object detection algorithms in monitoring and detecting workers who fail to adhere to standard safety practices, a dataset was utilized that contained numerous instances of construction workers that could be classi ed as wearing a safety helmet, safety vest, both, or neither. Construction workers comprise 5% (more than 7 million employees) of the total workforce in the United States and almost 6.3% (more than $1.3 trillion) of its Gross Domestic Product (GDP) [ [90] and are capable of learning order dependence in sequence prediction and able to remember much previous information using Back Propagation (BP) or previous neuron signals and include it for the current processing [91], [92]. LSTM can be leveraged with various other architectures of NN [93]; CNN-LSTM was used to recognize workers' potentially unsafe behavior [94].…”

Section: Introductionmentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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Scholarly work points to 5S+1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6 th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6 th S in 5S+1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing Personal Protective Equipment (PPE).The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and veri es PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 Frames per Second (FPS), making it suitable for real-time detection.

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PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System

Cited by 15 publications

References 47 publications

Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

Enhanced safety implementation in 5S + 1 via object detection algorithms

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