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    "description": "**Author**: Ankita Gupta, Dr.Lakwinder Kaur, Dr. Gurmeet Kaur    \r\n**Source**: Unknown - 01-11-2019 \r\n**Please cite**:   \r\n\r\nWater stress dataset for Indian variety of wheat crop: \r\n\r\nThe data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images captured every alternative day (Control and Drought) that have been captured for a period of sixty days. A total of (594 x 2) images are used for this research work.\r\nThis dataset comprises of images of wheat crop using Chlorophyll Fluorescence modality. Which is further used to identify drought water stress at canopy level in the wheat crop with the help of  Image Processing algorithms.\r\n\r\n\r\n\r\nAutocorrelation: (out.autoc)\r\nContrast: matlab (out.contr)\r\nCorrelation: matlab (out.corrm)\r\n4.Correlation: (out.corrp)\r\n5.Cluster Prominence: (out.cprom)\r\nCluster Shade: (out.cshad)\r\n7.Dissimilarity: (out.dissi)\r\nEnergy: matlab (out.energ)\r\nEntropy: (out.entro)\r\nHomogeneity: matlab (out.homom)\r\nHomogeneity: (out.homop)\r\nMaximum probability: (out.maxpr)\r\nSum of sqaures: Variance (out.sosvh)\r\nSum average (out.savgh)\r\nSum variance (out.svarh)\r\nSum entropy (out.senth)\r\nDifference variance (out.dvarh)\r\nDifference entropy (out.denth)\r\nInformation measure of correlation1 (out.inf1h)\r\nInformaiton measure of correlation2 (out.inf2h)\r\nInverse difference (INV) is homom (out.homom)\r\nInverse difference normalized (INN) (out.indnc)\r\nInverse difference moment normalized (out.idmnc)\r\nThese variables then undergone through various statistical processes to identify the key detection variables suited best for water stress which in-turn help to build root cause analysis model (RCA) for water stress.\r\nThe dataset has been produced using MATLAB GLCM libraries https:\/\/in.mathworks.com\/help\/images\/ref\/graycomatrix.html\r\n\r\nTexture feature analysis is done using 23 texture GLCM features to extract features pertaining to water stress identification.\r\n\r\n\r\nThese variables then undergone through various statistical processes to identify the key detection variables suited best for water stress which in-turn help to build root cause analysis model (RCA) for water stress.\r\nThe dataset has been produced using MATLAB GLCM libraries https:\/\/in.mathworks.com\/help\/images\/ref\/graycomatrix.html\r\n",
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    "uploader": "Puneet Arora",
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    "creator": null,
    "contributor": null,
    "date": "2020-05-30 07:46:36",
    "update_comment": null,
    "last_update": "2020-05-30 07:46:36",
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