{"title":"CHESTNUT (CASTANEA SATIVA MILL.) GENOTYPE IDENTIFICATION: AN ARTIFICIAL NEURAL NETWORK APPROACH","authors":"S. Mancuso, F. Ferrini, F. Nicese","doi":"10.1080/14620316.1999.11511188","DOIUrl":null,"url":null,"abstract":"SummaryThe potential use of the artificial neural networks (ANNs) for characterization and identification of seventeen chestnut (Castanea sativa Mill.) accessions, belonging to the ``marrone''-type and ``chestnut''-type, was investigated in genotypes originating from regions of Italy. Different back-propagation neural networks (BPNN) were built on the basis of image analysis parameters of the leaves, for two tasks of chestnut classification. In the first case a BPNN was built and trained to differentiate the 17 accessions of chestnut. In the second case a BPNN was conceived to distinguish between the ``marrone'' and ``chestnut'' types. BPNN produced a clear identification of all the accessions except in the case of `Garrone nero', `Garrone rosso' and `Tempuriva', which showed almost the same output diagram. Cluster analysis separated the 17 chestnut genotypes into four main groups whose differences were related to the original sources of the genotypes and to the type of affiliation (``marrone''-type or ``...","PeriodicalId":54808,"journal":{"name":"Journal of Horticultural Science & Biotechnology","volume":"50 1","pages":"777-784"},"PeriodicalIF":1.7000,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/14620316.1999.11511188","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Horticultural Science & Biotechnology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1080/14620316.1999.11511188","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HORTICULTURE","Score":null,"Total":0}
引用次数: 17
Abstract
SummaryThe potential use of the artificial neural networks (ANNs) for characterization and identification of seventeen chestnut (Castanea sativa Mill.) accessions, belonging to the ``marrone''-type and ``chestnut''-type, was investigated in genotypes originating from regions of Italy. Different back-propagation neural networks (BPNN) were built on the basis of image analysis parameters of the leaves, for two tasks of chestnut classification. In the first case a BPNN was built and trained to differentiate the 17 accessions of chestnut. In the second case a BPNN was conceived to distinguish between the ``marrone'' and ``chestnut'' types. BPNN produced a clear identification of all the accessions except in the case of `Garrone nero', `Garrone rosso' and `Tempuriva', which showed almost the same output diagram. Cluster analysis separated the 17 chestnut genotypes into four main groups whose differences were related to the original sources of the genotypes and to the type of affiliation (``marrone''-type or ``...
期刊介绍:
The Journal of Horticultural Science and Biotechnology is an international, peer-reviewed journal, which publishes original research contributions into the production, improvement and utilisation of horticultural crops. It aims to provide scientific knowledge of interest to those engaged in scientific research and the practice of horticulture. The scope of the journal includes studies on fruit and other perennial crops, vegetables and ornamentals grown in temperate or tropical regions and their use in commercial, amenity or urban horticulture. Papers, including reviews, that give new insights into plant and crop growth, yield, quality and response to the environment, are welcome, including those arising from technological innovation and developments in crop genome sequencing and other biotechnological advances.