ArticleSensors (Basel, Switzerland)2018
Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Perception and Localization Error Propagation and Robust Path-Tracking Control for Agricultural Machinery in Hilly Orchards: A Review.Sensors (Basel, Switzerland) · 2026Review
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Smart Image-Based Deep Learning System for Automated Quality Grading of Phalaenopsis Seedlings in Outsourced Production.Sensors (Basel, Switzerland) · 2025Article
- Outdoor Characterization and Geometry-Aware Error Modelling of an RGB-D Stereo Camera for Safety-Related Obstacle Detection.Sensors (Basel, Switzerland) · 2025Article
- A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond.Plant phenomics (Washington, D.C.) · 2025Review
- Technology-driven approaches to intelligent mechanical weed control: a systematic review for sustainable weed management.Frontiers in plant science · 2025Review
- Stereo Vision for Plant Detection in Dense Scenes.Sensors (Basel, Switzerland) · 2024Article
- Detection of maize stem diameter by using RGB-D cameras' depth information under selected field condition.Frontiers in plant science · 2024Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning.Sensors (Basel, Switzerland) · 2023Review
- Study on the detection of water status of tomato (Frontiers in plant science · 2023Article
- Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.Frontiers in plant science · 2023Article
- Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments.Sensors (Basel, Switzerland) · 2022Article
- Banana Pseudostem Width Detection Based on Kinect V2 Depth Sensor.Computational intelligence and neuroscience · 2022Article
- PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.Plant phenomics (Washington, D.C.) · 2022Article
- A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots.Plant phenomics (Washington, D.C.) · 2022Review
- Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Multiple Sensor Synchronization with theRealSense RGB-D Camera.Sensors (Basel, Switzerland) · 2021Article
- Human-Robot Perception in Industrial Environments: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.