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

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-04-09 DOI:10.1016/j.ecoinf.2025.103134

Akash Kumar , Siddhartha Khare , Sergio Rossi

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引用次数: 0

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.

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一个深度学习Python框架，用于处理近距离延时PhenoCam数据

近景数码重复摄影是研究植物物候和季节动态的有力技术。然而，处理PhenoCam图像是耗时的，需要大量的专业知识。本文描述了phenai，一个Python框架，可以自动处理时间序列的PhenoCam图像。该软件包包括四个模块：(i)图像质量控制，（ii）使用深度学习的植被分割，（iii）绿色指数计算，（iv）参数提取。这些模块与文献中使用的标准和既定方法一致。我们通过分析黑云杉（Picea mariana (Mill.)）来演示PhenoAI包在案例研究中的应用。B.S.P.]加拿大魁北克省物候学研究，为期五年（2017-2021）。结果表明，绿色坐标（Green Chromatic Coordinate， GCC）的季初（SOS）出现在5月第3周（DOY 144±5），季末（EOS）出现在9月底（DOY 269±20），绿度最大值出现在7月第1周（DOY 183±5）。这些结果与以往在同一地区和物种的研究结果相吻合，证实了PhenoAI能够准确地复制野外观测结果。PhenoAI是一个开源软件包，可以定制以适应特定的研究需求，大大减少了处理时间，并简化了工作流程，使新用户可以使用它进行近距离观察PhenoCam。PhenoAI将提高科学家使用物候数据进行生态和林业研究的数据提取的效率和准确性。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.