ArticleApplications in plant sciences2018
Raspberry Pi-powered imaging for plant phenotyping.
Article in Applications in plant sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed.
- Phenome-to-genome insights for evaluating root system architecture in field studies of maize.The plant genome · 2025Article
- Time course measurements of leaf elevation angles during shade avoidance response inmicroPublication biology · 2025Article
- GRABSEEDS: extraction of plant organ traits through image analysis.Plant methods · 2024Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.Plant physiology · 2024Article
- Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.Plant phenomics (Washington, D.C.) · 2024Article
- Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.Plant phenomics (Washington, D.C.) · 2024Article
- Development of an accurate low cost NDVI imaging system for assessing plant health.Plant methods · 2023Article
- Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography.Bio-protocol · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Tapping into the plasticity of plant architecture for increased stress resilience.F1000Research · 2023Review
- High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils.Frontiers in plant science · 2023Article
- Open Hardware in Science: The Benefits of Open Electronics.Integrative and comparative biology · 2022Review
- Phenotyping for waterlogging tolerance in crops: current trends and future prospects.Journal of experimental botany · 2022Review
- Capturing crop adaptation to abiotic stress using image-based technologies.Open biology · 2022Review
- Development of a plant conveyance system using an AGV and a self-designed plant-handling device: A case study of DIY plant phenotyping.Breeding science · 2022Article
- CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements.Biosensors · 2021Article
- Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications.Sensors (Basel, Switzerland) · 2021Article
- Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits.Plant & cell physiology · 2020Review
- Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity.The Plant journal : for cell and molecular biology · 2020Article
Corrections and comments
- Commented on by
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premise of the studyImage-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND
resultsWe used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV.
conclusionsThis protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.
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.