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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.