PhenoAI: A deep learning Python framework to process close-range time-lapse PhenoCam data

Close-range digital repeat photography is a powerful technique for studying phenology and the seasonal dynamics of plants. However, the processing of PhenoCam images is time-consuming and requires substantial human expertise. This paper describes PhenoAI , a Python framework that automates the processing of time-series PhenoCam images. The package consists of four modules: (i) image quality control, (ii) vegetation segmentation using deep learning, (iii) greenness index calculation, and (iv) parameter extraction. These modules are consistent with the standard and established methodologies used in the literature. We demonstrate the application of the PhenoAI package in a case study by analyzing black spruce [ Picea mariana (Mill.) B.S.P.] phenology in Quebec, Canada, over five years (2017–2021). The result revealed that the Start of Season (SOS) of Green Chromatic Coordinate (GCC) occurred in the third week of May (DOY 144 ± 5), End of Season (EOS) occurred in the end of September (DOY 269 ± 20) and day of maximum greenness occurred in the first week of July (DOY 183 ± 5). The findings correlate with the previous studies in the same region and species, confirming the ability of the PhenoAI to replicate field observations accurately. PhenoAI is an open-source software package that can be customized to suit specific research needs, reduces significantly the processing time, and simplifies the workflow, making it accessible for use by new users for close range observations taken by PhenoCam. PhenoAI will enhance efficiency and accuracy of data extraction for scientists using phenological data for ecological and forestry research. • PhenoAI automates the data processing chain for close-range PhenoCam imagery. • The image quality control module filters out low-quality images for analysis. • Deep learning effectively segments and identifies different types of vegetation. • Demonstrated the framework in a 5-year case study of black spruce phenology. • PhenoAI reduces processing time and simplifies image analysis workflow.

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Publication Details

Journal
Ecological Informatics
Published
2025-04-09
DOI
https://doi.org/10.1016/j.ecoinf.2025.103134
Citations
2
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
3.27
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article

PhenoAI: A deep learning Python framework to process close-range time-lapse PhenoCam data

Akash Kumar, Siddhartha Khare, Sergio Rossi
2 citations
Ecological Informatics
Cell Image Analysis Techniques
3.27
article

PhenoAI: A deep learning Python framework to process close-range time-lapse PhenoCam data

Akash Kumar, Siddhartha Khare, Sergio Rossi
article en
2 citations

Abstract

Close-range digital repeat photography is a powerful technique for studying phenology and the seasonal dynamics of plants. However, the processing of PhenoCam images is time-consuming and requires substantial human expertise. This paper describes PhenoAI , a Python framework that automates the processing of time-series PhenoCam images. The package consists of four modules: (i) image quality control, (ii) vegetation segmentation using deep learning, (iii) greenness index calculation, and (iv) parameter extraction. These modules are consistent with the standard and established methodologies used in the literature. We demonstrate the application of the PhenoAI package in a case study by analyzing black spruce [ Picea mariana (Mill.) B.S.P.] phenology in Quebec, Canada, over five years (2017–2021). The result revealed that the Start of Season (SOS) of Green Chromatic Coordinate (GCC) occurred in the third week of May (DOY 144 ± 5), End of Season (EOS) occurred in the end of September (DOY 269 ± 20) and day of maximum greenness occurred in the first week of July (DOY 183 ± 5). The findings correlate with the previous studies in the same region and species, confirming the ability of the PhenoAI to replicate field observations accurately. PhenoAI is an open-source software package that can be customized to suit specific research needs, reduces significantly the processing time, and simplifies the workflow, making it accessible for use by new users for close range observations taken by PhenoCam. PhenoAI will enhance efficiency and accuracy of data extraction for scientists using phenological data for ecological and forestry research. • PhenoAI automates the data processing chain for close-range PhenoCam imagery. • The image quality control module filters out low-quality images for analysis. • Deep learning effectively segments and identifies different types of vegetation. • Demonstrated the framework in a 5-year case study of black spruce phenology. • PhenoAI reduces processing time and simplifies image analysis workflow.

Ecological InformaticsVol. 88
Université du Québec à Chicoutimi (CA), Indian Institute of Technology Roorkee (IN), Central Building Research Institute (IN)
Openalex Percentile: Top 9%
Cell Image Analysis Techniques
3.27
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