Random Forests Based Classification for Crops Ripeness Stages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86093038" target="_blank" >RIV/61989100:27240/14:86093038 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_21" target="_blank" >http://dx.doi.org/10.1007/978-3-319-08156-4_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_21" target="_blank" >10.1007/978-3-319-08156-4_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Random Forests Based Classification for Crops Ripeness Stages

Popis výsledku v původním jazyce

This article presents a classification approach based on random forests algorithm for estimating and classifying the different maturity/ripeness stages of two types of crops; namely tomato and bell pepper (sweet pepper). The proposed approach consists ofthree phases that are pre-processing, feature extraction, and classification phases. Surface color of tomato and bell pepper is the most important characteristic to observe ripeness. So, the proposed classification system uses color features for classifying ripeness stages. It implements principal components analysis (PCA) along with support vector machine (SVM) algorithms and random forests (RF) classifier for features extraction and classification of ripeness stages, respectively. The datasets used for experiments were constructed based on real sample images for both tomatoes and bell pepper at different stages, which were collected from farms in Minya city, Upper Egypt. Datasets of total 250 and 175 images for tomato and bell pepper

Název v anglickém jazyce

Random Forests Based Classification for Crops Ripeness Stages

Popis výsledku anglicky

This article presents a classification approach based on random forests algorithm for estimating and classifying the different maturity/ripeness stages of two types of crops; namely tomato and bell pepper (sweet pepper). The proposed approach consists ofthree phases that are pre-processing, feature extraction, and classification phases. Surface color of tomato and bell pepper is the most important characteristic to observe ripeness. So, the proposed classification system uses color features for classifying ripeness stages. It implements principal components analysis (PCA) along with support vector machine (SVM) algorithms and random forests (RF) classifier for features extraction and classification of ripeness stages, respectively. The datasets used for experiments were constructed based on real sample images for both tomatoes and bell pepper at different stages, which were collected from farms in Minya city, Upper Egypt. Datasets of total 250 and 175 images for tomato and bell pepper

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Soft Computing. Volume 303

ISBN

978-3-319-08155-7

ISSN

1615-3871

e-ISSN

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Počet stran výsledku

11

Strana od-do

205-215

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Ostrava

Datum konání akce

23. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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