Pre-processing of drought/water stress detection chlorophyll fluorescence wheat images for efficient segmentation

The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images (Control and Drought) that have been captured for a period of sixty days. A total of (1440 x 2) images are used for this research work.

The generated chlorophyll fluorescence set is subjected to various pre-processing operations including noise removal, and contrast enhancement. Pre-processed images are subjected to segmentation to increase water stress detection accuracy for automation in machine learning algorithms

本数据集包含针对Raj 3765号小麦品种的、基于文件系统存储的相关数据。本次研究共采集24幅叶绿素荧光图像，涵盖对照组与干旱胁迫组，图像采集周期为60天，总计使用（1440×2）幅图像开展相关研究工作。
所生成的叶绿素荧光数据集将经过一系列预处理操作，包括噪声去除与对比度增强。为提升机器学习算法自动化水分胁迫检测的精度，研究人员将对预处理后的图像实施分割操作。