Automatic Water Stress detection in wheat crop canopy using Chlorophyll fluorescence image dataset

Published: 31 October 2022| Version 2 | DOI: 10.17632/2mpd7d3vry.2
ankita gupta,


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)



Punjabi University


Machine Learning, Wheat, Photosystem I, Fluorescence, Chlorophyll, Color, Drought, Water Stress