{
    "abstract": "Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and RGB images of Xinjiang date palm were collected. The RGB images were preprocessed and their color features were extracted using Python and OpenCV. Through correlation analysis, 21 color features highly correlated with chlorophyll content were selected and downscaled with principal component analysis. Models including SVR, RVM, CNN, CNN-SVR, and CNN-RVM were used for prediction. Among them, the CNN-SVR model showed the most stable performance with R",
    "authors": [
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.\nKey Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alaer 843300, China.",
            "firstname": "Qi",
            "initials": "Q",
            "lastname": "Wang"
        },
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.",
            "firstname": "Ziyan",
            "initials": "Z",
            "lastname": "Shi"
        },
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.",
            "firstname": "Kaiyao",
            "initials": "K",
            "lastname": "Hou"
        },
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.\nKey Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alaer 843300, China.",
            "firstname": "Ning",
            "initials": "N",
            "lastname": "Yan"
        },
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.",
            "firstname": "Cuiyun",
            "initials": "C",
            "lastname": "Wu"
        },
        {
            "affiliation": "College of Information Engineering, Tarim University, Alaer 843300, China.",
            "firstname": "Xu",
            "initials": "X",
            "lastname": "Li"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3390/s25082545\n10.1016/j.carpta.2022.100220\n10.1016/j.lwt.2018.11.059\n10.1016/j.psra.2016.09.001\n10.1016/j.jff.2020.104205\n10.3390/foods13020193\n10.3389/fpls.2023.1260772\n10.1007/BF00039173\n10.3389/fpls.2021.786309\n10.1046/j.0028-646X.2001.00289.x\n10.1088/1755-1315/441/1/012056\n10.17660/ActaHortic.2001.564.28\n10.17660/ActaHortic.2007.754.37\n10.1007/s10341-021-00580-8\n10.1016/j.saa.2019.117202\n10.3389/fpls.2018.00869\n10.1016/S1011-1344(01)00145-2\n10.1016/j.isprsjprs.2014.08.005\n10.1016/j.compag.2024.109442\n10.1016/j.pmcj.2023.101874\n10.1007/s10346-021-01702-9\n10.1016/j.indcrop.2024.119233\n10.1016/j.geoderma.2020.114863\n10.1016/j.eja.2024.127331\n10.3390/agriculture14081262\n10.1016/j.compag.2024.109402\n10.1007/s11222-024-10476-8\n10.1016/j.engappai.2022.104860\n10.3390/horticulturae9080899\n10.1016/j.compag.2022.106711\n10.13031/2013.27838\n10.1080/10106040108542184\n10.1016/j.fcr.2010.05.011\n10.1016/j.jag.2014.03.018\n10.1016/j.compag.2024.109122\n10.3390/agronomy13030663\n10.3390/horticulturae10060548\n10.2478/fhort-2020-0006",
    "journal": "Sensors (Basel, Switzerland)",
    "keywords": [
        "SPAD",
        "chlorophyll content",
        "jujube leaves",
        "precision agriculture",
        "smartphone images"
    ],
    "methods": null,
    "publication_date": "2025-04-26",
    "pubmed_id": "40285234\n38254493\n38034562\n11538390\n35003172\n31181506\n30002664\n11485848",
    "results": null,
    "title": "Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling.",
    "xml": "<Element 'PubmedArticle' at 0x78850631dcb0>"
}