The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

International Conference on Innovative Technology, Engineering and Sciences 2018

工业技术;自然科学

Taha, Z.^1 ; Razman, M.A.M.^1 ; Abdul Ghani, A.S.^1 ; Abdul Majeed, A.P.P.^1 ; Musa, R.M.^1 ; Adnan, F.A.^1 ; Sallehudin, M.F.^2 ; Mukai, Y.^2

Innovative Manufacturing Mechatronics and Sports Lab (IMAMS), Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan Campus, Pekan, Pahang

26600, Malaysia^1

Kulliyyah of Science, International Islamic University Malaysia, Jalan Sultan Ahmad Shah, Bandar Indera Mahkota, Pahang, Kuantan

25200, Malaysia^2

关键词: Fish behaviours;    Fish disease;    Hybrid datum;    Integration of images;    Integration techniques;    K nearest neighbours (k-NN);    K-nearest neighbours;    Reduction of oxygen;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/342/1/012017/pdf
DOI  :  10.1088/1757-899X/342/1/012017

来源: IOP

【 摘 要 】

Fish Hunger behaviour is essential in determining the fish feeding routine, particularly for fish farmers. The inability to provide accurate feeding routines (under-feeding or over-feeding) may lead the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the excessive food that is not consumed by the fish will be dissolved in the water and accordingly reduce the water quality through the reduction of oxygen quantity. This problem also leads the death of the fish or even spur fish diseases. In the present study, a correlation of Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique is established. The behaviour is clustered with respect to the position of the school size as well as the school density of the fish before feeding, during feeding and after feeding. The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. Three different variations of the algorithm namely cosine, cubic and weighted are assessed on its ability to classify the aforementioned fish hunger behaviour. It was found from the study that the weighted k-NN variation provides the best classification with an accuracy of 86.5%. Therefore, it could be concluded that the proposed integration technique may assist fish farmers in ascertaining fish feeding routine.