Random forests based classification for crops ripeness stage

Citation:
Esraa Elhariri, N. El-Bendary, A. E. Hassanien, A. Badr, Ahmed M. M. Hussein, and V. Snasel, "Random forests based classification for crops ripeness stage", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.

Date Presented:

22-24 June

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 of three 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, respectively were used for both training and
testing datasets. Training dataset is divided into five classes representing the different
stages of tomato and bell pepper ripeness. Experimental results showed
that SVM with Linear Kernel function achieved accuracy better than RF.

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