Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein
Instances
- URL: http://arxiv.org/abs/2312.08805v1
- Date: Thu, 14 Dec 2023 10:45:54 GMT
- Title: Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein
Instances
- Authors: Ronja G\"uldenring, Rasmus Eckholdt Andersen, Lazaros Nalpantidis
- Abstract summary: 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.
- Score: 3.399289369740637
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robot perception is far from what humans are capable of. Humans do not only
have a complex semantic scene understanding but also extract fine-grained
intra-object properties for the salient ones. When humans look at plants, they
naturally perceive the plant architecture with its individual leaves and
branching system. In this work, we want to advance the granularity in plant
understanding for agricultural precision robots. 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 with keypoint-guided polyline annotations leading along the line
from the lowest stem point along the leaf basal to the leaf apex. Furthermore,
we introduce an adapted metric POKS complying with the concept of
keypoint-guided polylines. In our experimental evaluation, we provide baseline
results for our newly introduced dataset while showcasing the benefits of POKS
over OKS.
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) - BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level
Phenotyping of Sugar Beet Plants under Field Conditions [30.27773980916216]
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability.
Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) are helpful to address these challenges.
The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor.
arXiv Detail & Related papers (2023-12-22T14:06:44Z) - Keypoint Description by Symmetry Assessment -- Applications in
Biometrics [49.547569925407814]
We present a model-based feature extractor to describe neighborhoods around keypoints by finite expansion.
The iso-curves of such functions are highly symmetric w.r.t. the origin (a keypoint) and the estimated parameters have well defined geometric interpretations.
arXiv Detail & Related papers (2023-11-03T00:49:25Z) - 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) - Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf
Instance Segmentation in the Agricultural Domain [29.647846446064992]
Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities.
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
We propose a single convolutional neural network that addresses the three tasks simultaneously, exploiting their underlying hierarchical structure.
arXiv Detail & Related papers (2022-10-14T15:01:08Z) - GrowliFlower: An image time series dataset for GROWth analysis of
cauLIFLOWER [2.8247971782279615]
This article presents GrowliFlower, an image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021.
The dataset contains RGB and multispectral orthophotos from which about 14,000 individual plant coordinates are derived and provided.
The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size.
arXiv Detail & Related papers (2022-04-01T08:56:59Z) - A Deep Learning Generative Model Approach for Image Synthesis of Plant
Leaves [62.997667081978825]
We generate via advanced Deep Learning (DL) techniques artificial leaf images in an automatized way.
We aim to dispose of a source of training samples for AI applications for modern crop management.
arXiv Detail & Related papers (2021-11-05T10:53:35Z) - 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) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z) - 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.