Plant Stem Segmentation Using Fast Ground Truth Generation
- URL: http://arxiv.org/abs/2001.08854v1
- Date: Fri, 24 Jan 2020 00:22:14 GMT
- Title: Plant Stem Segmentation Using Fast Ground Truth Generation
- Authors: Changye Yang, Sriram Baireddy, Yuhao Chen, Enyu Cai, Denise Caldwell,
Val\'erian M\'eline, Anjali S. Iyer-Pascuzzi, Edward J. Delp
- Abstract summary: In this paper, we show that deep learning methods can accurately segment tomato plant stems.
We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach.
- Score: 18.208361096194654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurately phenotyping plant wilting is important for understanding responses
to environmental stress. Analysis of the shape of plants can potentially be
used to accurately quantify the degree of wilting. Plant shape analysis can be
enhanced by locating the stem, which serves as a consistent reference point
during wilting. In this paper, we show that deep learning methods can
accurately segment tomato plant stems. We also propose a control-point-based
ground truth method that drastically reduces the resources needed to create a
training dataset for a deep learning approach. Experimental results show the
viability of both our proposed ground truth approach and deep learning based
stem segmentation.
Related papers
- Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw) [7.13465721388535]
We present a dataset of 3D point clouds of strawberry plants for two varieties, totalling 84 individual point clouds.
We focus on the end use of such tools - the extraction of biologically relevant phenotypes - to demonstrate a phenotyping pipeline on the dataset.
This comprises of the steps, including; segmentation, skeletonisation and tracking, and we detail how each stage facilitates the extraction of different phenotypes or provision of data insights.
arXiv Detail & Related papers (2024-03-01T14:44:05Z) - Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein
Instances [3.399289369740637]
We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances.
The underlying dataset RumexLeaves is made publicly available and is the first of its kind.
We introduce an adapted metric POKS complying with the concept of keypoint-guided polylines.
arXiv Detail & Related papers (2023-12-14T10:45:54Z) - Eff-3DPSeg: 3D organ-level plant shoot segmentation using
annotation-efficient point clouds [1.5882586857953638]
We propose a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation.
High-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system.
A weakly-supervised deep learning method was proposed for plant organ segmentation.
arXiv Detail & Related papers (2022-12-20T14:09:37Z) - Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping.
Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
arXiv Detail & Related papers (2022-10-24T14:37:09Z) - End-to-end deep learning for directly estimating grape yield from
ground-based imagery [53.086864957064876]
This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards.
Three model architectures were tested: object detection, CNN regression, and transformer models.
The study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale.
arXiv Detail & Related papers (2022-08-04T01:34:46Z) - Potato Crop Stress Identification in Aerial Images using Deep
Learning-based Object Detection [60.83360138070649]
The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks.
The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level.
Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
arXiv Detail & Related papers (2021-06-14T21:57:40Z) - Surface Warping Incorporating Machine Learning Assisted Domain
Likelihood Estimation: A New Paradigm in Mine Geology Modelling and
Automation [68.8204255655161]
A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data.
This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable.
Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
arXiv Detail & Related papers (2021-02-15T10:37:52Z) - Deep Semi-supervised Knowledge Distillation for Overlapping Cervical
Cell Instance Segmentation [54.49894381464853]
We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation.
We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining.
Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
arXiv Detail & Related papers (2020-07-21T13:27:09Z) - Two-View Fine-grained Classification of Plant Species [66.75915278733197]
We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species.
A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
arXiv Detail & Related papers (2020-05-18T21:57:47Z) - Deep Transfer Learning For Plant Center Localization [19.322420819302263]
This paper investigates methods that estimate plant locations for a field-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs)
Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training.
We propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data.
arXiv Detail & Related papers (2020-04-29T06:29:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.