Monitoring and Analysis of Cotton Planting Parameters in Multiareas Based on Multisensor

Abstract

In order to realize cotton growth monitoring, a cotton planting monitoring system based on image processing technology was proposed. The system requires camera to collect cotton canopy images, leaf areometer to detect the leaf area index, and spectrometer to detect the normalized vegetation index ( $\text{NDVI}$ ) and the vegetation index ( $\text{RVI}$ ). Due to different types of data, based on the establishment of the output transmission system, the canopy coverage was calculated by the image processing method, and there was a linear relationship between canopy coverage $\text{NDVI}$ and $\text{RVI}$ . The leaf area index (LAI) of N content was established, and the model of dry matter accumulation and canopy coverage was exponential. The experiment result shows that the linear and exponential coefficients of the model $k$ and $b$ increased with the increase of the nitrogen application rate. The fitting determination coefficient remained high under different nitrogen application rates. The fitting coefficient R2 of the three models in the two test fields ranged from 0.83 to 0.923, which also met the needs of model evaluation. The system was used to detect the cotton field with good accuracy.