In-field high throughput grapevine phenotyping with a consumer-grade
depth camera
- URL: http://arxiv.org/abs/2104.06945v1
- Date: Wed, 14 Apr 2021 16:16:27 GMT
- Title: In-field high throughput grapevine phenotyping with a consumer-grade
depth camera
- Authors: Annalisa Milella, Roberto Marani, Antonio Petitti, Giulio Reina
- Abstract summary: 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.
- Score: 1.5541946106879052
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Plant phenotyping, that is, the quantitative assessment of plant traits
including growth, morphology, physiology, and yield, is a critical aspect
towards efficient and effective crop management. Currently, plant phenotyping
is a manually intensive and time consuming process, which involves human
operators making measurements in the field, based on visual estimates or using
hand-held devices. 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.
Related papers
- Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system.
We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z) - Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions.
Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
arXiv Detail & Related papers (2024-03-27T08:42:47Z) - 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) - 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) - 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) - 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) - High-Throughput Image-Based Plant Stand Count Estimation Using
Convolutional Neural Networks [23.67862313758282]
We propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages.
Our proposed method can successfully count corn stands and out-perform other state-of-the-art methods.
arXiv Detail & Related papers (2020-10-23T17:28:29Z) - Computer Vision with Deep Learning for Plant Phenotyping in Agriculture:
A Survey [25.365163119362045]
Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions.
Plant phenotyping techniques play a major role in accurate crop monitoring.
This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.
arXiv Detail & Related papers (2020-06-18T14:21:19Z)
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.