NDLI: Application of a hybrid neural network for the discrimination of seeds by artificial vision

Content Provider	IEEE Xplore Digital Library
Author	Chtioui, Y. Bertrand, D. Devaux, M.-F. Barba, D.
Copyright Year	1996
Description	Author affiliation: Lab. de Tech. Appliquee a la Nutrition, Inst. Nat. de la Recherche Agronomique, Nantes, France (Chtioui, Y.; Bertrand, D.; Devaux, M.-F.)
Abstract	Intelligent hybrid systems are playing an increasing role in the development of artificial intelligence. In this study, we applied simulated annealing to adjust the weights of a multilayer neural network (MNN). Two versions of simulated annealing were tested: conventional simulated annealing (CSA) and fast simulated annealing (FSA). The applied hybrid system was used as a classifier in order to discriminate between 3 seed species (1 cultivated seed species which is perennial rye grass, and 2 adventitious seed species which are rumex and wild oat). From a set of colour digital images, 73 morphometrical and textural features were extracted to characterise each individual seed. Stepwise discriminant analysis made it possible to select the first 3 relevant features. The performances of classification were highly dependent on the scaling parameters of simulated annealing. For example, when the number of iterations of simulated annealing was 5, and the number of temperatures was 40, the combination between CSA and MNN correctly classified 98.18 and 97.77 percent of the training and the test sets, whereas FSA and MNN identified 99.18 and 99.68 percent of the same data sets. Globally, FSA outperformed CSA both in reliability and computational resources. A hybrid system combined with a colour image analysis showed promise for the design of an automatic seed identification device.
Starting Page	484
Ending Page	489
File Size	627573
Page Count	6
File Format	PDF
ISBN	0818676867
ISSN	10823409
DOI	10.1109/TAI.1996.560797
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-11-16
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Simulated annealing Multi-layer neural network Artificial intelligence Testing Image color analysis Hybrid intelligent systems Neural networks Digital images Feature extraction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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