Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. H...

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Autores principales:	Sajid Ullah, Michael Henke, Narendra Narisetti, Klára Panzarová, Martin Trtílek, Jan Hejatko, Evgeny Gladilin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	high-throughput plant image analysis spike detection spike segmentation deep learning automated plant phenotyping Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/809f54c958a54ad19a00344090331f23
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spelling	oai:doaj.org-article:809f54c958a54ad19a00344090331f232021-11-25T18:56:26ZTowards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods10.3390/s212274411424-8220https://doaj.org/article/809f54c958a54ad19a00344090331f232021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7441https://doaj.org/toc/1424-8220Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.Sajid UllahMichael HenkeNarendra NarisettiKlára PanzarováMartin TrtílekJan HejatkoEvgeny GladilinMDPI AGarticlehigh-throughput plant image analysisspike detectionspike segmentationdeep learningautomated plant phenotypingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7441, p 7441 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high-throughput plant image analysis spike detection spike segmentation deep learning automated plant phenotyping Chemical technology TP1-1185
spellingShingle	high-throughput plant image analysis spike detection spike segmentation deep learning automated plant phenotyping Chemical technology TP1-1185 Sajid Ullah Michael Henke Narendra Narisetti Klára Panzarová Martin Trtílek Jan Hejatko Evgeny Gladilin Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
description	Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.
format	article
author	Sajid Ullah Michael Henke Narendra Narisetti Klára Panzarová Martin Trtílek Jan Hejatko Evgeny Gladilin
author_facet	Sajid Ullah Michael Henke Narendra Narisetti Klára Panzarová Martin Trtílek Jan Hejatko Evgeny Gladilin
author_sort	Sajid Ullah
title	Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
title_short	Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
title_full	Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
title_fullStr	Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
title_full_unstemmed	Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
title_sort	towards automated analysis of grain spikes in greenhouse images using neural network approaches: a comparative investigation of six methods
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/809f54c958a54ad19a00344090331f23
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Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods

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