HSI-PP: A flexible open-source software for hyperspectral imaging-based plant phenotyping
文献类型： 外文期刊
作者： ElManawy, Ahmed Islam 1 ; Sun, Dawei 4 ; Abdalla, Alwaseela 1 ; Zhu, Yueming 1 ; Cen, Haiyan 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, State Key Lab Modern Opt Instrumentat, Hangzhou 310058, Peoples R China
2.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China
3.Suez Canal Univ, Fac Agr, Agr Engn Dept, Ismailia 41522, Egypt
4.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310000, Peoples R China
5.Zhejiang Univ, Former Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
6.866 Yuhangtang Rd, Hangzhou 310058, Peoples R China
关键词： Hyperspectral image analysis; Plant phenotyping; Feature extraction; Open-source software; Arabidopsis thaliana; Oilseed rape
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）
ISSN： 0168-1699
年卷期： 2022 年 200 卷
页码：
收录情况： SCI
摘要： Hyperspectral imaging has become one of the most popular techniques for high-throughput plant phenotyping. Extracting and analyzing useful plant phenotypic traits from hyperspectral images represents a major bottleneck for plant science and breeding communities. This study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping. The HSI-PP software integrates pre-processing, feature extraction, and modeling functions. The application of HSI-PP is exemplified by investigating the response of different Arabidopsis thaliana genotypes to drought stress, and the impact of various imaging angles on predicting the canopy nitrogen content (CNC) of oilseed rape (Brassica napus L.). The results showed that HSI-PP can process 10 GB on an ordinary PC in time ranging from 30 to 73 min according to image size and the complexity of the pipeline. HSI-PP extracted multiple phenotyping traits (spectral, textural, and morphological) of Arabidopsis thaliana from a large image dataset (104 GB) within five hours. The fusion of these features achieved higher accuracy (94%) than only using spectral information (85%) as early as day 4 after drought stress treatment. For oilseed rape, about 384 GB image data was processed within eighteen hours, and it was found that the tilted imaging angle of 75 had the optimized PLSR fitting (0.83) to the ground truth. The results demonstrate that HSI-PP is a stand-alone, automated, and open-source hyperspectral image processing platform adapted to various applications in plant phenotyping without requiring professional programming skills to serve the plant research community.
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