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Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching

Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching

Muthukumar Arunachalam, Meena Arunachalam
Copyright: © 2019 |Pages: 20
ISBN13: 9781522580270|ISBN10: 1522580271|ISBN13 Softcover: 9781522591399|EISBN13: 9781522580287
DOI: 10.4018/978-1-5225-8027-0.ch003
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MLA

Arunachalam, Muthukumar, and Meena Arunachalam. "Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching." Applications of Image Processing and Soft Computing Systems in Agriculture, edited by Navid Razmjooy and Vania Vieira Estrela, IGI Global, 2019, pp. 80-99. https://doi.org/10.4018/978-1-5225-8027-0.ch003

APA

Arunachalam, M. & Arunachalam, M. (2019). Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching. In N. Razmjooy & V. Estrela (Eds.), Applications of Image Processing and Soft Computing Systems in Agriculture (pp. 80-99). IGI Global. https://doi.org/10.4018/978-1-5225-8027-0.ch003

Chicago

Arunachalam, Muthukumar, and Meena Arunachalam. "Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching." In Applications of Image Processing and Soft Computing Systems in Agriculture, edited by Navid Razmjooy and Vania Vieira Estrela, 80-99. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8027-0.ch003

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Abstract

Identification of useful items that can be picked up from the damaged crops or batch of fruits/vegetables is a challenging task nowadays. Humans may fail to identify them correctly with their naked eyes due to strain. Image processing techniques can help to maximize the amount of the good agro-items easily by comparing the existing goods to templates. This chapter introduces an effective recognition method to spot good agro-items by extracting the local features using Gabor filter for orientation information. Another local information of that fruit/vegetable is extracted by speeded up robust features (SURF) algorithm. The extracted features are matched with their templates which results in the decision of individual feature extraction method. Finally, both local information is fused at decision level individually with AND operation (i.e., both algorithms will give correct decision to identify the good agro-item).

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