Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

Ubbens, Jordan; Cieslak, Mikolaj; Prusinkiewicz, Przemysław; Stavness, Ian

doi:10.1101/557678

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Images represent a visual depiction of something. They have been utilized for diagnosis and detection in a variety of fields, including medicine, agriculture, and others [5]. A data-driven method to picture categorization is more robust to a wide variety of image features and disorders.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Classification of Poultry Disease

Srivastava,

Pandey

2023

Int. J. Autom. Smart

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Poultry diseases including Coccidiosis, Salmonella, and Newcastle can lower chicken productivity if they are not detected early on. Deep learning algorithms can assist with the early identification of diseases. In this study, a Convolutional Neural Network based framework has been proposed to classify poultry diseases by distinguishing healthy and unhealthy fecal images. Unhealthy images can be a sign of the poultry diseases. The Image Classification dataset was used to train the framework, and it was discovered that it performed with an accuracy of 99.99%, 96.05%, 93.23% on the training set, validation set, testing set respectively. When the proposed network's performance was evaluated against pre-trained models, it was discovered that the proposed model was unquestionably the best one for classifying chicken disease. This framework can beat resource-intensive machine learning methods due to the trained model's reduced weight and can be implemented with a small amount of memory and computational power.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Based Classification of Poultry Disease

Srivastava,

Pandey

2023

Int. J. Autom. Smart

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“…They used those traits for QTL mapping and were able to reproduce discovery of a QTL related to water 88 use (Ubbens et al, 2019). In this study, we expand their definition to also include principal component 89 analysis (PCA), which provides a way to create latent phenotypes without building machine learning 90 models.…”

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confidence: 99%

In-field whole plant maize architecture characterized by Latent Space Phenotyping

Gage

Richards

Lepak

et al. 2019

Preprint

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Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots. Machine learning produces heritable latent traits that describe plant architecture. Rover‐based phenotyping is far more efficient than manual phenotyping. Latent phenotypes from rovers are ready for application to plant biology and breeding. Collecting useful, interpretable, and biologically relevant phenotypes in a resource‐efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor‐based datasets of in‐field crop plants. Rovers equipped with lidar can produce three‐dimensional reconstructions of entire hybrid maize (Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole‐plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.

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Root system influence on high dimensional leaf phenotypes over the grapevine growing season

Harris

Klein

Awale

et al. 2020

Preprint

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SummaryIn many perennial crops, grafting the root system of one individual to the shoot system of another individual has become an integral part of propagation performed at industrial scales to enhance pest, disease, and stress tolerance and to regulate yield and vigor. Grafted plants offer important experimental systems for understanding the extent and seasonality of root system effects on shoot system biology.Using an experimental vineyard where a common scion ‘Chambourcin’ is growing ungrafted and grafted to three different rootstocks, we explore associations between root system genotype and leaf phenotypes in grafted grapevines across a growing season. We quantified five high-dimensional leaf phenotyping modalities: ionomics, metabolomics, transcriptomics, morphometrics, and physiology and show that rootstock influence is subtle but ubiquitous across modalities.We find strong signatures of rootstock influence on the leaf ionome, with unique signatures detected at each phenological stage. Moreover, all phenotypes and patterns of phenotypic covariation were highly dynamic across the season.These findings expand upon previously identified patterns to suggest that the influence of root system on shoot system phenotypes is complex and broad understanding necessitates volumes of high-dimensional, multi-scale data previously unmet.

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Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

Cited by 6 publications

References 30 publications

Deep Learning Based Classification of Poultry Disease

Deep Learning Based Classification of Poultry Disease

In-field whole plant maize architecture characterized by Latent Space Phenotyping

Root system influence on high dimensional leaf phenotypes over the grapevine growing season

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