Classification of Diospyros Lotus Seeds Based on MLP Neural Network

Yue-hua LI, Qi-gao FENG, Zhi-song HOU

Abstract


In order to apply machine vision technology replacing human vision to identify the plant germplasm resources. This paper select 5 different types of Diospyros lotus seeds, 7 different appearance features and 6 color features were extracted by machine vision technology based on traditional identification method. One input, seven hidden layers and one output has been used for the multilayer perception (MLP) in our system. K-fold cross Validation was used for the modeling and classified of Diospyros lotus seeds. The results showed that the average identification rate of 5 types seeds was reached 91.8%, which indicated that the established seed model could be used as an effective method for the accurate classification of the seeds

Keywords


Neural network, Feature extraction, Seed classification


DOI
10.12783/dtcse/smce2017/12475

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