Learning with fewer labels in deep learning for plant phenotyping

QR Code

Learning with fewer labels in deep learning for plant phenotyping

Deep learning has become an important approach in the research of plant phenotyping. However, training a highly-performing deep learning model generally requires a large amount of labeled data. These training data are normally labeled by human annotators and the labeling process is expensive and tim...

Full description

Bibliographic Details
Main Author:	Chen, Feng
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2022
Online Access:	https://eprints.nottingham.ac.uk/71783/

Similar Items

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

Deep learning for multi-task 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)

Fewer dropouts but figures still worrying
by: The Star,
Published: (2017)

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

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

Deep Learning Using Tiny Domain-Specific Datasets with Sparse Labels
by: Smith, Thomas J
Published: (2021)

Populations with higher average intelligence are healthier, but do they smoke fewer cigarettes?
by: Ahmad Suhaimi, Siti Shazwani, et al.
Published: (2019)

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

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)

Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning
by: Marrable, Daniel, et al.
Published: (2022)

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

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

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

Herbal plant image classification using transfer learning and fine-tuning deep learning model
by: Khalid, Fatimah, et al.
Published: (2024)

Herbal plant image classification using transfer learning and fine-tuning deep learning model
by: Khalid, Fatimah, et al.
Published: (2024)

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

The efficacy of deep learning algorithm in classifying chilli plant growth stages
by: Muammar Rozilan, Danial Mirza, et al.
Published: (2021)

The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware
by: Nur Khairani, Kamarudin, et al.
Published: (2024)

Deep learning-based approach in plant species identification / Tan Jing Wei
by: Tan, Jing Wei
Published: (2018)

Integration of image processing algorithm and deep learning approaches to monitor ginger plant
by: Tan, Cheng Yong
Published: (2024)

Develop a mobile learning application on food labels
by: Tan, Chee Hong
Published: (2015)

Deep Reinforcement Learning For Control
by: Bakar, Nurul Asyikin Abu
Published: (2021)

Deep Learning for Coral Classification
by: Mahmood, A., et al.
Published: (2017)

Vehicle Detection in Deep Learning
by: Teoh, Per Nian
Published: (2019)

Deep learning for image classification
by: Koi, Chin Chong
Published: (2024)

Deep learning sensor fusion in plant water stress assessment: a comprehensive review
by: Kamarudin, Mohd Hider, et al.
Published: (2021)

Learning approaches in accounting education: towards deep learning
by: Lau, Yeng Wai, et al.
Published: (2015)

Leveraging transfer learning with deep learning for crime prediction
by: Butt, Umair Muneer, et al.
Published: (2024)

Learning And Optimization Of The Kernel Functions From Insufficiently Labeled Data
by: Abbasnejad, M. Ehsan
Published: (2010)

Deep learning approach with image noise reduction to determine planting density and defected paddy seedlings
by: Mohamed Anuar, Mohamed Marzhar
Published: (2022)

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

Deep learning model for opinion mining
by: Lee, Hao Jie
Published: (2022)

Deep learning for emotional speech recognition
by: Alhamada, M. I., et al.
Published: (2020)

Financial market predictions with deep learning
by: Yap, Zhong Jing, et al.
Published: (2023)

Ensemble deep learning for tuberculosis detection
by: Ahmad Hijazi, Mohd Hanafi, et al.
Published: (2020)

Face recognition using deep learning
by: Ooi, Zi Xen
Published: (2019)

Smart Security Camera With Deep Learning
by: Wong, Yee Cheng
Published: (2020)

Deep learning for EEG data analysis
by: Cheah, Kit Hwa
Published: (2018)