Development of real-time machine vision system for water balance monitoring of Lilium flower

Water plays an important role in the growth and health of plants. The supply of this vital material affects not only agricultural yields, but also the quality and control of their physiological processes. Therefore, water scarcity has a negative effect on the growth of plants. In this research, in order to detect the plant's water requirement, a series of images of lilium plant under drought stress conditions were investigated to extract the color and morphological features and based on them, an intelligent system was designed to recognize the plant's water status. After analyzing the parameters extracted from the imagesusing statistical analysis at 5% probability level and the sequential forward feature ranking method, the most suitable features were selected to predict the moisture content of the plant; Furthermore, the classification was carried out using the support vector machine (SVM) with different kernels. Finally, it was shown that the classification accuracy for the linear kernels, sigmoeid, and RBF was with 6, 9, and 9 features, respectively, were 81.19, 81.04 and 83.12%, respectively. The results showed that the proposed system had the ability to determine the levels of stress and control the amount of water needed