Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
By Michael P. Pound, J. Atkinson, Alexandra J. Townsend, Michael H. Wilson, M. Griffiths, Aaron S. Jackson, Adrian Bulat, Georgios Tzimiropoulos, D. Wells, E. Murchie and 2 more. 2016
365 citations recorded by the source corpus.
Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.
Subfields: Ecology, Environmental Engineering, Plant Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Marcin Wozniak
- Farid Melgani
- Geeta Rani
- Sangeeta Vaibhav Meena
- Vijaypal Singh Dhaka
- Muhammad Fazal Ijaz
- Kavita Kavita
- Deepak Sinwar
In Plant Science
- Marcin Wozniak
- T. Hirayama
- Farid Melgani
- Geeta Rani
- Sangeeta Vaibhav Meena
- Vijaypal Singh Dhaka
- Muhammad Fazal Ijaz
- Kavita Kavita
Citations in the corpus
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, positive): “[62] applied CNNs to two types of identification tasks, classification and localization,”
- Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild (2019, background): “Agricultural applications are not an exception to this trend, and deep neural nets have been successfully been used to predict land use from remote sensing images (Hu et al. (2015)), plant phenotyping (Pound et al. (2016)) or weed scouting (Di Cicco et al. (2016)).”
- A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, background): “However, disease classification accuracy depends on human expertise in leaf image acquisition [9].”
- A review of deep learning used in the hyperspectral image analysis for agriculture (2021, methodology): “An approach (KELM-AL) using kernel-based ELM (KELM) with AL techniques is proposed (Pradhan et al. 2019a).”