Automatic recognition of stored-grain pests based on extension decision theory
1. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education and Jiangsu Province, Jiangsu University Zhenjiang, Jiangsu 212013, China; 2. Institute of Electric Power, North China Institute of Water Conservancy and Hydroelectric Power Zhengzhou, Henan 450011, China）
Abstract：The ten morphological features of the stored-grain pests were normalized after selecting features. The standard and extensional matter-element matrixes were constructed based on the feature mean value and standard deviation. A quantitative method identifying the feature weight coefficients by fuzzy analysis was put forward. The correlative degrees between the stored-grain pests to be recognized and the nine species pests were calculated, such that the pests were classified according to the principle of the maximum correlative degree. The nine species of the stored-grain pests in grain-depot were automatically recognized by a classifier based on the extension decision theory, and the identification ratio was over 93%. The experiment showed that the recognition ratio can be improved by constructing standard and ex- tensional matter-element matrixes based on the feature mean value and standard deviation.
2008, Vol. 29 
