Abstract
The pricing of agricultural products in supermarket needs to rely on artificial memory.In order to realize intelligent recognition of agricultural products,an image recognition method of agricultural products based on the multi-instance learning was proposed.An improved Single Blob with Neighbors(SBN) method was adopted to organize bags and meanwhile extract features of an image.The target concept was learned by maximizing Diverse Density(DD) and applied to images recognition.Experiments were performed on both multi-class produce image dataset by self-collection and single-class agricultural product images selected from Amsterdam Library of Object Images(ALOI).The experimental results show that the method is able to recognize multi-class agricultural product images captured under various illumination conditions and interference environment,and the recognition rate can achieve 94.21 percent.Additionally,the method performs better than global method when recognizing single-class agricultural product images.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
