ArticlePlant methods2017
A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions.
Article in Plant methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning.Scientific reports · 2026Article
- Article
- Raspberry plant stress detection using hyperspectral imaging.Plant direct · 2023Article
- A Survey of Robotic Harvesting Systems and Enabling Technologies.Journal of intelligent & robotic systems · 2023Review
- Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.Frontiers in plant science · 2023Article
- Development and Validation of Methodology for Estimating Potato Canopy Structure for Field Crop Phenotyping and Improved Breeding.Frontiers in plant science · 2021Article
- High-Throughput Plant Phenotyping for Developing Novel Biostimulants: From Lab to Field or From Field to Lab?Frontiers in plant science · 2018Article
- On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties.Frontiers in plant science · 2018Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400-2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r
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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.