| Timely detection of water stress in agricultural crops is important. In this paper, a smart classification algorithm was developed to detect water stress in tomato plants that were grown in the greenhouse. During the growth period, thermal and visible light images were acquired from the canopy tops in two states: (1) plants in normal conditions; and (2) plants under water stress. Images were obtained using a camera that recorded simultaneous frames of thermal and visible (red, green, and blue (RGB)) features. Based on these features, 22 parameters were defined and applied to classify the image frames. In order to develop an efficient algorithm, principal component analysis (PCA) was applied to optimize the classifying of parameters. For normalizing the... |
