Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments

Abstract

The objective of this study was to check whether different water and nitrogen treatments and, even the water-nitrogen coupling effect of plants could be correctly differentiated via chlorophyll a fluorescence image. We developed a classification method using the imaging analysis of chlorophyll a fluorescence induction based on Artificial Neural Network. The measurements were carried out on scheffera octophylla (Lour.) Harms, and the images were recorded at 690 nm with a high-resolution imaging device consisting of LEDs for an excitation at 460 nm and an Electron-Multiplying CCD camera. The effect of three different water and three different nitrogen treatments on the fluorescence parameters were obtained by hundreds of time-resolved fluorescence images. We used a Radial Basis Function neural network to model and test the sample data. The results showed that the different water and nitrogen statuses of plants were identified by the chlorophyll a fluorescence images and showed a high recognition accuracy. Compared with nitrogen, water had more of an influence on chlorophyll a fluorescence and was easier to identify. However, because the water and nitrogen restrict and promote each other, studying the coupling effect of water and nitrogen is necessary. Nine levels of water-nitrogen coupling plants were tested and classified. We discovered that a significant decrease on the classified accuracy was observed for the high nitrogen and low nitrogen treatments, while under a medium N-supply, the recognition rate was high. The method in this paper allowed plants to be classified under different water and nitrogen treatments, and has the potential to monitor the water and nitrogen coupling effect of plants in situ.