Hyperspectral Imaging Technology for Detection of Moisture Content of Tomato Leaves

摘要：

Hyperspectral imaging technology for detection of moisture content of crops takes into account both the internal information and external features. It improves the comprehensiveness and reliability of detection. A hyperspectral imaging system is developed to perform acquisition of hyperspectral imaging data. The adaptive band selection is adopted to select the optimal characteristic wavelength from lots of data, and the optimal wavelength is 1420nm. The images of all samples at 1420nm are segmented, reversed and operated, and then the target images are obtained. The mean value and standard deviation of gray scale are extracted as grey features, and the mean value and standard deviation of energy, entropy, geometrical moment of inertia, correlation as texture features. The optimal feature subset is selected by GA-PLSR, and then the partial least-squares regression model is established. The correlation coefficient between the predict value and the real value is 0.902. It is higher obviously than the prediction models based on grey features or texture features.

关键词： hyperspectral imaging genetic algorithm(GA) partial least squares regression(PLSR) content moisture detection

作者: Ying Zhou Hanping Mao Xiaodong Zhang

作者单位: Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province Jiangsu University Zhenjiang, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 172-176

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）