Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping
- URL: http://arxiv.org/abs/2210.13296v1
- Date: Mon, 24 Oct 2022 14:37:09 GMT
- Title: Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping
- Authors: Petros N. Tamvakis, Chairi Kiourt, Alexandra D. Solomou, George
Ioannakis and Nestoras C. Tsirliganis
- Abstract summary: 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.
- Score: 59.0626764544669
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Plant phenotyping refers to a quantitative description of the plants
properties, however in image-based phenotyping analysis, our focus is primarily
on the plants anatomical, ontogenetical and physiological properties.This
technique reinforced by the success of Deep Learning in the field of image
based analysis is applicable to a wide range of research areas making
high-throughput screens of plants possible, reducing the time and effort needed
for phenotypic characterization.In this study, we use Deep Learning methods
(supervised and unsupervised learning based approaches) to semantically segment
grapevine leaves images in order to develop an automated object detection
(through segmentation) system for leaf phenotyping which will yield information
regarding their structure and function.In these directions we studied several
deep learning approaches with promising results as well as we reported some
future challenging tasks in the area of precision agriculture.Our work
contributes to plant lifecycle monitoring through which dynamic traits such as
growth and development can be captured and quantified, targeted intervention
and selective application of agrochemicals and grapevine variety identification
which are key prerequisites in sustainable agriculture.
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) - Neural Echos: Depthwise Convolutional Filters Replicate Biological
Receptive Fields [56.69755544814834]
We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina.
We propose a scheme that draws inspiration from the biological receptive fields.
arXiv Detail & Related papers (2024-01-18T18:06:22Z) - 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) - Morphological Profiling for Drug Discovery in the Era of Deep Learning [13.307277432389496]
We provide a comprehensive overview of the recent advances in the field of morphological profiling.
We place a particular emphasis on the application of deep learning in this pipeline.
arXiv Detail & Related papers (2023-12-13T05:08:32Z) - 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) - Temporal Prediction and Evaluation of Brassica Growth in the Field using
Conditional Generative Adversarial Networks [1.2926587870771542]
The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors.
This paper proposes a novel monitoring approach that comprises high- throughput imaging sensor measurements and their automatic analysis.
Our approach's core is a novel machine learning-based growth model based on conditional generative adversarial networks.
arXiv Detail & Related papers (2021-05-17T13:00:01Z) - In-field high throughput grapevine phenotyping with a consumer-grade
depth camera [1.5541946106879052]
Plant phenotyping is a quantitative assessment of plant traits including growth, morphology, physiology, and yield.
In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting.
It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted onboard an agricultural vehicle.
arXiv Detail & Related papers (2021-04-14T16:16:27Z) - Deep Low-Shot Learning for Biological Image Classification and
Visualization from Limited Training Samples [52.549928980694695]
In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared.
labeling training data with precise stages is very time-consuming even for biologists.
We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
arXiv Detail & Related papers (2020-10-20T06:06:06Z) - 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)
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.