Random Forests Based Classification for Crops Ripeness Stages

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Random Forests Based Classification for Crops Ripeness Stages
Original language description
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
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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