Deep learning for multi-task plant phenotyping

QR Code

Deep learning for multi-task plant phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impress...

Full description

Bibliographic Details
Main Authors:	Pound, Michael P., Atkinson, Jonathan A., Wells, Darren M., Pridmore, Tony P., French, Andrew P.
Format:	Conference or Workshop Item
Published:	2017
Online Access:	https://eprints.nottingham.ac.uk/47610/

Similar Items

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
by: Pound, Michael P., et al.
Published: (2017)

Deep machine learning provides state-of-the art performance in image-based plant phenotyping
by: Pound, Michael P., et al.
Published: (2016)

Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction
by: Pridmore, Tony P., et al.
Published: (2018)

Uncovering the hidden half of plants using new advances in root phenotyping
by: Atkinson, Jonathan A., et al.
Published: (2019)

A patch-based approach to 3D plant shoot phenotyping
by: Pound, Michael P., et al.
Published: (2016)

Towards low-cost image-based plant phenotyping using reduced-parameter CNN
by: Atanbori, John, et al.
Published: (2018)

Approaches to three-dimensional reconstruction of plant shoot topology and geometry
by: Gibbs, Jonathon, et al.
Published: (2016)

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system
by: Gibbs, Jonathon, et al.
Published: (2015)

Automated recovery of 3D models of plant shoots from multiple colour images
by: Pound, Michael P., et al.
Published: (2014)

Deep Learning for Plant Phenotyping
by: Mori, Matteo
Published: (2016)

Learning with fewer labels in deep learning for plant phenotyping
by: Chen, Feng
Published: (2022)

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping
by: Pound, Michael P., et al.
Published: (2017)

Synthetic data driven deep learning for plant phenotyping
by: Hartley, Zane K.J.
Published: (2024)

Field phenotyping for the future
by: Atkinson, Jonathan A., et al.
Published: (2018)

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology
by: Burrell, Thomas, et al.
Published: (2017)

An updated protocol for high throughput plant tissue sectioning
by: Atkinson, Jonathan A., et al.
Published: (2017)

Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat
by: Atkinson, Jonathan A., et al.
Published: (2015)

Phenotyping root architecture in diverse wheat germplasm
by: Atkinson, Jonathan A.
Published: (2016)

Leaf segmentation in plant phenotyping: a collation study
by: Scharr, Hanno, et al.
Published: (2016)

The 4-dimensional plant: effects of wind-induced canopy movement on light fluctuations and photosynthesis
by: Burgess, Alexandra J., et al.
Published: (2016)

Deep reinforcement learning to multi-agent deep reinforcement learning
by: Samieiyeganeh, Mehdi, et al.
Published: (2022)

DeepPulmoTB: a benchmark dataset for multi-task learning of tuberculosis lesions in lung computerized tomography (CT)
by: Tan, Zhuoyi, et al.
Published: (2024)

On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images
by: Mairhofer, Stefan, et al.
Published: (2014)

Deep plant: A deep learning approach for plant classification / Lee Sue Han
by: Lee , Sue Han
Published: (2018)

Plant phenomics, from sensors to knowledge
by: Tardieu, François, et al.
Published: (2017)

A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties
by: Rouillard, Thibault
Published: (2020)

Herbal Plant Identification Using Deep Learning
by: Md., Fouziya, et al.
Published: (2025)

Plant disease detection using deep learning
by: Cheng, Jung Yin
Published: (2024)

Multi-scale digital soil mapping with deep learning
by: Behrens, T., et al.
Published: (2018)

Multi-objective deep reinforcement learning for recommendation systems
by: Keat, Ee Yeo, et al.
Published: (2022)

Deep learning detector for pests and plant disease recognition
by: Ileladewa, Oluwatimilehin Adekunle
Published: (2020)

Task-specific transfer of perceptual learning across sensory modalities
by: McGovern, David P., et al.
Published: (2016)

Molecular and phenotypic characterization of potential plant growth-promoting Pseudomonas from rice and maize rhizospheres
by: Lawongsa, P., et al.
Published: (2008)

Edge computing techniques for classification based multi-task incremental learning
by: Dube, Swaraj Sunilkumar
Published: (2023)

Smart car plate recognition system using multi-task learning
by: Khor, Yin Loon
Published: (2024)

SMURFS: superpixels from multi-scale refinement of super-regions
by: Luengo, Imanol, et al.
Published: (2016)

Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning
by: Goh, Ching Pang
Published: (2023)

High-resolution three-dimensional structural data quantify the impact of photoinhibition on long-term carbon gain in wheat canopies in the field
by: Burgess, Alexandra J., et al.
Published: (2015)

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
by: Lowe, Amy, et al.
Published: (2017)

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies
by: Atkinson, Jonathan A., et al.
Published: (2017)