Automatic Water Stress detection in wheat crop canopy using Chlorophyll fluorescence image dataset
Description
Pre-processed Chlorophyll fluorescence dataset of wheat canopy images for automatic water stress detection using machine learning models. The dataset is comprised of file system-based information about the wheat variety Raj 3765. The analysis is done for the period of sixty days, twenty-four chlorophyll fluorescence images every day for each (Control and Drought) have been recorded. A total of (1440 x 2 (for both control and drought) =2880) images has been utilized. The size of the dataset was increased to (2880*6) for this study's endeavor using data argumentation techniques in order to develop the more generalized model. The created dataset is subjected to different pre-processing pipelines comprising noise and contrast enhancement procedures. Pre-processed output images are subjected to novel segmentation algorithm called "Cfit k-means " to extract appropriate ROI which maximizes photosynthetic activity. The dataset and publications links are already shared in related links. PSII (photosystem -II) colour features and GLCM correlation-based features are used for further analysis in the automation process. The automation procedure involves the selection of an appropriate algorithm from the top 9 algorithms listed below for the said purpose. 1. Logistic Regression 2. Linear Discriminant Analysis (LDA) 3. K-nearest neighbours (KNN) 4. Decision Tree (CART) 5. Gaussian Naïve Bayes (GNB) 6. Support Vector Machine (SVM) 7. Extra Trees (ETC) 8. Gradient Boosting (GB) 9. Random forest (RF)