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3 December 2015 Detection method of vegetable maturity based on neural network and bayesian information fusions
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Proceedings Volume 9794, Sixth International Conference on Electronics and Information Engineering; 97942M (2015) https://doi.org/10.1117/12.2203472
Event: Sixth International Conference on Electronics and Information Engineering, 2015, Dalian, China
Abstract
In order to better grasp the maturity of vegetables, this paper proposes a method which makes full use of external morphological characteristics of vegetables to infer the maturity of vegetables. Especially extracting the morphological features of the root and combine them with the ground morphological features. In this paper, firstly, vegetable images are disposed by threshold segmentation and feature extraction using the image processing toolbox of Matlab. Through this way, the value of leaf crown projected area, plant height, root length and root side area will be got. Secondly, Features of ground part and underground part can be used as training samples for corresponding neural network maturity detection models. Ultimately, Bayesian theory is utilized to process information fusion with obtained values of each neural network. The results show that this method improved the accuracy of detection.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fan Liang, Hong-Dou Chen, Shi-Gang Cui, Li-Li Yang, and Xing-Li Wu "Detection method of vegetable maturity based on neural network and bayesian information fusions", Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 97942M (3 December 2015); https://doi.org/10.1117/12.2203472
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