Inspection of Lettuce Water Stress Based on Multi- sensor Information Fusion Technology

Abstract. Characteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce's water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models' universality. By the extraction of lettuce's multisensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4 characteristic wavelengths, characteristic model of multispectral image and CWSI were established. These multi-sensor characteristics were fused by using the BP artificial neural network. Based on the fused multisensor characteristics, the lettuce moisture evaluation model was established. The results showed that the correlation coefficient of multi-spectral images model, spectral characteristics model and information fusion model were in turn increased, the correlation coefficients were respectively 0.8042、0.8547 and 0.9337. It was feasible to diagnose lettuce water content by using multi-sensor information fusion of reflectance spectroscopy, multi-spectral images and canopy temperature .The correct rate and robustness of the discriminating model from multi-sensor information fusion were better than those of the model from the single-sensor information. Keywords: Lettuce, Water stress, Information fusion 1．Introduction Leaf stomata conductance, leaf water potential, transpiration rate, plant stem diameter changes and soil moisture, etc. indirectly illustrated the crop water stress and water requirement. Or the plant water stress is determined using dry weight method by collecting in vivo samples. The traditional testing methods have the shortages of low precision and affecting crop growth ,and it is not conducive to the promotion application, because in sampling and data analysis are time-consuming. Modern diagnostic methods are mainly spectroscopy, visual images and canopy temperature. However, canopy reflectance spectrum and images have interactions as nitrogen, water and leaf area index, spectrum and image are affected by the crop canopy structure, environmental factors and others. Therefore, a single detection method can not accurately and comprehensively explain the water stress. The discussion grou