Evaluation of Mangrove Crab Classification System
Jasmin H. Almarinez1, Alexander Hernandez2

1Jasmin H. Almarinez, Technological Institute of the Philippines, Manila, Philippines.
2Alexander Hernandez, Technological Institute of the Philippines, Manila, Philippines.
Manuscript received on 26 March 2019 | Revised Manuscript received on 07 April 2019 | Manuscript Published on 18 April 2019 | PP: 830-834 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03610376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Mangrove crab has a growing demand in the Philippines and international market. However, the use of information technology to improve mangrove crab farming practices has been very limited. This study aims to develop mangrove crab classification using machine learning as well as use environmental sensors to monitoring temperature, water quality in real time basis. Images were analyzed using KNN. Results of this project show high recognition rates of the larval images in different stages with an average of 85% accuracy. Also, this project evaluates the software developed that monitors larval growth stages and classification having 4.68 overall weighted mean. Hence, the system is accurate and efficient in classification and growth stages monitoring activities.
Keywords: Object Analysis, Image Processing, KNN, Raspberry Pi.
Scope of the Article: Classification