ArticleSensors (Basel, Switzerland)2016
Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 47 citations in OpenAlex.
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.BMC plant biology · 2024Article
- Research on sports image classification method based on SE-RES-CNN model.Scientific reports · 2024Article
- A perspective on computer vision in biosensing.Biomicrofluidics · 2024Article
- Recognition of Maize Phenology in Sentinel Images with Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation.Sensors (Basel, Switzerland) · 2021Article
- The Snapdragon LATE ELONGATED HYPOCOTYL Plays A Dual Role in Activating Floral Growth and Scent Emission.Cells · 2019Article
- Article
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.GigaScience · 2019Review
- Leveraging Image Analysis for High-Throughput Plant Phenotyping.Frontiers in plant science · 2019Review
- Systems biology of seeds: decoding the secret of biochemical seed factories for nutritional security.3 Biotech · 2018Review
- An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.PloS one · 2018Article
- Editorial: Phenomics.Frontiers in plant science · 2018Article
- Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.GigaScience · 2017Review
- Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.Scientific reports · 2017Article
- An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping.Plant methods · 2017Article
- AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping.Plant methods · 2017Article
- A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage.Plant methods · 2017Article
- A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.
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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.