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Identification of Agricultural Crop Residues Using Non-Destructive Methods

Identification of Agricultural Crop Residues Using Non-Destructive Methods

Dimitrios Kateris, Ioannis Gravalos, Theodoros Gialamas
Copyright: © 2019 |Pages: 31
ISBN13: 9781522580270|ISBN10: 1522580271|ISBN13 Softcover: 9781522591399|EISBN13: 9781522580287
DOI: 10.4018/978-1-5225-8027-0.ch005
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MLA

Kateris, Dimitrios, et al. "Identification of Agricultural Crop Residues Using Non-Destructive Methods." Applications of Image Processing and Soft Computing Systems in Agriculture, edited by Navid Razmjooy and Vania Vieira Estrela, IGI Global, 2019, pp. 114-144. https://doi.org/10.4018/978-1-5225-8027-0.ch005

APA

Kateris, D., Gravalos, I., & Gialamas, T. (2019). Identification of Agricultural Crop Residues Using Non-Destructive Methods. In N. Razmjooy & V. Estrela (Eds.), Applications of Image Processing and Soft Computing Systems in Agriculture (pp. 114-144). IGI Global. https://doi.org/10.4018/978-1-5225-8027-0.ch005

Chicago

Kateris, Dimitrios, Ioannis Gravalos, and Theodoros Gialamas. "Identification of Agricultural Crop Residues Using Non-Destructive Methods." In Applications of Image Processing and Soft Computing Systems in Agriculture, edited by Navid Razmjooy and Vania Vieira Estrela, 114-144. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8027-0.ch005

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Abstract

Biomass is a bulky and inhomogeneous material, making it difficult to transport and store. In order to solve this problem, it has been found that the most common way to overcome the limitation of the biomass bulk density is to increase it with fine shredding. This chapter investigated the ability to identify specific operation conditions in a prototype biomass shredder by developing and utilizing non-destructive testing and artificial intelligence techniques. In order to demonstrate the performance of proposed methods, three different case studies investigated the different operation conditions from the vibration signals acquired through the ball bearings of the biomass shredder. The results showed that the two classifiers can provide reliable results using as inputs statistical features in time and frequency domain. These statistical features can be used with success for identify different operating condition. The combination of the statistical features with the appropriate classifiers gives a powerful tool for the agricultural biomass shredder condition monitoring.

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