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Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques

Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques

Esraa El Hariri, Nashwa El-Bendary, Aboul Ella Hassanien, Amr Badr
Copyright: © 2014 |Pages: 30
ISBN13: 9781466660304|ISBN10: 1466660309|EISBN13: 9781466660311
DOI: 10.4018/978-1-4666-6030-4.ch006
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MLA

El Hariri, Esraa, et al. "Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques." Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, IGI Global, 2014, pp. 101-130. https://doi.org/10.4018/978-1-4666-6030-4.ch006

APA

El Hariri, E., El-Bendary, N., Hassanien, A. E., & Badr, A. (2014). Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques. In M. Sarfraz (Ed.), Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies (pp. 101-130). IGI Global. https://doi.org/10.4018/978-1-4666-6030-4.ch006

Chicago

El Hariri, Esraa, et al. "Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, 101-130. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6030-4.ch006

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Abstract

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production, since ripeness is the main quality indicator from the customers' perspective. To ensure optimum yield of high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based image classification approach to automate the ripeness assessment process of tomato via examining and classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent research work related to monitoring and classification of maturity stages for fruits/vegetables and provides the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of three phases, namely pre-processing, feature extraction, and classification phase. The classification process depends totally on color features (colored histogram and color moments), since the surface color of a tomato is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis (PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.

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