An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

Article

Sharma, Neelesh, Banerjee, Bikram Pratap, Hayden, Matthew and Kant, Surya. 2023. "An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse." Plants. 12 (2). https://doi.org/10.3390/plants12020317
Article Title

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse

ERA Journal ID213962
Article CategoryArticle
AuthorsSharma, Neelesh, Banerjee, Bikram Pratap, Hayden, Matthew and Kant, Surya
Journal TitlePlants
Journal Citation12 (2)
Article Number317
Number of Pages19
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2223-7747
Digital Object Identifier (DOI)https://doi.org/10.3390/plants12020317
Web Address (URL)https://www.mdpi.com/2223-7747/12/2/317
Abstract

Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.

Keywordsco-registration; illumination correction; image processing; multispectral; segmentation; thermal
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020309999. Other agricultural, veterinary and food sciences not elsewhere classified
400999. Electronics, sensors and digital hardware not elsewhere classified
Byline AffiliationsAgriculture Victoria
La Trobe University
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